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Sample records for single neuron models

  1. Stochastic models for spike trains of single neurons

    CERN Document Server

    Sampath, G

    1977-01-01

    1 Some basic neurophysiology 4 The neuron 1. 1 4 1. 1. 1 The axon 7 1. 1. 2 The synapse 9 12 1. 1. 3 The soma 1. 1. 4 The dendrites 13 13 1. 2 Types of neurons 2 Signals in the nervous system 14 2. 1 Action potentials as point events - point processes in the nervous system 15 18 2. 2 Spontaneous activi~ in neurons 3 Stochastic modelling of single neuron spike trains 19 3. 1 Characteristics of a neuron spike train 19 3. 2 The mathematical neuron 23 4 Superposition models 26 4. 1 superposition of renewal processes 26 4. 2 Superposition of stationary point processe- limiting behaviour 34 4. 2. 1 Palm functions 35 4. 2. 2 Asymptotic behaviour of n stationary point processes superposed 36 4. 3 Superposition models of neuron spike trains 37 4. 3. 1 Model 4. 1 39 4. 3. 2 Model 4. 2 - A superposition model with 40 two input channels 40 4. 3. 3 Model 4. 3 4. 4 Discussion 41 43 5 Deletion models 5. 1 Deletion models with 1nd~endent interaction of excitatory and inhibitory sequences 44 VI 5. 1. 1 Model 5. 1 The basic de...

  2. Single neuron computation

    CERN Document Server

    McKenna, Thomas M; Zornetzer, Steven F

    1992-01-01

    This book contains twenty-two original contributions that provide a comprehensive overview of computational approaches to understanding a single neuron structure. The focus on cellular-level processes is twofold. From a computational neuroscience perspective, a thorough understanding of the information processing performed by single neurons leads to an understanding of circuit- and systems-level activity. From the standpoint of artificial neural networks (ANNs), a single real neuron is as complex an operational unit as an entire ANN, and formalizing the complex computations performed by real n

  3. Single-cell axotomy of cultured hippocampal neurons integrated in neuronal circuits.

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    Gomis-Rüth, Susana; Stiess, Michael; Wierenga, Corette J; Meyn, Liane; Bradke, Frank

    2014-05-01

    An understanding of the molecular mechanisms of axon regeneration after injury is key for the development of potential therapies. Single-cell axotomy of dissociated neurons enables the study of the intrinsic regenerative capacities of injured axons. This protocol describes how to perform single-cell axotomy on dissociated hippocampal neurons containing synapses. Furthermore, to axotomize hippocampal neurons integrated in neuronal circuits, we describe how to set up coculture with a few fluorescently labeled neurons. This approach allows axotomy of single cells in a complex neuronal network and the observation of morphological and molecular changes during axon regeneration. Thus, single-cell axotomy of mature neurons is a valuable tool for gaining insights into cell intrinsic axon regeneration and the plasticity of neuronal polarity of mature neurons. Dissociation of the hippocampus and plating of hippocampal neurons takes ∼2 h. Neurons are then left to grow for 2 weeks, during which time they integrate into neuronal circuits. Subsequent axotomy takes 10 min per neuron and further imaging takes 10 min per neuron.

  4. Discrimination of communication vocalizations by single neurons and groups of neurons in the auditory midbrain.

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    Schneider, David M; Woolley, Sarah M N

    2010-06-01

    Many social animals including songbirds use communication vocalizations for individual recognition. The perception of vocalizations depends on the encoding of complex sounds by neurons in the ascending auditory system, each of which is tuned to a particular subset of acoustic features. Here, we examined how well the responses of single auditory neurons could be used to discriminate among bird songs and we compared discriminability to spectrotemporal tuning. We then used biologically realistic models of pooled neural responses to test whether the responses of groups of neurons discriminated among songs better than the responses of single neurons and whether discrimination by groups of neurons was related to spectrotemporal tuning and trial-to-trial response variability. The responses of single auditory midbrain neurons could be used to discriminate among vocalizations with a wide range of abilities, ranging from chance to 100%. The ability to discriminate among songs using single neuron responses was not correlated with spectrotemporal tuning. Pooling the responses of pairs of neurons generally led to better discrimination than the average of the two inputs and the most discriminating input. Pooling the responses of three to five single neurons continued to improve neural discrimination. The increase in discriminability was largest for groups of neurons with similar spectrotemporal tuning. Further, we found that groups of neurons with correlated spike trains achieved the largest gains in discriminability. We simulated neurons with varying levels of temporal precision and measured the discriminability of responses from single simulated neurons and groups of simulated neurons. Simulated neurons with biologically observed levels of temporal precision benefited more from pooling correlated inputs than did neurons with highly precise or imprecise spike trains. These findings suggest that pooling correlated neural responses with the levels of precision observed in the

  5. Coexisting chaotic attractors in a single neuron model with adapting feedback synapse

    International Nuclear Information System (INIS)

    Li Chunguang; Chen Guanrong

    2005-01-01

    In this paper, we consider the nonlinear dynamical behavior of a single neuron model with adapting feedback synapse, and show that chaotic behaviors exist in this model. In some parameter domain, we observe two coexisting chaotic attractors, switching from the coexisting chaotic attractors to a connected chaotic attractor, and then switching back to the two coexisting chaotic attractors. We confirm the chaoticity by simulations with phase plots, waveform plots, and power spectra

  6. A study of single multiplicative neuron model with nonlinear filters for hourly wind speed prediction

    International Nuclear Information System (INIS)

    Wu, Xuedong; Zhu, Zhiyu; Su, Xunliang; Fan, Shaosheng; Du, Zhaoping; Chang, Yanchao; Zeng, Qingjun

    2015-01-01

    Wind speed prediction is one important methods to guarantee the wind energy integrated into the whole power system smoothly. However, wind power has a non–schedulable nature due to the strong stochastic nature and dynamic uncertainty nature of wind speed. Therefore, wind speed prediction is an indispensable requirement for power system operators. Two new approaches for hourly wind speed prediction are developed in this study by integrating the single multiplicative neuron model and the iterated nonlinear filters for updating the wind speed sequence accurately. In the presented methods, a nonlinear state–space model is first formed based on the single multiplicative neuron model and then the iterated nonlinear filters are employed to perform dynamic state estimation on wind speed sequence with stochastic uncertainty. The suggested approaches are demonstrated using three cases wind speed data and are compared with autoregressive moving average, artificial neural network, kernel ridge regression based residual active learning and single multiplicative neuron model methods. Three types of prediction errors, mean absolute error improvement ratio and running time are employed for different models’ performance comparison. Comparison results from Tables 1–3 indicate that the presented strategies have much better performance for hourly wind speed prediction than other technologies. - Highlights: • Developed two novel hybrid modeling methods for hourly wind speed prediction. • Uncertainty and fluctuations of wind speed can be better explained by novel methods. • Proposed strategies have online adaptive learning ability. • Proposed approaches have shown better performance compared with existed approaches. • Comparison and analysis of two proposed novel models for three cases are provided

  7. Auditory-nerve single-neuron thresholds to electrical stimulation from scala tympani electrodes.

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    Parkins, C W; Colombo, J

    1987-12-31

    Single auditory-nerve neuron thresholds were studied in sensory-deafened squirrel monkeys to determine the effects of electrical stimulus shape and frequency on single-neuron thresholds. Frequency was separated into its components, pulse width and pulse rate, which were analyzed separately. Square and sinusoidal pulse shapes were compared. There were no or questionably significant threshold differences in charge per phase between sinusoidal and square pulses of the same pulse width. There was a small (less than 0.5 dB) but significant threshold advantage for 200 microseconds/phase pulses delivered at low pulse rates (156 pps) compared to higher pulse rates (625 pps and 2500 pps). Pulse width was demonstrated to be the prime determinant of single-neuron threshold, resulting in strength-duration curves similar to other mammalian myelinated neurons, but with longer chronaxies. The most efficient electrical stimulus pulse width to use for cochlear implant stimulation was determined to be 100 microseconds/phase. This pulse width delivers the lowest charge/phase at threshold. The single-neuron strength-duration curves were compared to strength-duration curves of a computer model based on the specific anatomy of auditory-nerve neurons. The membrane capacitance and resulting chronaxie of the model can be varied by altering the length of the unmyelinated termination of the neuron, representing the unmyelinated portion of the neuron between the habenula perforata and the hair cell. This unmyelinated segment of the auditory-nerve neuron may be subject to aminoglycoside damage. Simulating a 10 micron unmyelinated termination for this model neuron produces a strength-duration curve that closely fits the single-neuron data obtained from aminoglycoside deafened animals. Both the model and the single-neuron strength-duration curves differ significantly from behavioral threshold data obtained from monkeys and humans with cochlear implants. This discrepancy can best be explained by

  8. Reconstruction of neuronal input through modeling single-neuron dynamics and computations

    International Nuclear Information System (INIS)

    Qin, Qing; Wang, Jiang; Yu, Haitao; Deng, Bin; Chan, Wai-lok

    2016-01-01

    Mathematical models provide a mathematical description of neuron activity, which can better understand and quantify neural computations and corresponding biophysical mechanisms evoked by stimulus. In this paper, based on the output spike train evoked by the acupuncture mechanical stimulus, we present two different levels of models to describe the input-output system to achieve the reconstruction of neuronal input. The reconstruction process is divided into two steps: First, considering the neuronal spiking event as a Gamma stochastic process. The scale parameter and the shape parameter of Gamma process are, respectively, defined as two spiking characteristics, which are estimated by a state-space method. Then, leaky integrate-and-fire (LIF) model is used to mimic the response system and the estimated spiking characteristics are transformed into two temporal input parameters of LIF model, through two conversion formulas. We test this reconstruction method by three different groups of simulation data. All three groups of estimates reconstruct input parameters with fairly high accuracy. We then use this reconstruction method to estimate the non-measurable acupuncture input parameters. Results show that under three different frequencies of acupuncture stimulus conditions, estimated input parameters have an obvious difference. The higher the frequency of the acupuncture stimulus is, the higher the accuracy of reconstruction is.

  9. Reconstruction of neuronal input through modeling single-neuron dynamics and computations

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    Qin, Qing; Wang, Jiang; Yu, Haitao; Deng, Bin, E-mail: dengbin@tju.edu.cn; Chan, Wai-lok [School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072 (China)

    2016-06-15

    Mathematical models provide a mathematical description of neuron activity, which can better understand and quantify neural computations and corresponding biophysical mechanisms evoked by stimulus. In this paper, based on the output spike train evoked by the acupuncture mechanical stimulus, we present two different levels of models to describe the input-output system to achieve the reconstruction of neuronal input. The reconstruction process is divided into two steps: First, considering the neuronal spiking event as a Gamma stochastic process. The scale parameter and the shape parameter of Gamma process are, respectively, defined as two spiking characteristics, which are estimated by a state-space method. Then, leaky integrate-and-fire (LIF) model is used to mimic the response system and the estimated spiking characteristics are transformed into two temporal input parameters of LIF model, through two conversion formulas. We test this reconstruction method by three different groups of simulation data. All three groups of estimates reconstruct input parameters with fairly high accuracy. We then use this reconstruction method to estimate the non-measurable acupuncture input parameters. Results show that under three different frequencies of acupuncture stimulus conditions, estimated input parameters have an obvious difference. The higher the frequency of the acupuncture stimulus is, the higher the accuracy of reconstruction is.

  10. Spiking neuron devices consisting of single-flux-quantum circuits

    International Nuclear Information System (INIS)

    Hirose, Tetsuya; Asai, Tetsuya; Amemiya, Yoshihito

    2006-01-01

    Single-flux-quantum (SFQ) circuits can be used for making spiking neuron devices, which are useful elements for constructing intelligent, brain-like computers. The device we propose is based on the leaky integrate-and-fire neuron (IFN) model and uses a SFQ pulse as an action signal or a spike of neurons. The operation of the neuron device is confirmed by computer simulator. It can operate with a short delay of 100 ps or less and is the highest-speed neuron device ever reported

  11. Results on a Binding Neuron Model and Their Implications for Modified Hourglass Model for Neuronal Network

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    Viswanathan Arunachalam

    2013-01-01

    Full Text Available The classical models of single neuron like Hodgkin-Huxley point neuron or leaky integrate and fire neuron assume the influence of postsynaptic potentials to last till the neuron fires. Vidybida (2008 in a refreshing departure has proposed models for binding neurons in which the trace of an input is remembered only for a finite fixed period of time after which it is forgotten. The binding neurons conform to the behaviour of real neurons and are applicable in constructing fast recurrent networks for computer modeling. This paper develops explicitly several useful results for a binding neuron like the firing time distribution and other statistical characteristics. We also discuss the applicability of the developed results in constructing a modified hourglass network model in which there are interconnected neurons with excitatory as well as inhibitory inputs. Limited simulation results of the hourglass network are presented.

  12. Existence of multiple receptors in single neurons: responses of single bullfrog olfactory neurons to many cAMP-dependent and independent odorants.

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    Kashiwayanagi, M; Shimano, K; Kurihara, K

    1996-11-04

    The responses of single bullfrog olfactory neurons to various odorants were measured with the whole-cell patch clamp which offers direct information on cellular events and with the ciliary recording technique to obtain stable quantitative data from many neurons. A large portion of single olfactory neurons (about 64% and 79% in the whole-cell recording and in the ciliary recording, respectively) responded to many odorants with quite diverse molecular structures, including both odorants previously indicated to be cAMP-dependent (increasing) and independent odorants. One odorant elicited a response in many cells; e.g. hedione and citralva elicited the response in 100% and 92% of total neurons examined with the ciliary recording technique. To confirm that a single neuron carries different receptors or transduction pathways, the cross-adaptation technique was applied to single neurons. Application of hedione to a single neuron after desensitization of the current in response to lyral or citralva induced an inward current with a similar magnitude to that applied alone. It was suggested that most single olfactory neurons carry multiple receptors and at least dual transduction pathways.

  13. The role of dendritic non-linearities in single neuron computation

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    Boris Gutkin

    2014-05-01

    Full Text Available Experiment has demonstrated that summation of excitatory post-synaptic protientials (EPSPs in dendrites is non-linear. The sum of multiple EPSPs can be larger than their arithmetic sum, a superlinear summation due to the opening of voltage-gated channels and similar to somatic spiking. The so-called dendritic spike. The sum of multiple of EPSPs can also be smaller than their arithmetic sum, because the synaptic current necessarily saturates at some point. While these observations are well-explained by biophysical models the impact of dendritic spikes on computation remains a matter of debate. One reason is that dendritic spikes may fail to make the neuron spike; similarly, dendritic saturations are sometime presented as a glitch which should be corrected by dendritic spikes. We will provide solid arguments against this claim and show that dendritic saturations as well as dendritic spikes enhance single neuron computation, even when they cannot directly make the neuron fire. To explore the computational impact of dendritic spikes and saturations, we are using a binary neuron model in conjunction with Boolean algebra. We demonstrate using these tools that a single dendritic non-linearity, either spiking or saturating, combined with somatic non-linearity, enables a neuron to compute linearly non-separable Boolean functions (lnBfs. These functions are impossible to compute when summation is linear and the exclusive OR is a famous example of lnBfs. Importantly, the implementation of these functions does not require the dendritic non-linearity to make the neuron spike. Next, We show that reduced and realistic biophysical models of the neuron are capable of computing lnBfs. Within these models and contrary to the binary model, the dendritic and somatic non-linearity are tightly coupled. Yet we show that these neuron models are capable of linearly non-separable computations.

  14. Neural Plasticity: Single Neuron Models for Discrimination and Generalization and AN Experimental Ensemble Approach.

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    Munro, Paul Wesley

    A special form for modification of neuronal response properties is described in which the change in the synaptic state vector is parallel to the vector of afferent activity. This process is termed "parallel modification" and its theoretical and experimental implications are examined. A theoretical framework has been devised to describe the complementary functions of generalization and discrimination by single neurons. This constitutes a basis for three models each describing processes for the development of maximum selectivity (discrimination) and minimum selectivity (generalization) by neurons. Strengthening and weakening of synapses is expressed as a product of the presynaptic activity and a nonlinear modulatory function of two postsynaptic variables--namely a measure of the spatially integrated activity of the cell and a temporal integration (time-average) of that activity. Some theorems are given for low-dimensional systems and computer simulation results from more complex systems are discussed. Model neurons that achieve high selectivity mimic the development of cat visual cortex neurons in a wide variety of rearing conditions. A role for low-selectivity neurons is proposed in which they provide inhibitory input to neurons of the opposite type, thereby suppressing the common component of a pattern class and enhancing their selective properties. Such contrast-enhancing circuits are analyzed and supported by computer simulation. To enable maximum selectivity, the net inhibition to a cell must become strong enough to offset whatever excitation is produced by the non-preferred patterns. Ramifications of parallel models for certain experimental paradigms are analyzed. A methodology is outlined for testing synaptic modification hypotheses in the laboratory. A plastic projection from one neuronal population to another will attain stable equilibrium under periodic electrical stimulation of constant intensity. The perturbative effect of shifting this intensity level

  15. Understanding metal homeostasis in primary cultured neurons. Studies using single neuron subcellular and quantitative metallomics.

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    Colvin, Robert A; Lai, Barry; Holmes, William R; Lee, Daewoo

    2015-07-01

    The purpose of this study was to demonstrate how single cell quantitative and subcellular metallomics inform us about both the spatial distribution and cellular mechanisms of metal buffering and homeostasis in primary cultured neurons from embryonic rat brain, which are often used as models of human disease involving metal dyshomeostasis. The present studies utilized synchrotron radiation X-ray fluorescence (SRXRF) and focused primarily on zinc and iron, two abundant metals in neurons that have been implicated in the pathogenesis of neurodegenerative diseases such as Alzheimer's disease and Parkinson's disease. Total single cell contents for calcium, iron, zinc, copper, manganese, and nickel were determined. Resting steady state zinc showed a diffuse distribution in both soma and processes, best defined by the mass profile of the neuron with an enrichment in the nucleus compared with the cytoplasm. Zinc buffering and homeostasis was studied using two modes of cellular zinc loading - transporter and ionophore (pyrithione) mediated. Single neuron zinc contents were shown to statistically significantly increase by either loading method - ionophore: 160 million to 7 billion; transporter 160 million to 280 million atoms per neuronal soma. The newly acquired and buffered zinc still showed a diffuse distribution. Soma and processes have about equal abilities to take up zinc via transporter mediated pathways. Copper levels are distributed diffusely as well, but are relatively higher in the processes relative to zinc levels. Prior studies have observed iron puncta in certain cell types, but others have not. In the present study, iron puncta were characterized in several primary neuronal types. The results show that iron puncta could be found in all neuronal types studied and can account for up to 50% of the total steady state content of iron in neuronal soma. Although other metals can be present in iron puncta, they are predominantly iron containing and do not appear to be

  16. A single-neuron tracing study of arkypallidal and prototypic neurons in healthy rats.

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    Fujiyama, Fumino; Nakano, Takashi; Matsuda, Wakoto; Furuta, Takahiro; Udagawa, Jun; Kaneko, Takeshi

    2016-12-01

    The external globus pallidus (GP) is known as a relay nucleus of the indirect pathway of the basal ganglia. Recent studies in dopamine-depleted and healthy rats indicate that the GP comprises two main types of pallidofugal neurons: the so-called "prototypic" and "arkypallidal" neurons. However, the reconstruction of complete arkypallidal neurons in healthy rats has not been reported. Here we visualized the entire axonal arborization of four single arkypallidal neurons and six single prototypic neurons in rat brain using labeling with a viral vector expressing membrane-targeted green fluorescent protein and examined the distribution of axon boutons in the target nuclei. Results revealed that not only the arkypallidal neurons but nearly all of the prototypic neurons projected to the striatum with numerous axon varicosities. Thus, the striatum is a major target nucleus for pallidal neurons. Arkypallidal and prototypic GP neurons located in the calbindin-positive and calbindin-negative regions mainly projected to the corresponding positive and negative regions in the striatum. Because the GP and striatum calbindin staining patterns reflect the topographic organization of the striatopallidal projection, the striatal neurons in the sensorimotor and associative regions constitute the reciprocal connection with the GP neurons in the corresponding regions.

  17. Energy Model of Neuron Activation.

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    Romanyshyn, Yuriy; Smerdov, Andriy; Petrytska, Svitlana

    2017-02-01

    On the basis of the neurophysiological strength-duration (amplitude-duration) curve of neuron activation (which relates the threshold amplitude of a rectangular current pulse of neuron activation to the pulse duration), as well as with the use of activation energy constraint (the threshold curve corresponds to the energy threshold of neuron activation by a rectangular current pulse), an energy model of neuron activation by a single current pulse has been constructed. The constructed model of activation, which determines its spectral properties, is a bandpass filter. Under the condition of minimum-phase feature of the neuron activation model, on the basis of Hilbert transform, the possibilities of phase-frequency response calculation from its amplitude-frequency response have been considered. Approximation to the amplitude-frequency response by the response of the Butterworth filter of the first order, as well as obtaining the pulse response corresponding to this approximation, give us the possibility of analyzing the efficiency of activating current pulses of various shapes, including analysis in accordance with the energy constraint.

  18. Large-scale modelling of neuronal systems

    International Nuclear Information System (INIS)

    Castellani, G.; Verondini, E.; Giampieri, E.; Bersani, F.; Remondini, D.; Milanesi, L.; Zironi, I.

    2009-01-01

    The brain is, without any doubt, the most, complex system of the human body. Its complexity is also due to the extremely high number of neurons, as well as the huge number of synapses connecting them. Each neuron is capable to perform complex tasks, like learning and memorizing a large class of patterns. The simulation of large neuronal systems is challenging for both technological and computational reasons, and can open new perspectives for the comprehension of brain functioning. A well-known and widely accepted model of bidirectional synaptic plasticity, the BCM model, is stated by a differential equation approach based on bistability and selectivity properties. We have modified the BCM model extending it from a single-neuron to a whole-network model. This new model is capable to generate interesting network topologies starting from a small number of local parameters, describing the interaction between incoming and outgoing links from each neuron. We have characterized this model in terms of complex network theory, showing how this, learning rule can be a support For network generation.

  19. Creation of defined single cell resolution neuronal circuits on microelectrode arrays

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    Pirlo, Russell Kirk

    2009-12-01

    The way cell-cell organization of neuronal networks influences activity and facilitates function is not well understood. Microelectrode arrays (MEAs) and advancing cell patterning technologies have enabled access to and control of in vitro neuronal networks spawning much new research in neuroscience and neuroengineering. We propose that small, simple networks of neurons with defined circuitry may serve as valuable research models where every connection can be analyzed, controlled and manipulated. Towards the goal of creating such neuronal networks we have applied microfabricated elastomeric membranes, surface modification and our unique laser cell patterning system to create defined neuronal circuits with single-cell precision on MEAs. Definition of synaptic connectivity was imposed by the 3D physical constraints of polydimethylsiloxane elastomeric membranes. The membranes had 20mum clear-through holes and 2-3mum deep channels which when applied to the surface of the MEA formed microwells to confine neurons to electrodes connected via shallow tunnels to direct neurite outgrowth. Tapering and turning of channels was used to influence neurite polarity. Biocompatibility of the membranes was increased by vacuum baking, oligomer extraction, and autoclaving. Membranes were bound to the MEA by oxygen plasma treatment and heated pressure. The MEA/membrane surface was treated with oxygen plasma, poly-D-lysine and laminin to improve neuron attachment, survival and neurite outgrowth. Prior to cell patterning the outer edge of culture area was seeded with 5x10 5 cells per cm and incubated for 2 days. Single embryonic day 7 chick forebrain neurons were then patterned into the microwells and onto the electrodes using our laser cell patterning system. Patterned neurons successfully attached to and were confined to the electrodes. Neurites extended through the interconnecting channels and connected with adjacent neurons. These results demonstrate that neuronal circuits can be

  20. Current Source Density Estimation for Single Neurons

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    Dorottya Cserpán

    2014-03-01

    Full Text Available Recent developments of multielectrode technology made it possible to measure the extracellular potential generated in the neural tissue with spatial precision on the order of tens of micrometers and on submillisecond time scale. Combining such measurements with imaging of single neurons within the studied tissue opens up new experimental possibilities for estimating distribution of current sources along a dendritic tree. In this work we show that if we are able to relate part of the recording of extracellular potential to a specific cell of known morphology we can estimate the spatiotemporal distribution of transmembrane currents along it. We present here an extension of the kernel CSD method (Potworowski et al., 2012 applicable in such case. We test it on several model neurons of progressively complicated morphologies from ball-and-stick to realistic, up to analysis of simulated neuron activity embedded in a substantial working network (Traub et al, 2005. We discuss the caveats and possibilities of this new approach.

  1. Bayesian nonparametric modeling for comparison of single-neuron firing intensities.

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    Kottas, Athanasios; Behseta, Sam

    2010-03-01

    We propose a fully inferential model-based approach to the problem of comparing the firing patterns of a neuron recorded under two distinct experimental conditions. The methodology is based on nonhomogeneous Poisson process models for the firing times of each condition with flexible nonparametric mixture prior models for the corresponding intensity functions. We demonstrate posterior inferences from a global analysis, which may be used to compare the two conditions over the entire experimental time window, as well as from a pointwise analysis at selected time points to detect local deviations of firing patterns from one condition to another. We apply our method on two neurons recorded from the primary motor cortex area of a monkey's brain while performing a sequence of reaching tasks.

  2. A self-organizing state-space-model approach for parameter estimation in hodgkin-huxley-type models of single neurons.

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    Dimitrios V Vavoulis

    Full Text Available Traditional approaches to the problem of parameter estimation in biophysical models of neurons and neural networks usually adopt a global search algorithm (for example, an evolutionary algorithm, often in combination with a local search method (such as gradient descent in order to minimize the value of a cost function, which measures the discrepancy between various features of the available experimental data and model output. In this study, we approach the problem of parameter estimation in conductance-based models of single neurons from a different perspective. By adopting a hidden-dynamical-systems formalism, we expressed parameter estimation as an inference problem in these systems, which can then be tackled using a range of well-established statistical inference methods. The particular method we used was Kitagawa's self-organizing state-space model, which was applied on a number of Hodgkin-Huxley-type models using simulated or actual electrophysiological data. We showed that the algorithm can be used to estimate a large number of parameters, including maximal conductances, reversal potentials, kinetics of ionic currents, measurement and intrinsic noise, based on low-dimensional experimental data and sufficiently informative priors in the form of pre-defined constraints imposed on model parameters. The algorithm remained operational even when very noisy experimental data were used. Importantly, by combining the self-organizing state-space model with an adaptive sampling algorithm akin to the Covariance Matrix Adaptation Evolution Strategy, we achieved a significant reduction in the variance of parameter estimates. The algorithm did not require the explicit formulation of a cost function and it was straightforward to apply on compartmental models and multiple data sets. Overall, the proposed methodology is particularly suitable for resolving high-dimensional inference problems based on noisy electrophysiological data and, therefore, a

  3. A hidden Markov model approach to neuron firing patterns.

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    Camproux, A C; Saunier, F; Chouvet, G; Thalabard, J C; Thomas, G

    1996-11-01

    Analysis and characterization of neuronal discharge patterns are of interest to neurophysiologists and neuropharmacologists. In this paper we present a hidden Markov model approach to modeling single neuron electrical activity. Basically the model assumes that each interspike interval corresponds to one of several possible states of the neuron. Fitting the model to experimental series of interspike intervals by maximum likelihood allows estimation of the number of possible underlying neuron states, the probability density functions of interspike intervals corresponding to each state, and the transition probabilities between states. We present an application to the analysis of recordings of a locus coeruleus neuron under three pharmacological conditions. The model distinguishes two states during halothane anesthesia and during recovery from halothane anesthesia, and four states after administration of clonidine. The transition probabilities yield additional insights into the mechanisms of neuron firing.

  4. Cochlear spike synchronization and neuron coincidence detection model

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    Bader, Rolf

    2018-02-01

    Coincidence detection of a spike pattern fed from the cochlea into a single neuron is investigated using a physical Finite-Difference model of the cochlea and a physiologically motivated neuron model. Previous studies have shown experimental evidence of increased spike synchronization in the nucleus cochlearis and the trapezoid body [Joris et al., J. Neurophysiol. 71(3), 1022-1036 and 1037-1051 (1994)] and models show tone partial phase synchronization at the transition from mechanical waves on the basilar membrane into spike patterns [Ch. F. Babbs, J. Biophys. 2011, 435135]. Still the traveling speed of waves on the basilar membrane cause a frequency-dependent time delay of simultaneously incoming sound wavefronts up to 10 ms. The present model shows nearly perfect synchronization of multiple spike inputs as neuron outputs with interspike intervals (ISI) at the periodicity of the incoming sound for frequencies from about 30 to 300 Hz for two different amounts of afferent nerve fiber neuron inputs. Coincidence detection serves here as a fusion of multiple inputs into one single event enhancing pitch periodicity detection for low frequencies, impulse detection, or increased sound or speech intelligibility due to dereverberation.

  5. A COMPUTATIONAL MODEL OF MOTOR NEURON DEGENERATION

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    Le Masson, Gwendal; Przedborski, Serge; Abbott, L.F.

    2014-01-01

    SUMMARY To explore the link between bioenergetics and motor neuron degeneration, we used a computational model in which detailed morphology and ion conductance are paired with intracellular ATP production and consumption. We found that reduced ATP availability increases the metabolic cost of a single action potential and disrupts K+/Na+ homeostasis, resulting in a chronic depolarization. The magnitude of the ATP shortage at which this ionic instability occurs depends on the morphology and intrinsic conductance characteristic of the neuron. If ATP shortage is confined to the distal part of the axon, the ensuing local ionic instability eventually spreads to the whole neuron and involves fasciculation-like spiking events. A shortage of ATP also causes a rise in intracellular calcium. Our modeling work supports the notion that mitochondrial dysfunction can account for salient features of the paralytic disorder amyotrophic lateral sclerosis, including motor neuron hyperexcitability, fasciculation, and differential vulnerability of motor neuron subpopulations. PMID:25088365

  6. A computational model of motor neuron degeneration.

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    Le Masson, Gwendal; Przedborski, Serge; Abbott, L F

    2014-08-20

    To explore the link between bioenergetics and motor neuron degeneration, we used a computational model in which detailed morphology and ion conductance are paired with intracellular ATP production and consumption. We found that reduced ATP availability increases the metabolic cost of a single action potential and disrupts K+/Na+ homeostasis, resulting in a chronic depolarization. The magnitude of the ATP shortage at which this ionic instability occurs depends on the morphology and intrinsic conductance characteristic of the neuron. If ATP shortage is confined to the distal part of the axon, the ensuing local ionic instability eventually spreads to the whole neuron and involves fasciculation-like spiking events. A shortage of ATP also causes a rise in intracellular calcium. Our modeling work supports the notion that mitochondrial dysfunction can account for salient features of the paralytic disorder amyotrophic lateral sclerosis, including motor neuron hyperexcitability, fasciculation, and differential vulnerability of motor neuron subpopulations. Copyright © 2014 Elsevier Inc. All rights reserved.

  7. Automatic fitting of spiking neuron models to electrophysiological recordings

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    Cyrille Rossant

    2010-03-01

    Full Text Available Spiking models can accurately predict the spike trains produced by cortical neurons in response to somatically injected currents. Since the specific characteristics of the model depend on the neuron, a computational method is required to fit models to electrophysiological recordings. The fitting procedure can be very time consuming both in terms of computer simulations and in terms of code writing. We present algorithms to fit spiking models to electrophysiological data (time-varying input and spike trains that can run in parallel on graphics processing units (GPUs. The model fitting library is interfaced with Brian, a neural network simulator in Python. If a GPU is present it uses just-in-time compilation to translate model equations into optimized code. Arbitrary models can then be defined at script level and run on the graphics card. This tool can be used to obtain empirically validated spiking models of neurons in various systems. We demonstrate its use on public data from the INCF Quantitative Single-Neuron Modeling 2009 competition by comparing the performance of a number of neuron spiking models.

  8. Single neuron dynamics during experimentally induced anoxic depolarization

    NARCIS (Netherlands)

    Zandt, B.; Stigen, Tyler; ten Haken, Bernard; Netoff, Theoden; van Putten, Michel Johannes Antonius Maria

    2013-01-01

    We studied single neuron dynamics during anoxic depolarizations, which are often observed in cases of neuronal energy depletion. Anoxic and similar depolarizations play an important role in several pathologies, notably stroke, migraine, and epilepsy. One of the effects of energy depletion was

  9. Human temporal cortical single neuron activity during working memory maintenance.

    Science.gov (United States)

    Zamora, Leona; Corina, David; Ojemann, George

    2016-06-01

    The Working Memory model of human memory, first introduced by Baddeley and Hitch (1974), has been one of the most influential psychological constructs in cognitive psychology and human neuroscience. However the neuronal correlates of core components of this model have yet to be fully elucidated. Here we present data from two studies where human temporal cortical single neuron activity was recorded during tasks differentially affecting the maintenance component of verbal working memory. In Study One we vary the presence or absence of distracting items for the entire period of memory storage. In Study Two we vary the duration of storage so that distractors filled all, or only one-third of the time the memory was stored. Extracellular single neuron recordings were obtained from 36 subjects undergoing awake temporal lobe resections for epilepsy, 25 in Study one, 11 in Study two. Recordings were obtained from a total of 166 lateral temporal cortex neurons during performance of one of these two tasks, 86 study one, 80 study two. Significant changes in activity with distractor manipulation were present in 74 of these neurons (45%), 38 Study one, 36 Study two. In 48 (65%) of those there was increased activity during the period when distracting items were absent, 26 Study One, 22 Study Two. The magnitude of this increase was greater for Study One, 47.6%, than Study Two, 8.1%, paralleling the reduction in memory errors in the absence of distracters, for Study One of 70.3%, Study Two 26.3% These findings establish that human lateral temporal cortex is part of the neural system for working memory, with activity during maintenance of that memory that parallels performance, suggesting it represents active rehearsal. In 31 of these neurons (65%) this activity was an extension of that during working memory encoding that differed significantly from the neural processes recorded during overt and silent language tasks without a recent memory component, 17 Study one, 14 Study two

  10. Human Temporal Cortical Single Neuron Activity During Working Memory Maintenance

    Science.gov (United States)

    Zamora, Leona; Corina, David; Ojemann, George

    2016-01-01

    The Working Memory model of human memory, first introduced by Baddeley and Hitch (1974), has been one of the most influential psychological constructs in cognitive psychology and human neuroscience. However the neuronal correlates of core components of this model have yet to be fully elucidated. Here we present data from two studies where human temporal cortical single neuron activity was recorded during tasks differentially affecting the maintenance component of verbal working memory. In Study One we vary the presence or absence of distracting items for the entire period of memory storage. In Study Two we vary the duration of storage so that distractors filled all, or only one-third of the time the memory was stored. Extracellular single neuron recordings were obtained from 36 subjects undergoing awake temporal lobe resections for epilepsy, 25 in Study one, 11 in Study two. Recordings were obtained from a total of 166 lateral temporal cortex neurons during performance of one of these two tasks, 86 study one, 80 study two. Significant changes in activity with distractor manipulation were present in 74 of these neurons (45%), 38 Study one, 36 Study two. In 48 (65%) of those there was increased activity during the period when distracting items were absent, 26 Study One, 22 Study Two. The magnitude of this increase was greater for Study One, 47.6%, than Study Two, 8.1%, paralleling the reduction in memory errors in the absence of distracters, for Study One of 70.3%, Study Two 26.3% These findings establish that human lateral temporal cortex is part of the neural system for working memory, with activity during maintenance of that memory that parallels performance, suggesting it represents active rehearsal. In 31 of these neurons (65%) this activity was an extension of that during working memory encoding that differed significantly from the neural processes recorded during overt and silent language tasks without a recent memory component, 17 Study one, 14 Study two

  11. Spiking irregularity and frequency modulate the behavioral report of single-neuron stimulation.

    Science.gov (United States)

    Doron, Guy; von Heimendahl, Moritz; Schlattmann, Peter; Houweling, Arthur R; Brecht, Michael

    2014-02-05

    The action potential activity of single cortical neurons can evoke measurable sensory effects, but it is not known how spiking parameters and neuronal subtypes affect the evoked sensations. Here, we examined the effects of spike train irregularity, spike frequency, and spike number on the detectability of single-neuron stimulation in rat somatosensory cortex. For regular-spiking, putative excitatory neurons, detectability increased with spike train irregularity and decreasing spike frequencies but was not affected by spike number. Stimulation of single, fast-spiking, putative inhibitory neurons led to a larger sensory effect compared to regular-spiking neurons, and the effect size depended only on spike irregularity. An ideal-observer analysis suggests that, under our experimental conditions, rats were using integration windows of a few hundred milliseconds or more. Our data imply that the behaving animal is sensitive to single neurons' spikes and even to their temporal patterning. Copyright © 2014 Elsevier Inc. All rights reserved.

  12. Generation of Induced Neuronal Cells by the Single Reprogramming Factor ASCL1

    Directory of Open Access Journals (Sweden)

    Soham Chanda

    2014-08-01

    Full Text Available Direct conversion of nonneural cells to functional neurons holds great promise for neurological disease modeling and regenerative medicine. We previously reported rapid reprogramming of mouse embryonic fibroblasts (MEFs into mature induced neuronal (iN cells by forced expression of three transcription factors: ASCL1, MYT1L, and BRN2. Here, we show that ASCL1 alone is sufficient to generate functional iN cells from mouse and human fibroblasts and embryonic stem cells, indicating that ASCL1 is the key driver of iN cell reprogramming in different cell contexts and that the role of MYT1L and BRN2 is primarily to enhance the neuronal maturation process. ASCL1-induced single-factor neurons (1F-iN expressed mature neuronal markers, exhibited typical passive and active intrinsic membrane properties, and formed functional pre- and postsynaptic structures. Surprisingly, ASCL1-induced iN cells were predominantly excitatory, demonstrating that ASCL1 is permissive but alone not deterministic for the inhibitory neuronal lineage.

  13. Stability and Hopf Bifurcation of Fractional-Order Complex-Valued Single Neuron Model with Time Delay

    Science.gov (United States)

    Wang, Zhen; Wang, Xiaohong; Li, Yuxia; Huang, Xia

    2017-12-01

    In this paper, the problems of stability and Hopf bifurcation in a class of fractional-order complex-valued single neuron model with time delay are addressed. With the help of the stability theory of fractional-order differential equations and Laplace transforms, several new sufficient conditions, which ensure the stability of the system are derived. Taking the time delay as the bifurcation parameter, Hopf bifurcation is investigated and the critical value of the time delay for the occurrence of Hopf bifurcation is determined. Finally, two representative numerical examples are given to show the effectiveness of the theoretical results.

  14. Mapping cortical mesoscopic networks of single spiking cortical or sub-cortical neurons.

    Science.gov (United States)

    Xiao, Dongsheng; Vanni, Matthieu P; Mitelut, Catalin C; Chan, Allen W; LeDue, Jeffrey M; Xie, Yicheng; Chen, Andrew Cn; Swindale, Nicholas V; Murphy, Timothy H

    2017-02-04

    Understanding the basis of brain function requires knowledge of cortical operations over wide-spatial scales, but also within the context of single neurons. In vivo, wide-field GCaMP imaging and sub-cortical/cortical cellular electrophysiology were used in mice to investigate relationships between spontaneous single neuron spiking and mesoscopic cortical activity. We make use of a rich set of cortical activity motifs that are present in spontaneous activity in anesthetized and awake animals. A mesoscale spike-triggered averaging procedure allowed the identification of motifs that are preferentially linked to individual spiking neurons by employing genetically targeted indicators of neuronal activity. Thalamic neurons predicted and reported specific cycles of wide-scale cortical inhibition/excitation. In contrast, spike-triggered maps derived from single cortical neurons yielded spatio-temporal maps expected for regional cortical consensus function. This approach can define network relationships between any point source of neuronal spiking and mesoscale cortical maps.

  15. On the number of preganglionic neurones driving human postganglionic sympathetic neurones: a comparison of modelling and empirical data

    Directory of Open Access Journals (Sweden)

    Vaughan G Macefield

    2011-12-01

    Full Text Available Postganglionic sympathetic axons in awake healthy human subjects, regardless of their identity as muscle vasoconstrictor, cutaneous vasoconstrictor or sudomotor neurones, discharge with a low firing probability (~30%, generate low firing rates (~0.5 Hz and typically fire only once per cardiac interval. The purpose of the present study was to use modelling of spike trains in an attempt to define the number of preganglionic neurones that drive an individual postganglionic neurone. Artificial spike trains were generated in 1-3 preganglionic neurones converging onto a single postganglionic neurone. Each preganglionic input fired with a mean interval distribution of either 1000, 1500, 2000, 2500 or 3000 ms and the standard deviation varied between 0.5, 1.0 and 2.0 x the mean interval; the discharge frequency of each preganglionic neurone exhibited positive skewness and kurtosis. Of the 45 patterns examined, the mean discharge properties of the postganglionic neurone could only be explained by it being driven by, on average, two preganglionic neurones firing with a mean interspike interval of 2500 ms and SD of 5000 ms. The mean firing rate resulting from this pattern was 0.22 Hz, comparable to that of spontaneously active muscle vasoconstrictor neurones in healthy subjects (0.40 Hz. Likewise, the distribution of the number of spikes per cardiac interval was similar between the modelled and actual data: 0 spikes (69.5 vs 66.6 %, 1 spike (25.6 vs 21.2 %, 2 spikes (4.3 vs 6.4 %, 3 spikes (0.5 vs 1.7 % and 4 spikes (0.1 vs 0.7 %. Although some features of the firing patterns could be explained by the postganglionic neurone being driven by a single preganglionic neurone, none of the emulated firing patterns generated by the firing of three preganglionic neurones matched the discharge of the real neurones. These modelling data indicate that, on average, human postganglionic sympathetic neurones are driven by two preganglionic inputs.

  16. A Hybrid Fuzzy Time Series Approach Based on Fuzzy Clustering and Artificial Neural Network with Single Multiplicative Neuron Model

    Directory of Open Access Journals (Sweden)

    Ozge Cagcag Yolcu

    2013-01-01

    Full Text Available Particularly in recent years, artificial intelligence optimization techniques have been used to make fuzzy time series approaches more systematic and improve forecasting performance. Besides, some fuzzy clustering methods and artificial neural networks with different structures are used in the fuzzification of observations and determination of fuzzy relationships, respectively. In approaches considering the membership values, the membership values are determined subjectively or fuzzy outputs of the system are obtained by considering that there is a relation between membership values in identification of relation. This necessitates defuzzification step and increases the model error. In this study, membership values were obtained more systematically by using Gustafson-Kessel fuzzy clustering technique. The use of artificial neural network with single multiplicative neuron model in identification of fuzzy relation eliminated the architecture selection problem as well as the necessity for defuzzification step by constituting target values from real observations of time series. The training of artificial neural network with single multiplicative neuron model which is used for identification of fuzzy relation step is carried out with particle swarm optimization. The proposed method is implemented using various time series and the results are compared with those of previous studies to demonstrate the performance of the proposed method.

  17. Internally generated preactivation of single neurons in human medial frontal cortex predicts volition

    Science.gov (United States)

    Fried, Itzhak; Mukamel, Roy; Kreiman, Gabriel

    2011-01-01

    Understanding how self-initiated behavior is encoded by neuronal circuits in the human brain remains elusive. We recorded the activity of 1019 neurons while twelve subjects performed self-initiated finger movement. We report progressive neuronal recruitment over ~1500 ms before subjects report making the decision to move. We observed progressive increase or decrease in neuronal firing rate, particularly in the supplementary motor area (SMA), as the reported time of decision was approached. A population of 256 SMA neurons is sufficient to predict in single trials the impending decision to move with accuracy greater than 80% already 700 ms prior to subjects’ awareness. Furthermore, we predict, with a precision of a few hundred ms, the actual time point of this voluntary decision to move. We implement a computational model whereby volition emerges once a change in internally generated firing rate of neuronal assemblies crosses a threshold. PMID:21315264

  18. Gender differences in human single neuron responses to male emotional faces.

    Science.gov (United States)

    Newhoff, Morgan; Treiman, David M; Smith, Kris A; Steinmetz, Peter N

    2015-01-01

    Well-documented differences in the psychology and behavior of men and women have spurred extensive exploration of gender's role within the brain, particularly regarding emotional processing. While neuroanatomical studies clearly show differences between the sexes, the functional effects of these differences are less understood. Neuroimaging studies have shown inconsistent locations and magnitudes of gender differences in brain hemodynamic responses to emotion. To better understand the neurophysiology of these gender differences, we analyzed recordings of single neuron activity in the human brain as subjects of both genders viewed emotional expressions. This study included recordings of single-neuron activity of 14 (6 male) epileptic patients in four brain areas: amygdala (236 neurons), hippocampus (n = 270), anterior cingulate cortex (n = 256), and ventromedial prefrontal cortex (n = 174). Neural activity was recorded while participants viewed a series of avatar male faces portraying positive, negative or neutral expressions. Significant gender differences were found in the left amygdala, where 23% (n = 15∕66) of neurons in men were significantly affected by facial emotion, vs. 8% (n = 6∕76) of neurons in women. A Fisher's exact test comparing the two ratios found a highly significant difference between the two (p differences between genders at the single-neuron level in the human amygdala. These differences may reflect gender-based distinctions in evolved capacities for emotional processing and also demonstrate the importance of including subject gender as an independent factor in future studies of emotional processing by single neurons in the human amygdala.

  19. Biophysics Model of Heavy-Ion Degradation of Neuron Morphology in Mouse Hippocampal Granular Cell Layer Neurons.

    Science.gov (United States)

    Alp, Murat; Cucinotta, Francis A

    2018-03-01

    Exposure to heavy-ion radiation during cancer treatment or space travel may cause cognitive detriments that have been associated with changes in neuron morphology and plasticity. Observations in mice of reduced neuronal dendritic complexity have revealed a dependence on radiation quality and absorbed dose, suggesting that microscopic energy deposition plays an important role. In this work we used morphological data for mouse dentate granular cell layer (GCL) neurons and a stochastic model of particle track structure and microscopic energy deposition (ED) to develop a predictive model of high-charge and energy (HZE) particle-induced morphological changes to the complex structures of dendritic arbors. We represented dendrites as cylindrical segments of varying diameter with unit aspect ratios, and developed a fast sampling method to consider the stochastic distribution of ED by δ rays (secondary electrons) around the path of heavy ions, to reduce computational times. We introduce probabilistic models with a small number of parameters to describe the induction of precursor lesions that precede dendritic snipping, denoted as snip sites. Predictions for oxygen ( 16 O, 600 MeV/n) and titanium ( 48 Ti, 600 MeV/n) particles with LET of 16.3 and 129 keV/μm, respectively, are considered. Morphometric parameters to quantify changes in neuron morphology are described, including reduction in total dendritic length, number of branch points and branch numbers. Sholl analysis is applied for single neurons to elucidate dose-dependent reductions in dendritic complexity. We predict important differences in measurements from imaging of tissues from brain slices with single neuron cell observations due to the role of neuron death through both soma apoptosis and excessive dendritic length reduction. To further elucidate the role of track structure, random segment excision (snips) models are introduced and a sensitivity study of the effects of the modes of neuron death in predictions

  20. Racing to learn: statistical inference and learning in a single spiking neuron with adaptive kernels.

    Science.gov (United States)

    Afshar, Saeed; George, Libin; Tapson, Jonathan; van Schaik, André; Hamilton, Tara J

    2014-01-01

    This paper describes the Synapto-dendritic Kernel Adapting Neuron (SKAN), a simple spiking neuron model that performs statistical inference and unsupervised learning of spatiotemporal spike patterns. SKAN is the first proposed neuron model to investigate the effects of dynamic synapto-dendritic kernels and demonstrate their computational power even at the single neuron scale. The rule-set defining the neuron is simple: there are no complex mathematical operations such as normalization, exponentiation or even multiplication. The functionalities of SKAN emerge from the real-time interaction of simple additive and binary processes. Like a biological neuron, SKAN is robust to signal and parameter noise, and can utilize both in its operations. At the network scale neurons are locked in a race with each other with the fastest neuron to spike effectively "hiding" its learnt pattern from its neighbors. The robustness to noise, high speed, and simple building blocks not only make SKAN an interesting neuron model in computational neuroscience, but also make it ideal for implementation in digital and analog neuromorphic systems which is demonstrated through an implementation in a Field Programmable Gate Array (FPGA). Matlab, Python, and Verilog implementations of SKAN are available at: http://www.uws.edu.au/bioelectronics_neuroscience/bens/reproducible_research.

  1. Contribution of LFP dynamics to single-neuron spiking variability in motor cortex during movement execution

    Science.gov (United States)

    Rule, Michael E.; Vargas-Irwin, Carlos; Donoghue, John P.; Truccolo, Wilson

    2015-01-01

    Understanding the sources of variability in single-neuron spiking responses is an important open problem for the theory of neural coding. This variability is thought to result primarily from spontaneous collective dynamics in neuronal networks. Here, we investigate how well collective dynamics reflected in motor cortex local field potentials (LFPs) can account for spiking variability during motor behavior. Neural activity was recorded via microelectrode arrays implanted in ventral and dorsal premotor and primary motor cortices of non-human primates performing naturalistic 3-D reaching and grasping actions. Point process models were used to quantify how well LFP features accounted for spiking variability not explained by the measured 3-D reach and grasp kinematics. LFP features included the instantaneous magnitude, phase and analytic-signal components of narrow band-pass filtered (δ,θ,α,β) LFPs, and analytic signal and amplitude envelope features in higher-frequency bands. Multiband LFP features predicted single-neuron spiking (1ms resolution) with substantial accuracy as assessed via ROC analysis. Notably, however, models including both LFP and kinematics features displayed marginal improvement over kinematics-only models. Furthermore, the small predictive information added by LFP features to kinematic models was redundant to information available in fast-timescale (spiking history. Overall, information in multiband LFP features, although predictive of single-neuron spiking during movement execution, was redundant to information available in movement parameters and spiking history. Our findings suggest that, during movement execution, collective dynamics reflected in motor cortex LFPs primarily relate to sensorimotor processes directly controlling movement output, adding little explanatory power to variability not accounted by movement parameters. PMID:26157365

  2. A neuron-astrocyte transistor-like model for neuromorphic dressed neurons.

    Science.gov (United States)

    Valenza, G; Pioggia, G; Armato, A; Ferro, M; Scilingo, E P; De Rossi, D

    2011-09-01

    Experimental evidences on the role of the synaptic glia as an active partner together with the bold synapse in neuronal signaling and dynamics of neural tissue strongly suggest to investigate on a more realistic neuron-glia model for better understanding human brain processing. Among the glial cells, the astrocytes play a crucial role in the tripartite synapsis, i.e. the dressed neuron. A well-known two-way astrocyte-neuron interaction can be found in the literature, completely revising the purely supportive role for the glia. The aim of this study is to provide a computationally efficient model for neuron-glia interaction. The neuron-glia interactions were simulated by implementing the Li-Rinzel model for an astrocyte and the Izhikevich model for a neuron. Assuming the dressed neuron dynamics similar to the nonlinear input-output characteristics of a bipolar junction transistor, we derived our computationally efficient model. This model may represent the fundamental computational unit for the development of real-time artificial neuron-glia networks opening new perspectives in pattern recognition systems and in brain neurophysiology. Copyright © 2011 Elsevier Ltd. All rights reserved.

  3. How neurons migrate: a dynamic in-silico model of neuronal migration in the developing cortex

    LENUS (Irish Health Repository)

    Setty, Yaki

    2011-09-30

    Abstract Background Neuronal migration, the process by which neurons migrate from their place of origin to their final position in the brain, is a central process for normal brain development and function. Advances in experimental techniques have revealed much about many of the molecular components involved in this process. Notwithstanding these advances, how the molecular machinery works together to govern the migration process has yet to be fully understood. Here we present a computational model of neuronal migration, in which four key molecular entities, Lis1, DCX, Reelin and GABA, form a molecular program that mediates the migration process. Results The model simulated the dynamic migration process, consistent with in-vivo observations of morphological, cellular and population-level phenomena. Specifically, the model reproduced migration phases, cellular dynamics and population distributions that concur with experimental observations in normal neuronal development. We tested the model under reduced activity of Lis1 and DCX and found an aberrant development similar to observations in Lis1 and DCX silencing expression experiments. Analysis of the model gave rise to unforeseen insights that could guide future experimental study. Specifically: (1) the model revealed the possibility that under conditions of Lis1 reduced expression, neurons experience an oscillatory neuron-glial association prior to the multipolar stage; and (2) we hypothesized that observed morphology variations in rats and mice may be explained by a single difference in the way that Lis1 and DCX stimulate bipolar motility. From this we make the following predictions: (1) under reduced Lis1 and enhanced DCX expression, we predict a reduced bipolar migration in rats, and (2) under enhanced DCX expression in mice we predict a normal or a higher bipolar migration. Conclusions We present here a system-wide computational model of neuronal migration that integrates theory and data within a precise

  4. How neurons migrate: a dynamic in-silico model of neuronal migration in the developing cortex

    Directory of Open Access Journals (Sweden)

    Skoblov Nikita

    2011-09-01

    Full Text Available Abstract Background Neuronal migration, the process by which neurons migrate from their place of origin to their final position in the brain, is a central process for normal brain development and function. Advances in experimental techniques have revealed much about many of the molecular components involved in this process. Notwithstanding these advances, how the molecular machinery works together to govern the migration process has yet to be fully understood. Here we present a computational model of neuronal migration, in which four key molecular entities, Lis1, DCX, Reelin and GABA, form a molecular program that mediates the migration process. Results The model simulated the dynamic migration process, consistent with in-vivo observations of morphological, cellular and population-level phenomena. Specifically, the model reproduced migration phases, cellular dynamics and population distributions that concur with experimental observations in normal neuronal development. We tested the model under reduced activity of Lis1 and DCX and found an aberrant development similar to observations in Lis1 and DCX silencing expression experiments. Analysis of the model gave rise to unforeseen insights that could guide future experimental study. Specifically: (1 the model revealed the possibility that under conditions of Lis1 reduced expression, neurons experience an oscillatory neuron-glial association prior to the multipolar stage; and (2 we hypothesized that observed morphology variations in rats and mice may be explained by a single difference in the way that Lis1 and DCX stimulate bipolar motility. From this we make the following predictions: (1 under reduced Lis1 and enhanced DCX expression, we predict a reduced bipolar migration in rats, and (2 under enhanced DCX expression in mice we predict a normal or a higher bipolar migration. Conclusions We present here a system-wide computational model of neuronal migration that integrates theory and data within a

  5. Gender Differences in Human Single Neuron Responses to Male Emotional Faces

    Directory of Open Access Journals (Sweden)

    Morgan eNewhoff

    2015-09-01

    Full Text Available Well-documented differences in the psychology and behavior of men and women have spurred extensive exploration of gender's role within the brain, particularly regarding emotional processing. While neuroanatomical studies clearly show differences between the sexes, the functional effects of these differences are less understood. Neuroimaging studies have shown inconsistent locations and magnitudes of gender differences in brain hemodynamic responses to emotion. To better understand the neurophysiology of these gender differences, we analyzed recordings of single neuron activity in the human brain as subjects of both genders viewed emotional expressions.This study included recordings of single-neuron activity of 14 (6 male epileptic patients in four brain areas: amygdala (236 neurons, hippocampus (n=270, anterior cingulate cortex (n=256, and ventromedial prefrontal cortex (n=174. Neural activity was recorded while participants viewed a series of avatar male faces portraying positive, negative or neutral expressions.Significant gender differences were found in the left amygdala, where 23% (n=15/66 of neurons in men were significantly affected by facial emotion, versus 8% (n=6/76 of neurons in women. A Fisher's exact test comparing the two ratios found a highly significant difference between the two (p<0.01. These results show specific differences between genders at the single-neuron level in the human amygdala. These differences may reflect gender-based distinctions in evolved capacities for emotional processing and also demonstrate the importance of including subject gender as an independent factor in future studies of emotional processing by single neurons in the human amygdala.

  6. From in silico astrocyte cell models to neuron-astrocyte network models: A review.

    Science.gov (United States)

    Oschmann, Franziska; Berry, Hugues; Obermayer, Klaus; Lenk, Kerstin

    2018-01-01

    The idea that astrocytes may be active partners in synaptic information processing has recently emerged from abundant experimental reports. Because of their spatial proximity to neurons and their bidirectional communication with them, astrocytes are now considered as an important third element of the synapse. Astrocytes integrate and process synaptic information and by doing so generate cytosolic calcium signals that are believed to reflect neuronal transmitter release. Moreover, they regulate neuronal information transmission by releasing gliotransmitters into the synaptic cleft affecting both pre- and postsynaptic receptors. Concurrent with the first experimental reports of the astrocytic impact on neural network dynamics, computational models describing astrocytic functions have been developed. In this review, we give an overview over the published computational models of astrocytic functions, from single-cell dynamics to the tripartite synapse level and network models of astrocytes and neurons. Copyright © 2017 Elsevier Inc. All rights reserved.

  7. Laser capture microdissection of enriched populations of neurons or single neurons for gene expression analysis after traumatic brain injury.

    Science.gov (United States)

    Boone, Deborah R; Sell, Stacy L; Hellmich, Helen Lee

    2013-04-10

    Long-term cognitive disability after TBI is associated with injury-induced neurodegeneration in the hippocampus-a region in the medial temporal lobe that is critical for learning, memory and executive function. Hence our studies focus on gene expression analysis of specific neuronal populations in distinct subregions of the hippocampus. The technique of laser capture microdissection (LCM), introduced in 1996 by Emmert-Buck, et al., has allowed for significant advances in gene expression analysis of single cells and enriched populations of cells from heterogeneous tissues such as the mammalian brain that contains thousands of functional cell types. We use LCM and a well established rat model of traumatic brain injury (TBI) to investigate the molecular mechanisms that underlie the pathogenesis of TBI. Following fluid-percussion TBI, brains are removed at pre-determined times post-injury, immediately frozen on dry ice, and prepared for sectioning in a cryostat. The rat brains can be embedded in OCT and sectioned immediately, or stored several months at -80 °C before sectioning for laser capture microdissection. Additionally, we use LCM to study the effects of TBI on circadian rhythms. For this, we capture neurons from the suprachiasmatic nuclei that contain the master clock of the mammalian brain. Here, we demonstrate the use of LCM to obtain single identified neurons (injured and degenerating, Fluoro-Jade-positive, or uninjured, Fluoro-Jade-negative) and enriched populations of hippocampal neurons for subsequent gene expression analysis by real time PCR and/or whole-genome microarrays. These LCM-enabled studies have revealed that the selective vulnerability of anatomically distinct regions of the rat hippocampus are reflected in the different gene expression profiles of different populations of neurons obtained by LCM from these distinct regions. The results from our single-cell studies, where we compare the transcriptional profiles of dying and adjacent surviving

  8. Bifurcation software in Matlab with applications in neuronal modeling.

    Science.gov (United States)

    Govaerts, Willy; Sautois, Bart

    2005-02-01

    Many biological phenomena, notably in neuroscience, can be modeled by dynamical systems. We describe a recent improvement of a Matlab software package for dynamical systems with applications to modeling single neurons and all-to-all connected networks of neurons. The new software features consist of an object-oriented approach to bifurcation computations and the partial inclusion of C-code to speed up the computation. As an application, we study the origin of the spiking behaviour of neurons when the equilibrium state is destabilized by an incoming current. We show that Class II behaviour, i.e. firing with a finite frequency, is possible even if the destabilization occurs through a saddle-node bifurcation. Furthermore, we show that synchronization of an all-to-all connected network of such neurons with only excitatory connections is also possible in this case.

  9. Stable long-term chronic brain mapping at the single-neuron level.

    Science.gov (United States)

    Fu, Tian-Ming; Hong, Guosong; Zhou, Tao; Schuhmann, Thomas G; Viveros, Robert D; Lieber, Charles M

    2016-10-01

    Stable in vivo mapping and modulation of the same neurons and brain circuits over extended periods is critical to both neuroscience and medicine. Current electrical implants offer single-neuron spatiotemporal resolution but are limited by such factors as relative shear motion and chronic immune responses during long-term recording. To overcome these limitations, we developed a chronic in vivo recording and stimulation platform based on flexible mesh electronics, and we demonstrated stable multiplexed local field potentials and single-unit recordings in mouse brains for at least 8 months without probe repositioning. Properties of acquired signals suggest robust tracking of the same neurons over this period. This recording and stimulation platform allowed us to evoke stable single-neuron responses to chronic electrical stimulation and to carry out longitudinal studies of brain aging in freely behaving mice. Such advantages could open up future studies in mapping and modulating changes associated with learning, aging and neurodegenerative diseases.

  10. Single-neuron identification of chemical constituents, physiological changes, and metabolism using mass spectrometry.

    Science.gov (United States)

    Zhu, Hongying; Zou, Guichang; Wang, Ning; Zhuang, Meihui; Xiong, Wei; Huang, Guangming

    2017-03-07

    The use of single-cell assays has emerged as a cutting-edge technique during the past decade. Although single-cell mass spectrometry (MS) has recently achieved remarkable results, deep biological insights have not yet been obtained, probably because of various technical issues, including the unavoidable use of matrices, the inability to maintain cell viability, low throughput because of sample pretreatment, and the lack of recordings of cell physiological activities from the same cell. In this study, we describe a patch clamp/MS-based platform that enables the sensitive, rapid, and in situ chemical profiling of single living neurons. This approach integrates modified patch clamp technique and modified MS measurements to directly collect and detect nanoliter-scale samples from the cytoplasm of single neurons in mice brain slices. Abundant possible cytoplasmic constituents were detected in a single neuron at a relatively fast rate, and over 50 metabolites were identified in this study. The advantages of direct, rapid, and in situ sampling and analysis enabled us to measure the biological activities of the cytoplasmic constituents in a single neuron, including comparing neuron types by cytoplasmic chemical constituents; observing changes in constituent concentrations as the physiological conditions, such as age, vary; and identifying the metabolic pathways of small molecules.

  11. Ensembles of gustatory cortical neurons anticipate and discriminate between tastants in a single lick

    Directory of Open Access Journals (Sweden)

    Jennifer R Stapleton

    2007-10-01

    Full Text Available The gustatory cortex (GC processes chemosensory and somatosensory information and is involved in learning and anticipation. Previously we found that a subpopulation of GC neurons responded to tastants in a single lick (Stapleton et al., 2006. Here we extend this investigation to determine if small ensembles of GC neurons, obtained while rats received blocks of tastants on a fixed ratio schedule (FR5, can discriminate between tastants and their concentrations after a single 50 µL delivery. In the FR5 schedule subjects received tastants every fifth (reinforced lick and the intervening licks were unreinforced. The ensemble firing patterns were analyzed with a Bayesian generalized linear model whose parameters included the firing rates and temporal patterns of the spike trains. We found that when both the temporal and rate parameters were included, 12 of 13 ensembles correctly identified single tastant deliveries. We also found that the activity during the unreinforced licks contained signals regarding the identity of the upcoming tastant, which suggests that GC neurons contain anticipatory information about the next tastant delivery. To support this finding we performed experiments in which tastant delivery was randomized within each block and found that the neural activity following the unreinforced licks did not predict the upcoming tastant. Collectively, these results suggest that after a single lick ensembles of GC neurons can discriminate between tastants, that they may utilize both temporal and rate information, and when the tastant delivery is repetitive ensembles contain information about the identity of the upcoming tastant delivery.

  12. Recording single neurons' action potentials from freely moving pigeons across three stages of learning.

    Science.gov (United States)

    Starosta, Sarah; Stüttgen, Maik C; Güntürkün, Onur

    2014-06-02

    While the subject of learning has attracted immense interest from both behavioral and neural scientists, only relatively few investigators have observed single-neuron activity while animals are acquiring an operantly conditioned response, or when that response is extinguished. But even in these cases, observation periods usually encompass only a single stage of learning, i.e. acquisition or extinction, but not both (exceptions include protocols employing reversal learning; see Bingman et al.(1) for an example). However, acquisition and extinction entail different learning mechanisms and are therefore expected to be accompanied by different types and/or loci of neural plasticity. Accordingly, we developed a behavioral paradigm which institutes three stages of learning in a single behavioral session and which is well suited for the simultaneous recording of single neurons' action potentials. Animals are trained on a single-interval forced choice task which requires mapping each of two possible choice responses to the presentation of different novel visual stimuli (acquisition). After having reached a predefined performance criterion, one of the two choice responses is no longer reinforced (extinction). Following a certain decrement in performance level, correct responses are reinforced again (reacquisition). By using a new set of stimuli in every session, animals can undergo the acquisition-extinction-reacquisition process repeatedly. Because all three stages of learning occur in a single behavioral session, the paradigm is ideal for the simultaneous observation of the spiking output of multiple single neurons. We use pigeons as model systems, but the task can easily be adapted to any other species capable of conditioned discrimination learning.

  13. A Route to Chaotic Behavior of Single Neuron Exposed to External Electromagnetic Radiation.

    Science.gov (United States)

    Feng, Peihua; Wu, Ying; Zhang, Jiazhong

    2017-01-01

    Non-linear behaviors of a single neuron described by Fitzhugh-Nagumo (FHN) neuron model, with external electromagnetic radiation considered, is investigated. It is discovered that with external electromagnetic radiation in form of a cosine function, the mode selection of membrane potential occurs among periodic, quasi-periodic, and chaotic motions as increasing the frequency of external transmembrane current, which is selected as a sinusoidal function. When the frequency is small or large enough, periodic, and quasi-periodic motions are captured alternatively. Otherwise, when frequency is in interval 0.778 electromagnetic radiation. The frequency apparently plays a more important role in determining the system behavior.

  14. Discrimination of Communication Vocalizations by Single Neurons and Groups of Neurons in the Auditory Midbrain

    OpenAIRE

    Schneider, David M.; Woolley, Sarah M. N.

    2010-01-01

    Many social animals including songbirds use communication vocalizations for individual recognition. The perception of vocalizations depends on the encoding of complex sounds by neurons in the ascending auditory system, each of which is tuned to a particular subset of acoustic features. Here, we examined how well the responses of single auditory neurons could be used to discriminate among bird songs and we compared discriminability to spectrotemporal tuning. We then used biologically realistic...

  15. hamlet, a binary genetic switch between single- and multiple- dendrite neuron morphology.

    Science.gov (United States)

    Moore, Adrian W; Jan, Lily Yeh; Jan, Yuh Nung

    2002-08-23

    The dendritic morphology of neurons determines the number and type of inputs they receive. In the Drosophila peripheral nervous system (PNS), the external sensory (ES) neurons have a single nonbranched dendrite, whereas the lineally related multidendritic (MD) neurons have extensively branched dendritic arbors. We report that hamlet is a binary genetic switch between these contrasting morphological types. In hamlet mutants, ES neurons are converted to an MD fate, whereas ectopic hamlet expression in MD precursors results in transformation of MD neurons into ES neurons. Moreover, hamlet expression induced in MD neurons undergoing dendrite outgrowth drastically reduces arbor branching.

  16. Imaging Action Potential in Single Mammalian Neurons by Tracking the Accompanying Sub-Nanometer Mechanical Motion.

    Science.gov (United States)

    Yang, Yunze; Liu, Xian-Wei; Wang, Hui; Yu, Hui; Guan, Yan; Wang, Shaopeng; Tao, Nongjian

    2018-03-28

    Action potentials in neurons have been studied traditionally by intracellular electrophysiological recordings and more recently by the fluorescence detection methods. Here we describe a label-free optical imaging method that can measure mechanical motion in single cells with a sub-nanometer detection limit. Using the method, we have observed sub-nanometer mechanical motion accompanying the action potential in single mammalian neurons by averaging the repeated action potential spikes. The shape and width of the transient displacement are similar to those of the electrically recorded action potential, but the amplitude varies from neuron to neuron, and from one region of a neuron to another, ranging from 0.2-0.4 nm. The work indicates that action potentials may be studied noninvasively in single mammalian neurons by label-free imaging of the accompanying sub-nanometer mechanical motion.

  17. Multiplicative mixing of object identity and image attributes in single inferior temporal neurons.

    Science.gov (United States)

    Ratan Murty, N Apurva; Arun, S P

    2018-04-03

    Object recognition is challenging because the same object can produce vastly different images, mixing signals related to its identity with signals due to its image attributes, such as size, position, rotation, etc. Previous studies have shown that both signals are present in high-level visual areas, but precisely how they are combined has remained unclear. One possibility is that neurons might encode identity and attribute signals multiplicatively so that each can be efficiently decoded without interference from the other. Here, we show that, in high-level visual cortex, responses of single neurons can be explained better as a product rather than a sum of tuning for object identity and tuning for image attributes. This subtle effect in single neurons produced substantially better population decoding of object identity and image attributes in the neural population as a whole. This property was absent both in low-level vision models and in deep neural networks. It was also unique to invariances: when tested with two-part objects, neural responses were explained better as a sum than as a product of part tuning. Taken together, our results indicate that signals requiring separate decoding, such as object identity and image attributes, are combined multiplicatively in IT neurons, whereas signals that require integration (such as parts in an object) are combined additively. Copyright © 2018 the Author(s). Published by PNAS.

  18. High-Throughput Mapping of Single-Neuron Projections by Sequencing of Barcoded RNA.

    Science.gov (United States)

    Kebschull, Justus M; Garcia da Silva, Pedro; Reid, Ashlan P; Peikon, Ian D; Albeanu, Dinu F; Zador, Anthony M

    2016-09-07

    Neurons transmit information to distant brain regions via long-range axonal projections. In the mouse, area-to-area connections have only been systematically mapped using bulk labeling techniques, which obscure the diverse projections of intermingled single neurons. Here we describe MAPseq (Multiplexed Analysis of Projections by Sequencing), a technique that can map the projections of thousands or even millions of single neurons by labeling large sets of neurons with random RNA sequences ("barcodes"). Axons are filled with barcode mRNA, each putative projection area is dissected, and the barcode mRNA is extracted and sequenced. Applying MAPseq to the locus coeruleus (LC), we find that individual LC neurons have preferred cortical targets. By recasting neuroanatomy, which is traditionally viewed as a problem of microscopy, as a problem of sequencing, MAPseq harnesses advances in sequencing technology to permit high-throughput interrogation of brain circuits. Copyright © 2016 Elsevier Inc. All rights reserved.

  19. Biophysically realistic minimal model of dopamine neuron

    Science.gov (United States)

    Oprisan, Sorinel

    2008-03-01

    We proposed and studied a new biophysically relevant computational model of dopaminergic neurons. Midbrain dopamine neurons are involved in motivation and the control of movement, and have been implicated in various pathologies such as Parkinson's disease, schizophrenia, and drug abuse. The model we developed is a single-compartment Hodgkin-Huxley (HH)-type parallel conductance membrane model. The model captures the essential mechanisms underlying the slow oscillatory potentials and plateau potential oscillations. The main currents involved are: 1) a voltage-dependent fast calcium current, 2) a small conductance potassium current that is modulated by the cytosolic concentration of calcium, and 3) a slow voltage-activated potassium current. We developed multidimensional bifurcation diagrams and extracted the effective domains of sustained oscillations. The model includes a calcium balance due to the fundamental importance of calcium influx as proved by simultaneous electrophysiological and calcium imaging procedure. Although there are significant evidences to suggest a partially electrogenic calcium pump, all previous models considered only elecrtogenic pumps. We investigated the effect of the electrogenic calcium pump on the bifurcation diagram of the model and compared our findings against the experimental results.

  20. Coding of vocalizations by single neurons in ventrolateral prefrontal cortex.

    Science.gov (United States)

    Plakke, Bethany; Diltz, Mark D; Romanski, Lizabeth M

    2013-11-01

    Neuronal activity in single prefrontal neurons has been correlated with behavioral responses, rules, task variables and stimulus features. In the non-human primate, neurons recorded in ventrolateral prefrontal cortex (VLPFC) have been found to respond to species-specific vocalizations. Previous studies have found multisensory neurons which respond to simultaneously presented faces and vocalizations in this region. Behavioral data suggests that face and vocal information are inextricably linked in animals and humans and therefore may also be tightly linked in the coding of communication calls in prefrontal neurons. In this study we therefore examined the role of VLPFC in encoding vocalization call type information. Specifically, we examined previously recorded single unit responses from the VLPFC in awake, behaving rhesus macaques in response to 3 types of species-specific vocalizations made by 3 individual callers. Analysis of responses by vocalization call type and caller identity showed that ∼19% of cells had a main effect of call type with fewer cells encoding caller. Classification performance of VLPFC neurons was ∼42% averaged across the population. When assessed at discrete time bins, classification performance reached 70 percent for coos in the first 300 ms and remained above chance for the duration of the response period, though performance was lower for other call types. In light of the sub-optimal classification performance of the majority of VLPFC neurons when only vocal information is present, and the recent evidence that most VLPFC neurons are multisensory, the potential enhancement of classification with the addition of accompanying face information is discussed and additional studies recommended. Behavioral and neuronal evidence has shown a considerable benefit in recognition and memory performance when faces and voices are presented simultaneously. In the natural environment both facial and vocalization information is present simultaneously and

  1. Individual mediodorsal thalamic neurons project to multiple areas of the rat prefrontal cortex: A single neuron-tracing study using virus vectors.

    Science.gov (United States)

    Kuramoto, Eriko; Pan, Shixiu; Furuta, Takahiro; Tanaka, Yasuhiro R; Iwai, Haruki; Yamanaka, Atsushi; Ohno, Sachi; Kaneko, Takeshi; Goto, Tetsuya; Hioki, Hiroyuki

    2017-01-01

    The prefrontal cortex has an important role in a variety of cognitive and executive processes, and is generally defined by its reciprocal connections with the mediodorsal thalamic nucleus (MD). The rat MD is mainly subdivided into three segments, the medial (MDm), central (MDc), and lateral (MDl) divisions, on the basis of the cytoarchitecture and chemoarchitecture. The MD segments are known to topographically project to multiple prefrontal areas at the population level: the MDm mainly to the prelimbic, infralimbic, and agranular insular areas; the MDc to the orbital and agranular insular areas; and the MDl to the prelimbic and anterior cingulate areas. However, it is unknown whether individual MD neurons project to single or multiple prefrontal cortical areas. In the present study, we visualized individual MD neurons with Sindbis virus vectors, and reconstructed whole structures of MD neurons. While the main cortical projection targets of MDm, MDc, and MDl neurons were generally consistent with those of previous results, it was found that individual MD neurons sent their axon fibers to multiple prefrontal areas, and displayed various projection patterns in the target areas. Furthermore, the axons of single MD neurons were not homogeneously spread, but were rather distributed to form patchy axon arbors approximately 1 mm in diameter. The multiple-area projections and patchy axon arbors of single MD neurons might be able to coactivate cortical neuron groups in distant prefrontal areas simultaneously. Furthermore, considerable heterogeneity of the projection patterns is likely, to recruit the different sets of cortical neurons, and thus contributes to a variety of prefrontal functions. J. Comp. Neurol. 525:166-185, 2017. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

  2. Pulsed neural networks consisting of single-flux-quantum spiking neurons

    International Nuclear Information System (INIS)

    Hirose, T.; Asai, T.; Amemiya, Y.

    2007-01-01

    An inhibitory pulsed neural network was developed for brain-like information processing, by using single-flux-quantum (SFQ) circuits. It consists of spiking neuron devices that are coupled to each other through all-to-all inhibitory connections. The network selects neural activity. The operation of the neural network was confirmed by computer simulation. SFQ neuron devices can imitate the operation of the inhibition phenomenon of neural networks

  3. Comparison of Langevin and Markov channel noise models for neuronal signal generation.

    Science.gov (United States)

    Sengupta, B; Laughlin, S B; Niven, J E

    2010-01-01

    The stochastic opening and closing of voltage-gated ion channels produce noise in neurons. The effect of this noise on the neuronal performance has been modeled using either an approximate or Langevin model based on stochastic differential equations or an exact model based on a Markov process model of channel gating. Yet whether the Langevin model accurately reproduces the channel noise produced by the Markov model remains unclear. Here we present a comparison between Langevin and Markov models of channel noise in neurons using single compartment Hodgkin-Huxley models containing either Na+ and K+, or only K+ voltage-gated ion channels. The performance of the Langevin and Markov models was quantified over a range of stimulus statistics, membrane areas, and channel numbers. We find that in comparison to the Markov model, the Langevin model underestimates the noise contributed by voltage-gated ion channels, overestimating information rates for both spiking and nonspiking membranes. Even with increasing numbers of channels, the difference between the two models persists. This suggests that the Langevin model may not be suitable for accurately simulating channel noise in neurons, even in simulations with large numbers of ion channels.

  4. Somatosensory neuron types identified by high-coverage single-cell RNA-sequencing and functional heterogeneity

    Science.gov (United States)

    Li, Chang-Lin; Li, Kai-Cheng; Wu, Dan; Chen, Yan; Luo, Hao; Zhao, Jing-Rong; Wang, Sa-Shuang; Sun, Ming-Ming; Lu, Ying-Jin; Zhong, Yan-Qing; Hu, Xu-Ye; Hou, Rui; Zhou, Bei-Bei; Bao, Lan; Xiao, Hua-Sheng; Zhang, Xu

    2016-01-01

    Sensory neurons are distinguished by distinct signaling networks and receptive characteristics. Thus, sensory neuron types can be defined by linking transcriptome-based neuron typing with the sensory phenotypes. Here we classify somatosensory neurons of the mouse dorsal root ganglion (DRG) by high-coverage single-cell RNA-sequencing (10 950 ± 1 218 genes per neuron) and neuron size-based hierarchical clustering. Moreover, single DRG neurons responding to cutaneous stimuli are recorded using an in vivo whole-cell patch clamp technique and classified by neuron-type genetic markers. Small diameter DRG neurons are classified into one type of low-threshold mechanoreceptor and five types of mechanoheat nociceptors (MHNs). Each of the MHN types is further categorized into two subtypes. Large DRG neurons are categorized into four types, including neurexophilin 1-expressing MHNs and mechanical nociceptors (MNs) expressing BAI1-associated protein 2-like 1 (Baiap2l1). Mechanoreceptors expressing trafficking protein particle complex 3-like and Baiap2l1-marked MNs are subdivided into two subtypes each. These results provide a new system for cataloging somatosensory neurons and their transcriptome databases. PMID:26691752

  5. Kaleido: Visualizing Big Brain Data with Automatic Color Assignment for Single-Neuron Images.

    Science.gov (United States)

    Wang, Ting-Yuan; Chen, Nan-Yow; He, Guan-Wei; Wang, Guo-Tzau; Shih, Chi-Tin; Chiang, Ann-Shyn

    2018-03-03

    Effective 3D visualization is essential for connectomics analysis, where the number of neural images easily reaches over tens of thousands. A formidable challenge is to simultaneously visualize a large number of distinguishable single-neuron images, with reasonable processing time and memory for file management and 3D rendering. In the present study, we proposed an algorithm named "Kaleido" that can visualize up to at least ten thousand single neurons from the Drosophila brain using only a fraction of the memory traditionally required, without increasing computing time. Adding more brain neurons increases memory only nominally. Importantly, Kaleido maximizes color contrast between neighboring neurons so that individual neurons can be easily distinguished. Colors can also be assigned to neurons based on biological relevance, such as gene expression, neurotransmitters, and/or development history. For cross-lab examination, the identity of every neuron is retrievable from the displayed image. To demonstrate the effectiveness and tractability of the method, we applied Kaleido to visualize the 10,000 Drosophila brain neurons obtained from the FlyCircuit database ( http://www.flycircuit.tw/modules.php?name=kaleido ). Thus, Kaleido visualization requires only sensible computer memory for manual examination of big connectomics data.

  6. Study of GABAergic extra-synaptic tonic inhibition in single neurons and neural populations by traversing neural scales: application to propofol-induced anaesthesia.

    Science.gov (United States)

    Hutt, Axel; Buhry, Laure

    2014-12-01

    Anaesthetic agents are known to affect extra-synaptic GABAergic receptors, which induce tonic inhibitory currents. Since these receptors are very sensitive to small concentrations of agents, they are supposed to play an important role in the underlying neural mechanism of general anaesthesia. Moreover anaesthetic agents modulate the encephalographic activity (EEG) of subjects and hence show an effect on neural populations. To understand better the tonic inhibition effect in single neurons on neural populations and hence how it affects the EEG, the work considers single neurons and neural populations in a steady-state and studies numerically and analytically the modulation of their firing rate and nonlinear gain with respect to different levels of tonic inhibition. We consider populations of both type-I (Leaky Integrate-and-Fire model) and type-II (Morris-Lecar model) neurons. To bridge the single neuron description to the population description analytically, a recently proposed statistical approach is employed which allows to derive new analytical expressions for the population firing rate for type-I neurons. In addition, the work shows the derivation of a novel transfer function for type-I neurons as considered in neural mass models and studies briefly the interaction of synaptic and extra-synaptic inhibition. We reveal a strong subtractive and divisive effect of tonic inhibition in type-I neurons, i.e. a shift of the firing rate to higher excitation levels accompanied by a change of the nonlinear gain. Tonic inhibition shortens the excitation window of type-II neurons and their populations while maintaining the nonlinear gain. The gained results are interpreted in the context of recent experimental findings under propofol-induced anaesthesia.

  7. Single-cell resolution mapping of neuronal damage in acute focal cerebral ischemia using thallium autometallography.

    Science.gov (United States)

    Stöber, Franziska; Baldauf, Kathrin; Ziabreva, Iryna; Harhausen, Denise; Zille, Marietta; Neubert, Jenni; Reymann, Klaus G; Scheich, Henning; Dirnagl, Ulrich; Schröder, Ulrich H; Wunder, Andreas; Goldschmidt, Jürgen

    2014-01-01

    Neuronal damage shortly after onset or after brief episodes of cerebral ischemia has remained difficult to assess with clinical and preclinical imaging techniques as well as with microscopical methods. We here show, in rodent models of middle cerebral artery occlusion (MCAO), that neuronal damage in acute focal cerebral ischemia can be mapped with single-cell resolution using thallium autometallography (TlAMG), a histochemical technique for the detection of the K(+)-probe thallium (Tl(+)) in the brain. We intravenously injected rats and mice with thallium diethyldithiocarbamate (TlDDC), a lipophilic chelate complex that releases Tl(+) after crossing the blood-brain barrier. We found, within the territories of the affected arteries, areas of markedly reduced neuronal Tl(+) uptake in all animals at all time points studied ranging from 15 minutes to 24 hours after MCAO. In large lesions at early time points, areas with neuronal and astrocytic Tl(+) uptake below thresholds of detection were surrounded by putative penumbral zones with preserved but diminished Tl(+) uptake. At 24 hours, the areas of reduced Tl(+)uptake matched with areas delineated by established markers of neuronal damage. The results suggest the use of (201)TlDDC for preclinical and clinical single-photon emission computed tomography (SPECT) imaging of hyperacute alterations in brain K(+) metabolism and prediction of tissue viability in cerebral ischemia.

  8. Stimulus-response functions of single avian olfactory bulb neurones.

    Science.gov (United States)

    McKeegan, Dorothy E F; Demmers, Theodorus G M; Wathes, Christopher M; Jones, R Bryan; Gentle, Michael J

    2002-10-25

    This study investigated olfactory processing in a functional context by examining the responses of single avian olfactory bulb neurones to two biologically important gases over relevant concentration ranges. Recordings of extracellular spike activity were made from 80 single units in the left olfactory bulb of 11 anaesthetised, freely breathing adult hens (Gallus domesticus). The units were spontaneously active, exhibiting widely variable firing rates (0.07-47.28 spikes/s) and variable temporal firing patterns. Single units were tested for their response to an ascending concentration series of either ammonia (2.5-100 ppm) or hydrogen sulphide (1-50 ppm), delivered directly to the olfactory epithelium. Stimulation with a calibrated gas delivery system resulted in modification of spontaneous activity causing either inhibition (47% of units) or excitation (53%) of firing. For ammonia, 20 of the 35 units tested exhibited a response, while for hydrogen sulphide, 25 of the 45 units tested were responsive. Approximate response thresholds for ammonia (median threshold 3.75 ppm (range 2.5-60 ppm, n=20)) and hydrogen sulphide (median threshold 1 ppm (range 1-10 ppm, n=25)) were determined with most units exhibiting thresholds near the lower end of these ranges. Stimulus response curves were constructed for 23 units; 16 (the most complete) were subjected to a linear regression analysis to determine whether they were best fitted by a linear, log or power function. No single function provided the best fit for all the curves (seven were linear, eight were log, one was power). These findings show that avian units respond to changes in stimulus concentration in a manner generally consistent with reported responses in mammalian olfactory bulb neurones. However, this study illustrates a level of fine-tuning to small step changes in concentration (<5 ppm) not previously demonstrated in vertebrate single olfactory bulb neurones.

  9. Molecular hierarchy in neurons differentiated from mouse ES cells containing a single human chromosome 21.

    Science.gov (United States)

    Wang, Chi Chiu; Kadota, Mitsutaka; Nishigaki, Ryuichi; Kazuki, Yasuhiro; Shirayoshi, Yasuaki; Rogers, Michael Scott; Gojobori, Takashi; Ikeo, Kazuho; Oshimura, Mitsuo

    2004-02-06

    Defects in neurogenesis and neuronal differentiation in the fetal brain of Down syndrome (DS) patients lead to the apparent neuropathological abnormalities and contribute to the phenotypic characters of mental retardation, and premature development of Alzheimer's disease, those being the most common phenotype in DS. In order to understand the molecular mechanism underlying the cause of phenotypic abnormalities in the DS brain, we have utilized an in vitro model of TT2F mouse embryonic stem cells containing a single human chromosome 21 (hChr21) to study neuron development and neuronal differentiation by microarray containing 15K developmentally expressed cDNAs. Defective neuronal differentiation in the presence of extra hChr21 manifested primarily the post-transcriptional and translational modification, such as Mrpl10, SNAPC3, Srprb, SF3a60 in the early neuronal stem cell stage, and Mrps18a, Eef1g, and Ubce8 in the late differentiated stage. Hierarchical clustering patterned specific expression of hChr21 gene dosage effects on neuron outgrowth, migration, and differentiation, such as Syngr2, Dncic2, Eif3sf, and Peg3.

  10. Stochastic neuron models

    CERN Document Server

    Greenwood, Priscilla E

    2016-01-01

    This book describes a large number of open problems in the theory of stochastic neural systems, with the aim of enticing probabilists to work on them. This includes problems arising from stochastic models of individual neurons as well as those arising from stochastic models of the activities of small and large networks of interconnected neurons. The necessary neuroscience background to these problems is outlined within the text, so readers can grasp the context in which they arise. This book will be useful for graduate students and instructors providing material and references for applying probability to stochastic neuron modeling. Methods and results are presented, but the emphasis is on questions where additional stochastic analysis may contribute neuroscience insight. An extensive bibliography is included. Dr. Priscilla E. Greenwood is a Professor Emerita in the Department of Mathematics at the University of British Columbia. Dr. Lawrence M. Ward is a Professor in the Department of Psychology and the Brain...

  11. Single-Cell Gene Expression Analysis of Cholinergic Neurons in the Arcuate Nucleus of the Hypothalamus.

    Directory of Open Access Journals (Sweden)

    Jae Hoon Jeong

    Full Text Available The cholinoceptive system in the hypothalamus, in particular in the arcuate nucleus (ARC, plays a role in regulating food intake. Neurons in the ARC contain multiple neuropeptides, amines, and neurotransmitters. To study molecular and neurochemical heterogeneity of ARC neurons, we combine single-cell qRT-PCR and single-cell whole transcriptome amplification methods to analyze expression patterns of our hand-picked 60 genes in individual neurons in the ARC. Immunohistochemical and single-cell qRT-PCR analyses show choline acetyltransferase (ChAT-expressing neurons in the ARC. Gene expression patterns are remarkably distinct in each individual cholinergic neuron. Two-thirds of cholinergic neurons express tyrosine hydroxylase (Th mRNA. A large subset of these Th-positive cholinergic neurons is GABAergic as they express the GABA synthesizing enzyme glutamate decarboxylase and vesicular GABA transporter transcripts. Some cholinergic neurons also express the vesicular glutamate transporter transcript gene. POMC and POMC-processing enzyme transcripts are found in a subpopulation of cholinergic neurons. Despite this heterogeneity, gene expression patterns in individual cholinergic cells appear to be highly regulated in a cell-specific manner. In fact, membrane receptor transcripts are clustered with their respective intracellular signaling and downstream targets. This novel population of cholinergic neurons may be part of the neural circuitries that detect homeostatic need for food and control the drive to eat.

  12. Probing the dynamics of identified neurons with a data-driven modeling approach.

    Directory of Open Access Journals (Sweden)

    Thomas Nowotny

    2008-07-01

    Full Text Available In controlling animal behavior the nervous system has to perform within the operational limits set by the requirements of each specific behavior. The implications for the corresponding range of suitable network, single neuron, and ion channel properties have remained elusive. In this article we approach the question of how well-constrained properties of neuronal systems may be on the neuronal level. We used large data sets of the activity of isolated invertebrate identified cells and built an accurate conductance-based model for this cell type using customized automated parameter estimation techniques. By direct inspection of the data we found that the variability of the neurons is larger when they are isolated from the circuit than when in the intact system. Furthermore, the responses of the neurons to perturbations appear to be more consistent than their autonomous behavior under stationary conditions. In the developed model, the constraints on different parameters that enforce appropriate model dynamics vary widely from some very tightly controlled parameters to others that are almost arbitrary. The model also allows predictions for the effect of blocking selected ionic currents and to prove that the origin of irregular dynamics in the neuron model is proper chaoticity and that this chaoticity is typical in an appropriate sense. Our results indicate that data driven models are useful tools for the in-depth analysis of neuronal dynamics. The better consistency of responses to perturbations, in the real neurons as well as in the model, suggests a paradigm shift away from measuring autonomous dynamics alone towards protocols of controlled perturbations. Our predictions for the impact of channel blockers on the neuronal dynamics and the proof of chaoticity underscore the wide scope of our approach.

  13. Noise and Synchronization Analysis of the Cold-Receptor Neuronal Network Model

    Directory of Open Access Journals (Sweden)

    Ying Du

    2014-01-01

    Full Text Available This paper analyzes the dynamics of the cold receptor neural network model. First, it examines noise effects on neuronal stimulus in the model. From ISI plots, it is shown that there are considerable differences between purely deterministic simulations and noisy ones. The ISI-distance is used to measure the noise effects on spike trains quantitatively. It is found that spike trains observed in neural models can be more strongly affected by noise for different temperatures in some aspects; meanwhile, spike train has greater variability with the noise intensity increasing. The synchronization of neuronal network with different connectivity patterns is also studied. It is shown that chaotic and high period patterns are more difficult to get complete synchronization than the situation in single spike and low period patterns. The neuronal network will exhibit various patterns of firing synchronization by varying some key parameters such as the coupling strength. Different types of firing synchronization are diagnosed by a correlation coefficient and the ISI-distance method. The simulations show that the synchronization status of neurons is related to the network connectivity patterns.

  14. Two-population model for medial temporal lobe neurons: The vast majority are almost silent.

    Science.gov (United States)

    Magyar, Andrew; Collins, John

    2015-07-01

    Recordings in the human medial temporal lobe have found many neurons that respond to pictures (and related stimuli) of just one particular person of those presented. It has been proposed that these are concept cells, responding to just a single concept. However, a direct experimental test of the concept cell idea appears impossible, because it would need the measurement of the response of each cell to enormous numbers of other stimuli. Here we propose a new statistical method for analysis of the data that gives a more powerful way to analyze how close data are to the concept-cell idea. Central to the model is the neuronal sparsity, defined as the total fraction of stimuli that elicit an above-threshold response in the neuron. The model exploits the large number of sampled neurons to give sensitivity to situations where the average response sparsity is much less than one response for the number of presented stimuli. We show that a conventional model where a single sparsity is postulated for all neurons gives an extremely poor fit to the data. In contrast, a model with two dramatically different populations gives an excellent fit to data from the hippocampus and entorhinal cortex. In the hippocampus, one population has 7% of the cells with a 2.6% sparsity. But a much larger fraction (93%) respond to only 0.1% of the stimuli. This can result in an extreme bias in the responsiveness of reported neurons compared with a typical neuron. Finally, we show how to allow for the fact that some identified units correspond to multiple neurons and find that our conclusions at the neural level are quantitatively changed but strengthened, with an even stronger difference between the two populations.

  15. Variations in interpulse interval of double action potentials during propagation in single neurons.

    Science.gov (United States)

    Villagran-Vargas, Edgar; Rodríguez-Sosa, Leonardo; Hustert, Reinhold; Blicher, Andreas; Laub, Katrine; Heimburg, Thomas

    2013-02-01

    In this work, we analyzed the interpulse interval (IPI) of doublets and triplets in single neurons of three biological models. Pulse trains with two or three spikes originate from the process of sensory mechanotransduction in neurons of the locust femoral nerve, as well as through spontaneous activity both in the abdominal motor neurons and the caudal photoreceptor of the crayfish. We show that the IPI for successive low-frequency single action potentials, as recorded with two electrodes at two different points along a nerve axon, remains constant. On the other hand, IPI in doublets either remains constant, increases or decreases by up to about 3 ms as the pair propagates. When IPI increases, the succeeding pulse travels at a slower speed than the preceding one. When IPI is reduced, the succeeding pulse travels faster than the preceding one and may exceed the normal value for the specific neuron. In both cases, IPI increase and reduction, the speed of the preceding pulse differs slightly from the normal value, therefore the two pulses travel at different speeds in the same nerve axon. On the basis of our results, we may state that the effect of attraction or repulsion in doublets suggests a tendency of the spikes to reach a stable configuration. We strongly suggest that the change in IPI during spike propagation of doublets opens up a whole new realm of possibilities for neural coding and may have major implications for understanding information processing in nervous systems. Copyright © 2012 Wiley Periodicals, Inc.

  16. Towards a general theory of neural computation based on prediction by single neurons.

    Directory of Open Access Journals (Sweden)

    Christopher D Fiorillo

    Full Text Available Although there has been tremendous progress in understanding the mechanics of the nervous system, there has not been a general theory of its computational function. Here I present a theory that relates the established biophysical properties of single generic neurons to principles of Bayesian probability theory, reinforcement learning and efficient coding. I suggest that this theory addresses the general computational problem facing the nervous system. Each neuron is proposed to mirror the function of the whole system in learning to predict aspects of the world related to future reward. According to the model, a typical neuron receives current information about the state of the world from a subset of its excitatory synaptic inputs, and prior information from its other inputs. Prior information would be contributed by synaptic inputs representing distinct regions of space, and by different types of non-synaptic, voltage-regulated channels representing distinct periods of the past. The neuron's membrane voltage is proposed to signal the difference between current and prior information ("prediction error" or "surprise". A neuron would apply a Hebbian plasticity rule to select those excitatory inputs that are the most closely correlated with reward but are the least predictable, since unpredictable inputs provide the neuron with the most "new" information about future reward. To minimize the error in its predictions and to respond only when excitation is "new and surprising," the neuron selects amongst its prior information sources through an anti-Hebbian rule. The unique inputs of a mature neuron would therefore result from learning about spatial and temporal patterns in its local environment, and by extension, the external world. Thus the theory describes how the structure of the mature nervous system could reflect the structure of the external world, and how the complexity and intelligence of the system might develop from a population of

  17. Spider Silk as Guiding Biomaterial for Human Model Neurons

    Directory of Open Access Journals (Sweden)

    Frank Roloff

    2014-01-01

    Full Text Available Over the last years, a number of therapeutic strategies have emerged to promote axonal regeneration. An attractive strategy is the implantation of biodegradable and nonimmunogenic artificial scaffolds into injured peripheral nerves. In previous studies, transplantation of decellularized veins filled with spider silk for bridging critical size nerve defects resulted in axonal regeneration and remyelination by invading endogenous Schwann cells. Detailed interaction of elongating neurons and the spider silk as guidance material is unknown. To visualize direct cellular interactions between spider silk and neurons in vitro, we developed an in vitro crossed silk fiber array. Here, we describe in detail for the first time that human (NT2 model neurons attach to silk scaffolds. Extending neurites can bridge gaps between single silk fibers and elongate afterwards on the neighboring fiber. Culturing human neurons on the silk arrays led to an increasing migration and adhesion of neuronal cell bodies to the spider silk fibers. Within three to four weeks, clustered somata and extending neurites formed ganglion-like cell structures. Microscopic imaging of human neurons on the crossed fiber arrays in vitro will allow for a more efficient development of methods to maximize cell adhesion and neurite growth on spider silk prior to transplantation studies.

  18. Automated quantification of neuronal networks and single-cell calcium dynamics using calcium imaging.

    Science.gov (United States)

    Patel, Tapan P; Man, Karen; Firestein, Bonnie L; Meaney, David F

    2015-03-30

    Recent advances in genetically engineered calcium and membrane potential indicators provide the potential to estimate the activation dynamics of individual neurons within larger, mesoscale networks (100s-1000+neurons). However, a fully integrated automated workflow for the analysis and visualization of neural microcircuits from high speed fluorescence imaging data is lacking. Here we introduce FluoroSNNAP, Fluorescence Single Neuron and Network Analysis Package. FluoroSNNAP is an open-source, interactive software developed in MATLAB for automated quantification of numerous biologically relevant features of both the calcium dynamics of single-cells and network activity patterns. FluoroSNNAP integrates and improves upon existing tools for spike detection, synchronization analysis, and inference of functional connectivity, making it most useful to experimentalists with little or no programming knowledge. We apply FluoroSNNAP to characterize the activity patterns of neuronal microcircuits undergoing developmental maturation in vitro. Separately, we highlight the utility of single-cell analysis for phenotyping a mixed population of neurons expressing a human mutant variant of the microtubule associated protein tau and wild-type tau. We show the performance of semi-automated cell segmentation using spatiotemporal independent component analysis and significant improvement in detecting calcium transients using a template-based algorithm in comparison to peak-based or wavelet-based detection methods. Our software further enables automated analysis of microcircuits, which is an improvement over existing methods. We expect the dissemination of this software will facilitate a comprehensive analysis of neuronal networks, promoting the rapid interrogation of circuits in health and disease. Copyright © 2015. Published by Elsevier B.V.

  19. Divisive normalization and neuronal oscillations in a single hierarchical framework of selective visual attention.

    Science.gov (United States)

    Montijn, Jorrit Steven; Klink, P Christaan; van Wezel, Richard J A

    2012-01-01

    Divisive normalization models of covert attention commonly use spike rate modulations as indicators of the effect of top-down attention. In addition, an increasing number of studies have shown that top-down attention increases the synchronization of neuronal oscillations as well, particularly in gamma-band frequencies (25-100 Hz). Although modulations of spike rate and synchronous oscillations are not mutually exclusive as mechanisms of attention, there has thus far been little effort to integrate these concepts into a single framework of attention. Here, we aim to provide such a unified framework by expanding the normalization model of attention with a multi-level hierarchical structure and a time dimension; allowing the simulation of a recently reported backward progression of attentional effects along the visual cortical hierarchy. A simple cascade of normalization models simulating different cortical areas is shown to cause signal degradation and a loss of stimulus discriminability over time. To negate this degradation and ensure stable neuronal stimulus representations, we incorporate a kind of oscillatory phase entrainment into our model that has previously been proposed as the "communication-through-coherence" (CTC) hypothesis. Our analysis shows that divisive normalization and oscillation models can complement each other in a unified account of the neural mechanisms of selective visual attention. The resulting hierarchical normalization and oscillation (HNO) model reproduces several additional spatial and temporal aspects of attentional modulation and predicts a latency effect on neuronal responses as a result of cued attention.

  20. [Hardware Implementation of Numerical Simulation Function of Hodgkin-Huxley Model Neurons Action Potential Based on Field Programmable Gate Array].

    Science.gov (United States)

    Wang, Jinlong; Lu, Mai; Hu, Yanwen; Chen, Xiaoqiang; Pan, Qiangqiang

    2015-12-01

    Neuron is the basic unit of the biological neural system. The Hodgkin-Huxley (HH) model is one of the most realistic neuron models on the electrophysiological characteristic description of neuron. Hardware implementation of neuron could provide new research ideas to clinical treatment of spinal cord injury, bionics and artificial intelligence. Based on the HH model neuron and the DSP Builder technology, in the present study, a single HH model neuron hardware implementation was completed in Field Programmable Gate Array (FPGA). The neuron implemented in FPGA was stimulated by different types of current, the action potential response characteristics were analyzed, and the correlation coefficient between numerical simulation result and hardware implementation result were calculated. The results showed that neuronal action potential response of FPGA was highly consistent with numerical simulation result. This work lays the foundation for hardware implementation of neural network.

  1. Fitting neuron models to spike trains

    Directory of Open Access Journals (Sweden)

    Cyrille eRossant

    2011-02-01

    Full Text Available Computational modeling is increasingly used to understand the function of neural circuitsin systems neuroscience.These studies require models of individual neurons with realisticinput-output properties.Recently, it was found that spiking models can accurately predict theprecisely timed spike trains produced by cortical neurons in response tosomatically injected currents,if properly fitted. This requires fitting techniques that are efficientand flexible enough to easily test different candidate models.We present a generic solution, based on the Brian simulator(a neural network simulator in Python, which allowsthe user to define and fit arbitrary neuron models to electrophysiological recordings.It relies on vectorization and parallel computing techniques toachieve efficiency.We demonstrate its use on neural recordings in the barrel cortex andin the auditory brainstem, and confirm that simple adaptive spiking modelscan accurately predict the response of cortical neurons. Finally, we show how a complexmulticompartmental model can be reduced to a simple effective spiking model.

  2. Single-cell imaging of bioenergetic responses to neuronal excitotoxicity and oxygen and glucose deprivation.

    Science.gov (United States)

    Connolly, Niamh M C; Düssmann, Heiko; Anilkumar, Ujval; Huber, Heinrich J; Prehn, Jochen H M

    2014-07-30

    Excitotoxicity is a condition occurring during cerebral ischemia, seizures, and chronic neurodegeneration. It is characterized by overactivation of glutamate receptors, leading to excessive Ca(2+)/Na(+) influx into neurons, energetic stress, and subsequent neuronal injury. We and others have previously investigated neuronal populations to study how bioenergetic parameters determine neuronal injury; however, such experiments are often confounded by population-based heterogeneity and the contribution of effects of non-neuronal cells. Hence, we here characterized bioenergetics during transient excitotoxicity in rat and mouse primary neurons at the single-cell level using fluorescent sensors for intracellular glucose, ATP, and activation of the energy sensor AMP-activated protein kinase (AMPK). We identified ATP depletion and recovery to energetic homeostasis, along with AMPK activation, as surprisingly rapid and plastic responses in two excitotoxic injury paradigms. We observed rapid recovery of neuronal ATP levels also in the absence of extracellular glucose, or when glycolytic ATP production was inhibited, but found mitochondria to be critical for fast and complete energetic recovery. Using an injury model of oxygen and glucose deprivation, we identified a similarly rapid bioenergetics response, yet with incomplete ATP recovery and decreased AMPK activity. Interestingly, excitotoxicity also induced an accumulation of intracellular glucose, providing an additional source of energy during and after excitotoxicity-induced energy depletion. We identified this to originate from extracellular, AMPK-dependent glucose uptake and from intracellular glucose mobilization. Surprisingly, cells recovering their elevated glucose levels faster to baseline survived longer, indicating that the plasticity of neurons to adapt to bioenergetic challenges is a key indicator of neuronal viability. Copyright © 2014 the authors 0270-6474/14/3410192-14$15.00/0.

  3. Behavioral and Single-Neuron Sensitivity to Millisecond Variations in Temporally Patterned Communication Signals.

    Science.gov (United States)

    Baker, Christa A; Ma, Lisa; Casareale, Chelsea R; Carlson, Bruce A

    2016-08-24

    In many sensory pathways, central neurons serve as temporal filters for timing patterns in communication signals. However, how a population of neurons with diverse temporal filtering properties codes for natural variation in communication signals is unknown. Here we addressed this question in the weakly electric fish Brienomyrus brachyistius, which varies the time intervals between successive electric organ discharges to communicate. These fish produce an individually stereotyped signal called a scallop, which consists of a distinctive temporal pattern of ∼8-12 electric pulses. We manipulated the temporal structure of natural scallops during behavioral playback and in vivo electrophysiology experiments to probe the temporal sensitivity of scallop encoding and recognition. We found that presenting time-reversed, randomized, or jittered scallops increased behavioral response thresholds, demonstrating that fish's electric signaling behavior was sensitive to the precise temporal structure of scallops. Next, using in vivo intracellular recordings and discriminant function analysis, we found that the responses of interval-selective midbrain neurons were also sensitive to the precise temporal structure of scallops. Subthreshold changes in membrane potential recorded from single neurons discriminated natural scallops from time-reversed, randomized, and jittered sequences. Pooling the responses of multiple neurons improved the discriminability of natural sequences from temporally manipulated sequences. Finally, we found that single-neuron responses were sensitive to interindividual variation in scallop sequences, raising the question of whether fish may analyze scallop structure to gain information about the sender. Collectively, these results demonstrate that a population of interval-selective neurons can encode behaviorally relevant temporal patterns with millisecond precision. The timing patterns of action potentials, or spikes, play important roles in representing

  4. C. elegans model of neuronal aging

    OpenAIRE

    Peng, Chiu-Ying; Chen, Chun-Hao; Hsu, Jiun-Min; Pan, Chun-Liang

    2011-01-01

    Aging of the nervous system underlies the behavioral and cognitive decline associated with senescence. Understanding the molecular and cellular basis of neuronal aging will therefore contribute to the development of effective treatments for aging and age-associated neurodegenerative disorders. Despite this pressing need, there are surprisingly few animal models that aim at recapitulating neuronal aging in a physiological context. We recently developed a C. elegans model of neuronal aging, and...

  5. Characterizing human stem cell-derived sensory neurons at the single-cell level reveals their ion channel expression and utility in pain research.

    Science.gov (United States)

    Young, Gareth T; Gutteridge, Alex; Fox, Heather DE; Wilbrey, Anna L; Cao, Lishuang; Cho, Lily T; Brown, Adam R; Benn, Caroline L; Kammonen, Laura R; Friedman, Julia H; Bictash, Magda; Whiting, Paul; Bilsland, James G; Stevens, Edward B

    2014-08-01

    The generation of human sensory neurons by directed differentiation of pluripotent stem cells opens new opportunities for investigating the biology of pain. The inability to generate this cell type has meant that up until now their study has been reliant on the use of rodent models. Here, we use a combination of population and single-cell techniques to perform a detailed molecular, electrophysiological, and pharmacological phenotyping of sensory neurons derived from human embryonic stem cells. We describe the evolution of cell populations over 6 weeks of directed differentiation; a process that results in the generation of a largely homogeneous population of neurons that are both molecularly and functionally comparable to human sensory neurons derived from mature dorsal root ganglia. This work opens the prospect of using pluripotent stem-cell-derived sensory neurons to study human neuronal physiology and as in vitro models for drug discovery in pain and sensory disorders.

  6. Implementation of an integrated op-amp based chaotic neuron model and observation of its chaotic dynamics

    International Nuclear Information System (INIS)

    Jung, Jinwoo; Lee, Jewon; Song, Hanjung

    2011-01-01

    This paper presents a fully integrated circuit implementation of an operational amplifier (op-amp) based chaotic neuron model with a bipolar output function, experimental measurements, and analyses of its chaotic behavior. The proposed chaotic neuron model integrated circuit consists of several op-amps, sample and hold circuits, a nonlinear function block for chaotic signal generation, a clock generator, a nonlinear output function, etc. Based on the HSPICE (circuit program) simulation results, approximated empirical equations for analyses were formulated. Then, the chaotic dynamical responses such as bifurcation diagrams, time series, and Lyapunov exponent were calculated using these empirical equations. In addition, we performed simulations about two chaotic neuron systems with four synapses to confirm neural network connections and got normal behavior of the chaotic neuron such as internal state bifurcation diagram according to the synaptic weight variation. The proposed circuit was fabricated using a 0.8-μm single poly complementary metal-oxide semiconductor technology. Measurements of the fabricated single chaotic neuron with ±2.5 V power supplies and a 10 kHz sampling clock frequency were carried out and compared with the simulated results.

  7. On the stability and dynamics of stochastic spiking neuron models: Nonlinear Hawkes process and point process GLMs.

    Science.gov (United States)

    Gerhard, Felipe; Deger, Moritz; Truccolo, Wilson

    2017-02-01

    Point process generalized linear models (PP-GLMs) provide an important statistical framework for modeling spiking activity in single-neurons and neuronal networks. Stochastic stability is essential when sampling from these models, as done in computational neuroscience to analyze statistical properties of neuronal dynamics and in neuro-engineering to implement closed-loop applications. Here we show, however, that despite passing common goodness-of-fit tests, PP-GLMs estimated from data are often unstable, leading to divergent firing rates. The inclusion of absolute refractory periods is not a satisfactory solution since the activity then typically settles into unphysiological rates. To address these issues, we derive a framework for determining the existence and stability of fixed points of the expected conditional intensity function (CIF) for general PP-GLMs. Specifically, in nonlinear Hawkes PP-GLMs, the CIF is expressed as a function of the previous spike history and exogenous inputs. We use a mean-field quasi-renewal (QR) approximation that decomposes spike history effects into the contribution of the last spike and an average of the CIF over all spike histories prior to the last spike. Fixed points for stationary rates are derived as self-consistent solutions of integral equations. Bifurcation analysis and the number of fixed points predict that the original models can show stable, divergent, and metastable (fragile) dynamics. For fragile models, fluctuations of the single-neuron dynamics predict expected divergence times after which rates approach unphysiologically high values. This metric can be used to estimate the probability of rates to remain physiological for given time periods, e.g., for simulation purposes. We demonstrate the use of the stability framework using simulated single-neuron examples and neurophysiological recordings. Finally, we show how to adapt PP-GLM estimation procedures to guarantee model stability. Overall, our results provide a

  8. Functionalized anatomical models for EM-neuron Interaction modeling

    Science.gov (United States)

    Neufeld, Esra; Cassará, Antonino Mario; Montanaro, Hazael; Kuster, Niels; Kainz, Wolfgang

    2016-06-01

    The understanding of interactions between electromagnetic (EM) fields and nerves are crucial in contexts ranging from therapeutic neurostimulation to low frequency EM exposure safety. To properly consider the impact of in vivo induced field inhomogeneity on non-linear neuronal dynamics, coupled EM-neuronal dynamics modeling is required. For that purpose, novel functionalized computable human phantoms have been developed. Their implementation and the systematic verification of the integrated anisotropic quasi-static EM solver and neuronal dynamics modeling functionality, based on the method of manufactured solutions and numerical reference data, is described. Electric and magnetic stimulation of the ulnar and sciatic nerve were modeled to help understanding a range of controversial issues related to the magnitude and optimal determination of strength-duration (SD) time constants. The results indicate the importance of considering the stimulation-specific inhomogeneous field distributions (especially at tissue interfaces), realistic models of non-linear neuronal dynamics, very short pulses, and suitable SD extrapolation models. These results and the functionalized computable phantom will influence and support the development of safe and effective neuroprosthetic devices and novel electroceuticals. Furthermore they will assist the evaluation of existing low frequency exposure standards for the entire population under all exposure conditions.

  9. Functional adaptation to loading of a single bone is neuronally regulated and involves multiple bones.

    Science.gov (United States)

    Sample, Susannah J; Behan, Mary; Smith, Lesley; Oldenhoff, William E; Markel, Mark D; Kalscheur, Vicki L; Hao, Zhengling; Miletic, Vjekoslav; Muir, Peter

    2008-09-01

    Regulation of load-induced bone formation is considered a local phenomenon controlled by osteocytes, although it has also been hypothesized that functional adaptation may be neuronally regulated. The aim of this study was to examine bone formation in multiple bones, in response to loading of a single bone, and to determine whether adaptation may be neuronally regulated. Load-induced responses in the left and right ulnas and humeri were determined after loading of the right ulna in male Sprague-Dawley rats (69 +/- 16 days of age). After a single period of loading at -760-, -2000-, or -3750-microepsilon initial peak strain, rats were given calcein to label new bone formation. Bone formation and bone neuropeptide concentrations were determined at 10 days. In one group, temporary neuronal blocking was achieved by perineural anesthesia of the brachial plexus with bupivicaine during loading. We found right ulna loading induces adaptive responses in other bones in both thoracic limbs compared with Sham controls and that neuronal blocking during loading abrogated bone formation in the loaded ulna and other thoracic limb bones. Skeletal adaptation was more evident in distal long bones compared with proximal long bones. We also found that the single period of loading modulated bone neuropeptide concentrations persistently for 10 days. We conclude that functional adaptation to loading of a single bone in young rapidly growing rats is neuronally regulated and involves multiple bones. Persistent changes in bone neuropeptide concentrations after a single loading period suggest that plasticity exists in the innervation of bone.

  10. Single or multiple synchronization transitions in scale-free neuronal networks with electrical or chemical coupling

    International Nuclear Information System (INIS)

    Hao Yinghang; Gong, Yubing; Wang Li; Ma Xiaoguang; Yang Chuanlu

    2011-01-01

    Research highlights: → Single synchronization transition for gap-junctional coupling. → Multiple synchronization transitions for chemical synaptic coupling. → Gap junctions and chemical synapses have different impacts on synchronization transition. → Chemical synapses may play a dominant role in neurons' information processing. - Abstract: In this paper, we have studied time delay- and coupling strength-induced synchronization transitions in scale-free modified Hodgkin-Huxley (MHH) neuron networks with gap-junctions and chemical synaptic coupling. It is shown that the synchronization transitions are much different for these two coupling types. For gap-junctions, the neurons exhibit a single synchronization transition with time delay and coupling strength, while for chemical synapses, there are multiple synchronization transitions with time delay, and the synchronization transition with coupling strength is dependent on the time delay lengths. For short delays we observe a single synchronization transition, whereas for long delays the neurons exhibit multiple synchronization transitions as the coupling strength is varied. These results show that gap junctions and chemical synapses have different impacts on the pattern formation and synchronization transitions of the scale-free MHH neuronal networks, and chemical synapses, compared to gap junctions, may play a dominant and more active function in the firing activity of the networks. These findings would be helpful for further understanding the roles of gap junctions and chemical synapses in the firing dynamics of neuronal networks.

  11. Single or multiple synchronization transitions in scale-free neuronal networks with electrical or chemical coupling

    Energy Technology Data Exchange (ETDEWEB)

    Hao Yinghang [School of Physics, Ludong University, Yantai 264025 (China); Gong, Yubing, E-mail: gongyubing09@hotmail.co [School of Physics, Ludong University, Yantai 264025 (China); Wang Li; Ma Xiaoguang; Yang Chuanlu [School of Physics, Ludong University, Yantai 264025 (China)

    2011-04-15

    Research highlights: Single synchronization transition for gap-junctional coupling. Multiple synchronization transitions for chemical synaptic coupling. Gap junctions and chemical synapses have different impacts on synchronization transition. Chemical synapses may play a dominant role in neurons' information processing. - Abstract: In this paper, we have studied time delay- and coupling strength-induced synchronization transitions in scale-free modified Hodgkin-Huxley (MHH) neuron networks with gap-junctions and chemical synaptic coupling. It is shown that the synchronization transitions are much different for these two coupling types. For gap-junctions, the neurons exhibit a single synchronization transition with time delay and coupling strength, while for chemical synapses, there are multiple synchronization transitions with time delay, and the synchronization transition with coupling strength is dependent on the time delay lengths. For short delays we observe a single synchronization transition, whereas for long delays the neurons exhibit multiple synchronization transitions as the coupling strength is varied. These results show that gap junctions and chemical synapses have different impacts on the pattern formation and synchronization transitions of the scale-free MHH neuronal networks, and chemical synapses, compared to gap junctions, may play a dominant and more active function in the firing activity of the networks. These findings would be helpful for further understanding the roles of gap junctions and chemical synapses in the firing dynamics of neuronal networks.

  12. A model explaining synchronization of neuron bioelectric frequency under weak alternating low frequency magnetic field

    Energy Technology Data Exchange (ETDEWEB)

    Moral, A. del, E-mail: delmoral@unizar.es [Laboratorio de Magnetismo, Departamento de Física de Materia Condensada and Instituto de Ciencia de Materiales, Universidad de Zaragoza and Consejo Superior de Investigaciones Científicas, 50009 Zaragoza (Spain); Laboratorio de Magnetobiología, Departamento de Anatomía e Histología, Facultad de Medicina, Universidad de Zaragoza, 50009 Zaragoza (Spain); Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, 28223 Madrid (Spain); Azanza, María J., E-mail: mjazanza@unizar.es [Laboratorio de Magnetobiología, Departamento de Anatomía e Histología, Facultad de Medicina, Universidad de Zaragoza, 50009 Zaragoza (Spain); Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, 28223 Madrid (Spain)

    2015-03-01

    A biomagnetic-electrical model is presented that explains rather well the experimentally observed synchronization of the bioelectric potential firing rate (“frequency”), f, of single unit neurons of Helix aspersa mollusc under the application of extremely low frequency (ELF) weak alternating (AC) magnetic fields (MF). The proposed model incorporates to our widely experimentally tested model of superdiamagnetism (SD) and Ca{sup 2+} Coulomb explosion (CE) from lipid (LP) bilayer membrane (SD–CE model), the electrical quadrupolar long range interaction between the bilayer LP membranes of synchronized neuron pairs, not considered before. The quadrupolar interaction is capable of explaining well the observed synchronization. Actual extension of our SD–CE-model shows that the neuron firing frequency field, B, dependence becomes not modified, but the bioelectric frequency is decreased and its spontaneous temperature, T, dependence is modified. A comparison of the model with synchronization experimental results of pair of neurons under weak (B{sub 0}≅0.2–15 mT) AC-MF of frequency f{sub M}=50 Hz is reported. From the deduced size of synchronized LP clusters under B, is suggested the formation of small neuron networks via the membrane lipid correlation. - Highlights: • Neuron pair synchronization under low frequency alternating (AC) magnetic field (MF). • Superdiamagnetism and Ca{sup 2+} Coulomb explosion for AC MF effect in synchronized frequency. • Membrane lipid electrical quadrupolar pair interaction as synchronization mechamism. • Good agreement of model with electrophysiological experiments on mollusc Helix neurons.

  13. A model explaining synchronization of neuron bioelectric frequency under weak alternating low frequency magnetic field

    International Nuclear Information System (INIS)

    Moral, A. del; Azanza, María J.

    2015-01-01

    A biomagnetic-electrical model is presented that explains rather well the experimentally observed synchronization of the bioelectric potential firing rate (“frequency”), f, of single unit neurons of Helix aspersa mollusc under the application of extremely low frequency (ELF) weak alternating (AC) magnetic fields (MF). The proposed model incorporates to our widely experimentally tested model of superdiamagnetism (SD) and Ca 2+ Coulomb explosion (CE) from lipid (LP) bilayer membrane (SD–CE model), the electrical quadrupolar long range interaction between the bilayer LP membranes of synchronized neuron pairs, not considered before. The quadrupolar interaction is capable of explaining well the observed synchronization. Actual extension of our SD–CE-model shows that the neuron firing frequency field, B, dependence becomes not modified, but the bioelectric frequency is decreased and its spontaneous temperature, T, dependence is modified. A comparison of the model with synchronization experimental results of pair of neurons under weak (B 0 ≅0.2–15 mT) AC-MF of frequency f M =50 Hz is reported. From the deduced size of synchronized LP clusters under B, is suggested the formation of small neuron networks via the membrane lipid correlation. - Highlights: • Neuron pair synchronization under low frequency alternating (AC) magnetic field (MF). • Superdiamagnetism and Ca 2+ Coulomb explosion for AC MF effect in synchronized frequency. • Membrane lipid electrical quadrupolar pair interaction as synchronization mechamism. • Good agreement of model with electrophysiological experiments on mollusc Helix neurons

  14. Single-trial estimation of stimulus and spike-history effects on time-varying ensemble spiking activity of multiple neurons: a simulation study

    International Nuclear Information System (INIS)

    Shimazaki, Hideaki

    2013-01-01

    Neurons in cortical circuits exhibit coordinated spiking activity, and can produce correlated synchronous spikes during behavior and cognition. We recently developed a method for estimating the dynamics of correlated ensemble activity by combining a model of simultaneous neuronal interactions (e.g., a spin-glass model) with a state-space method (Shimazaki et al. 2012 PLoS Comput Biol 8 e1002385). This method allows us to estimate stimulus-evoked dynamics of neuronal interactions which is reproducible in repeated trials under identical experimental conditions. However, the method may not be suitable for detecting stimulus responses if the neuronal dynamics exhibits significant variability across trials. In addition, the previous model does not include effects of past spiking activity of the neurons on the current state of ensemble activity. In this study, we develop a parametric method for simultaneously estimating the stimulus and spike-history effects on the ensemble activity from single-trial data even if the neurons exhibit dynamics that is largely unrelated to these effects. For this goal, we model ensemble neuronal activity as a latent process and include the stimulus and spike-history effects as exogenous inputs to the latent process. We develop an expectation-maximization algorithm that simultaneously achieves estimation of the latent process, stimulus responses, and spike-history effects. The proposed method is useful to analyze an interaction of internal cortical states and sensory evoked activity

  15. Characterizing Human Stem Cell–derived Sensory Neurons at the Single-cell Level Reveals Their Ion Channel Expression and Utility in Pain Research

    Science.gov (United States)

    Young, Gareth T; Gutteridge, Alex; Fox, Heather DE; Wilbrey, Anna L; Cao, Lishuang; Cho, Lily T; Brown, Adam R; Benn, Caroline L; Kammonen, Laura R; Friedman, Julia H; Bictash, Magda; Whiting, Paul; Bilsland, James G; Stevens, Edward B

    2014-01-01

    The generation of human sensory neurons by directed differentiation of pluripotent stem cells opens new opportunities for investigating the biology of pain. The inability to generate this cell type has meant that up until now their study has been reliant on the use of rodent models. Here, we use a combination of population and single-cell techniques to perform a detailed molecular, electrophysiological, and pharmacological phenotyping of sensory neurons derived from human embryonic stem cells. We describe the evolution of cell populations over 6 weeks of directed differentiation; a process that results in the generation of a largely homogeneous population of neurons that are both molecularly and functionally comparable to human sensory neurons derived from mature dorsal root ganglia. This work opens the prospect of using pluripotent stem-cell–derived sensory neurons to study human neuronal physiology and as in vitro models for drug discovery in pain and sensory disorders. PMID:24832007

  16. Models of the stochastic activity of neurones

    CERN Document Server

    Holden, Arun Vivian

    1976-01-01

    These notes have grown from a series of seminars given at Leeds between 1972 and 1975. They represent an attempt to gather together the different kinds of model which have been proposed to account for the stochastic activity of neurones, and to provide an introduction to this area of mathematical biology. A striking feature of the electrical activity of the nervous system is that it appears stochastic: this is apparent at all levels of recording, ranging from intracellular recordings to the electroencephalogram. The chapters start with fluctuations in membrane potential, proceed through single unit and synaptic activity and end with the behaviour of large aggregates of neurones: L have chgaen this seque~~e\\/~~';uggest that the interesting behaviourr~f :the nervous system - its individuality, variability and dynamic forms - may in part result from the stochastic behaviour of its components. I would like to thank Dr. Julio Rubio for reading and commenting on the drafts, Mrs. Doris Beighton for producing the fin...

  17. Audio-vocal interaction in single neurons of the monkey ventrolateral prefrontal cortex.

    Science.gov (United States)

    Hage, Steffen R; Nieder, Andreas

    2015-05-06

    Complex audio-vocal integration systems depend on a strong interconnection between the auditory and the vocal motor system. To gain cognitive control over audio-vocal interaction during vocal motor control, the PFC needs to be involved. Neurons in the ventrolateral PFC (VLPFC) have been shown to separately encode the sensory perceptions and motor production of vocalizations. It is unknown, however, whether single neurons in the PFC reflect audio-vocal interactions. We therefore recorded single-unit activity in the VLPFC of rhesus monkeys (Macaca mulatta) while they produced vocalizations on command or passively listened to monkey calls. We found that 12% of randomly selected neurons in VLPFC modulated their discharge rate in response to acoustic stimulation with species-specific calls. Almost three-fourths of these auditory neurons showed an additional modulation of their discharge rates either before and/or during the monkeys' motor production of vocalization. Based on these audio-vocal interactions, the VLPFC might be well positioned to combine higher order auditory processing with cognitive control of the vocal motor output. Such audio-vocal integration processes in the VLPFC might constitute a precursor for the evolution of complex learned audio-vocal integration systems, ultimately giving rise to human speech. Copyright © 2015 the authors 0270-6474/15/357030-11$15.00/0.

  18. Single-cell Transcriptional Analysis Reveals Novel Neuronal Phenotypes and Interaction Networks involved In the Central Circadian Clock

    Directory of Open Access Journals (Sweden)

    James Park

    2016-10-01

    Full Text Available Single-cell heterogeneity confounds efforts to understand how a population of cells organizes into cellular networks that underlie tissue-level function. This complexity is prominent in the mammalian suprachiasmatic nucleus (SCN. Here, individual neurons exhibit a remarkable amount of asynchronous behavior and transcriptional heterogeneity. However, SCN neurons are able to generate precisely coordinated synaptic and molecular outputs that synchronize the body to a common circadian cycle by organizing into cellular networks. To understand this emergent cellular network property, it is important to reconcile single-neuron heterogeneity with network organization. In light of recent studies suggesting that transcriptionally heterogeneous cells organize into distinct cellular phenotypes, we characterized the transcriptional, spatial, and functional organization of 352 SCN neurons from mice experiencing phase-shifts in their circadian cycle. Using the community structure detection method and multivariate analytical techniques, we identified previously undescribed neuronal phenotypes that are likely to participate in regulatory networks with known SCN cell types. Based on the newly discovered neuronal phenotypes, we developed a data-driven neuronal network structure in which multiple cell types interact through known synaptic and paracrine signaling mechanisms. These results provide a basis from which to interpret the functional variability of SCN neurons and describe methodologies towards understanding how a population of heterogeneous single cells organizes into cellular networks that underlie tissue-level function.

  19. Responses of single neurons and neuronal ensembles in frog first- and second-order olfactory neurons

    Czech Academy of Sciences Publication Activity Database

    Rospars, J. P.; Šanda, Pavel; Lánský, Petr; Duchamp-Viret, P.

    2013-01-01

    Roč. 1536, NOV 6 (2013), s. 144-158 ISSN 0006-8993 R&D Projects: GA ČR(CZ) GBP304/12/G069; GA ČR(CZ) GAP103/11/0282 Institutional support: RVO:67985823 Keywords : olfaction * spiking activity * neuronal model Subject RIV: JD - Computer Applications, Robotics Impact factor: 2.828, year: 2013

  20. Task-dependent changes in cross-level coupling between single neurons and oscillatory activity in multiscale networks.

    Directory of Open Access Journals (Sweden)

    Ryan T Canolty

    Full Text Available Understanding the principles governing the dynamic coordination of functional brain networks remains an important unmet goal within neuroscience. How do distributed ensembles of neurons transiently coordinate their activity across a variety of spatial and temporal scales? While a complete mechanistic account of this process remains elusive, evidence suggests that neuronal oscillations may play a key role in this process, with different rhythms influencing both local computation and long-range communication. To investigate this question, we recorded multiple single unit and local field potential (LFP activity from microelectrode arrays implanted bilaterally in macaque motor areas. Monkeys performed a delayed center-out reach task either manually using their natural arm (Manual Control, MC or under direct neural control through a brain-machine interface (Brain Control, BC. In accord with prior work, we found that the spiking activity of individual neurons is coupled to multiple aspects of the ongoing motor beta rhythm (10-45 Hz during both MC and BC, with neurons exhibiting a diversity of coupling preferences. However, here we show that for identified single neurons, this beta-to-rate mapping can change in a reversible and task-dependent way. For example, as beta power increases, a given neuron may increase spiking during MC but decrease spiking during BC, or exhibit a reversible shift in the preferred phase of firing. The within-task stability of coupling, combined with the reversible cross-task changes in coupling, suggest that task-dependent changes in the beta-to-rate mapping play a role in the transient functional reorganization of neural ensembles. We characterize the range of task-dependent changes in the mapping from beta amplitude, phase, and inter-hemispheric phase differences to the spike rates of an ensemble of simultaneously-recorded neurons, and discuss the potential implications that dynamic remapping from oscillatory activity to

  1. A minimal model for a slow pacemaking neuron

    International Nuclear Information System (INIS)

    Zakharov, D.G.; Kuznetsov, A.

    2012-01-01

    Highlights: ► We have constructed a phenomenological model for slow pacemaking neurons. ► The model implements a nonlinearity introduced by an ion-dependent current. ► The new nonlinear dependence allows for differentiating responses to various stimuli. ► We discuss implications of our results for a broad class of neurons. - Abstract: We have constructed a phenomenological model for slow pacemaking neurons. These are neurons that generate very regular periodic oscillations of the membrane potential. Many of these neurons also differentially respond to various types of stimulation. The model is based on FitzHugh–Nagumo (FHN) oscillator and implements a nonlinearity introduced by a current that depends on an ion concentration. The comparison with the original FHN oscillator has shown that the new nonlinear dependence allows for differentiating responses to various stimuli. We discuss implications of our results for a broad class of neurons.

  2. Reward-timing-dependent bidirectional modulation of cortical microcircuits during optical single-neuron operant conditioning.

    Science.gov (United States)

    Hira, Riichiro; Ohkubo, Fuki; Masamizu, Yoshito; Ohkura, Masamichi; Nakai, Junichi; Okada, Takashi; Matsuzaki, Masanori

    2014-11-24

    Animals rapidly adapt to environmental change. To reveal how cortical microcircuits are rapidly reorganized when an animal recognizes novel reward contingency, we conduct two-photon calcium imaging of layer 2/3 motor cortex neurons in mice and simultaneously reinforce the activity of a single cortical neuron with water delivery. Here we show that when the target neuron is not relevant to a pre-trained forelimb movement, the mouse increases the target neuron activity and the number of rewards delivered during 15-min operant conditioning without changing forelimb movement behaviour. The reinforcement bidirectionally modulates the activity of subsets of non-target neurons, independent of distance from the target neuron. The bidirectional modulation depends on the relative timing between the reward delivery and the neuronal activity, and is recreated by pairing reward delivery and photoactivation of a subset of neurons. Reward-timing-dependent bidirectional modulation may be one of the fundamental processes in microcircuit reorganization for rapid adaptation.

  3. 3D Reconstruction and Standardization of the Rat Vibrissal Cortex for Precise Registration of Single Neuron Morphology

    Science.gov (United States)

    Egger, Robert; Narayanan, Rajeevan T.; Helmstaedter, Moritz; de Kock, Christiaan P. J.; Oberlaender, Marcel

    2012-01-01

    The three-dimensional (3D) structure of neural circuits is commonly studied by reconstructing individual or small groups of neurons in separate preparations. Investigation of structural organization principles or quantification of dendritic and axonal innervation thus requires integration of many reconstructed morphologies into a common reference frame. Here we present a standardized 3D model of the rat vibrissal cortex and introduce an automated registration tool that allows for precise placement of single neuron reconstructions. We (1) developed an automated image processing pipeline to reconstruct 3D anatomical landmarks, i.e., the barrels in Layer 4, the pia and white matter surfaces and the blood vessel pattern from high-resolution images, (2) quantified these landmarks in 12 different rats, (3) generated an average 3D model of the vibrissal cortex and (4) used rigid transformations and stepwise linear scaling to register 94 neuron morphologies, reconstructed from in vivo stainings, to the standardized cortex model. We find that anatomical landmarks vary substantially across the vibrissal cortex within an individual rat. In contrast, the 3D layout of the entire vibrissal cortex remains remarkably preserved across animals. This allows for precise registration of individual neuron reconstructions with approximately 30 µm accuracy. Our approach could be used to reconstruct and standardize other anatomically defined brain areas and may ultimately lead to a precise digital reference atlas of the rat brain. PMID:23284282

  4. Single neurons in prefrontal cortex encode abstract rules.

    Science.gov (United States)

    Wallis, J D; Anderson, K C; Miller, E K

    2001-06-21

    The ability to abstract principles or rules from direct experience allows behaviour to extend beyond specific circumstances to general situations. For example, we learn the 'rules' for restaurant dining from specific experiences and can then apply them in new restaurants. The use of such rules is thought to depend on the prefrontal cortex (PFC) because its damage often results in difficulty in following rules. Here we explore its neural basis by recording from single neurons in the PFC of monkeys trained to use two abstract rules. They were required to indicate whether two successively presented pictures were the same or different depending on which rule was currently in effect. The monkeys performed this task with new pictures, thus showing that they had learned two general principles that could be applied to stimuli that they had not yet experienced. The most prevalent neuronal activity observed in the PFC reflected the coding of these abstract rules.

  5. Observability and synchronization of neuron models

    Science.gov (United States)

    Aguirre, Luis A.; Portes, Leonardo L.; Letellier, Christophe

    2017-10-01

    Observability is the property that enables recovering the state of a dynamical system from a reduced number of measured variables. In high-dimensional systems, it is therefore important to make sure that the variable recorded to perform the analysis conveys good observability of the system dynamics. The observability of a network of neuron models depends nontrivially on the observability of the node dynamics and on the topology of the network. The aim of this paper is twofold. First, to perform a study of observability using four well-known neuron models by computing three different observability coefficients. This not only clarifies observability properties of the models but also shows the limitations of applicability of each type of coefficients in the context of such models. Second, to study the emergence of phase synchronization in networks composed of neuron models. This is done performing multivariate singular spectrum analysis which, to the best of the authors' knowledge, has not been used in the context of networks of neuron models. It is shown that it is possible to detect phase synchronization: (i) without having to measure all the state variables, but only one (that provides greatest observability) from each node and (ii) without having to estimate the phase.

  6. [Establishment of oxygen and glucose deprive model of in vitro cultured hippocampal neuron and effect of ligustrazine on intracellular Ca+ level in model neurons].

    Science.gov (United States)

    Wan, Hai-tong; Wang, Yu; Yang, Jie-hong

    2007-03-01

    To establish the oxygen and glucose deprive (OGD) model in cultured hippocampal neuron and study the effect of ligustrazine on intracellular Ca2+ level in the model neurons. The OGD model was established in cultured hippocampal neuron, and the intracellular Ca2+ level in it was detected by laser scanning confocal microscope (LSCM). The OGD model was successfully established in cultured hippocampal neurons; the intracellular Ca2+ level in the OGD model group was significantly higher than that in the blank control group (P neuron, which could be antagonized by ligustrazine, indicating that ligustrazine has a protective effect on hippocampal neuron from hypoxic-ischemic injury.

  7. Plasticity of marrow mesenchymal stem cells from human first-trimester fetus: from single-cell clone to neuronal differentiation.

    Science.gov (United States)

    Zhang, Yihua; Shen, Wenzheng; Sun, Bingjie; Lv, Changrong; Dou, Zhongying

    2011-02-01

    Recent results have shown that bone marrow mesenchymal stem cells (BMSCs) from human first-trimester abortus (hfBMSCs) are closer to embryonic stem cells and perform greater telomerase activity and faster propagation than mid- and late-prophase fetal and adult BMSCs. However, no research has been done on the plasticity of hfBMSCs into neuronal cells using single-cell cloned strains without cell contamination. In this study, we isolated five single cells from hfBMSCs and obtained five single-cell cloned strains, and investigated their biological property and neuronal differentiation potential. We found that four of the five strains showed similar expression profile of surface antigen markers to hfBMSCs, and most of them differentiated into neuron-like cells expressing Nestin, Pax6, Sox1, β-III Tubulin, NF-L, and NSE under induction. One strain showed different expression profile of surface antigen markers from the four strains and hfBMSCs, and did not differentiate toward neuronal cells. We demonstrated for the first time that some of single-cell cloned strains from hfBMSCs can differentiate into nerve tissue-like cell clusters under induction in vitro, and that the plasticity of each single-cell cloned strain into neuronal cells is different.

  8. Six types of multistability in a neuronal model based on slow calcium current.

    Directory of Open Access Journals (Sweden)

    Tatiana Malashchenko

    Full Text Available BACKGROUND: Multistability of oscillatory and silent regimes is a ubiquitous phenomenon exhibited by excitable systems such as neurons and cardiac cells. Multistability can play functional roles in short-term memory and maintaining posture. It seems to pose an evolutionary advantage for neurons which are part of multifunctional Central Pattern Generators to possess multistability. The mechanisms supporting multistability of bursting regimes are not well understood or classified. METHODOLOGY/PRINCIPAL FINDINGS: Our study is focused on determining the bio-physical mechanisms underlying different types of co-existence of the oscillatory and silent regimes observed in a neuronal model. We develop a low-dimensional model typifying the dynamics of a single leech heart interneuron. We carry out a bifurcation analysis of the model and show that it possesses six different types of multistability of dynamical regimes. These types are the co-existence of 1 bursting and silence, 2 tonic spiking and silence, 3 tonic spiking and subthreshold oscillations, 4 bursting and subthreshold oscillations, 5 bursting, subthreshold oscillations and silence, and 6 bursting and tonic spiking. These first five types of multistability occur due to the presence of a separating regime that is either a saddle periodic orbit or a saddle equilibrium. We found that the parameter range wherein multistability is observed is limited by the parameter values at which the separating regimes emerge and terminate. CONCLUSIONS: We developed a neuronal model which exhibits a rich variety of different types of multistability. We described a novel mechanism supporting the bistability of bursting and silence. This neuronal model provides a unique opportunity to study the dynamics of networks with neurons possessing different types of multistability.

  9. A Computational Model Based on Multi-Regional Calcium Imaging Represents the Spatio-Temporal Dynamics in a Caenorhabditis elegans Sensory Neuron.

    Directory of Open Access Journals (Sweden)

    Masahiro Kuramochi

    Full Text Available Due to the huge number of neuronal cells in the brain and their complex circuit formation, computer simulation of neuronal activity is indispensable to understanding whole brain dynamics. Recently, various computational models have been developed based on whole-brain calcium imaging data. However, these analyses monitor only the activity of neuronal cell bodies and treat the cells as point unit. This point-neuron model is inexpensive in computational costs, but the model is unrealistically simplistic at representing intact neural activities in the brain. Here, we describe a novel three-unit Ordinary Differential Equation (ODE model based on the neuronal responses derived from a Caenorhabditis elegans salt-sensing neuron. We recorded calcium responses in three regions of the ASER neuron using a simple downstep of NaCl concentration. Our simple ODE model generated from a single recording can adequately reproduce and predict the temporal responses of each part of the neuron to various types of NaCl concentration changes. Our strategy which combines a simple recording data and an ODE mathematical model may be extended to realistically understand whole brain dynamics by computational simulation.

  10. Divisive normalization and neuronal oscillations in a single hierarchical framework of selective visual attention

    Directory of Open Access Journals (Sweden)

    Jorrit Steven Montijn

    2012-05-01

    Full Text Available In divisive normalization models of covert attention, spike rate modulations are commonly used as indicators of the effect of top-down attention. In addition, an increasing number of studies have shown that top-down attention increases the synchronization of neuronal oscillations as well, particularly those in gamma-band frequencies (25 to 100 Hz. Although modulations of spike rate and synchronous oscillations are not mutually exclusive as mechanisms of attention, there has thus far been little effort to integrate these concepts into a single framework of attention. Here, we aim to provide such a unified framework by expanding the normalization model of attention with a time dimension; allowing the simulation of a recently reported backward progression of attentional effects along the visual cortical hierarchy. A simple hierarchical cascade of normalization models simulating different cortical areas however leads to signal degradation and a loss of discriminability over time. To negate this degradation and ensure stable neuronal stimulus representations, we incorporate oscillatory phase entrainment into our model, a mechanism previously proposed as the communication-through-coherence (CTC hypothesis. Our analysis shows that divisive normalization and oscillation models can complement each other in a unified account of the neural mechanisms of selective visual attention. The resulting hierarchical normalization and oscillation (HNO model reproduces several additional spatial and temporal aspects of attentional modulation.

  11. NeuronBank: a tool for cataloging neuronal circuitry

    Directory of Open Access Journals (Sweden)

    Paul S Katz

    2010-04-01

    Full Text Available The basic unit of any nervous system is the neuron. Therefore, understanding the operation of nervous systems ultimately requires an inventory of their constituent neurons and synaptic connectivity, which form neural circuits. The presence of uniquely identifiable neurons or classes of neurons in many invertebrates has facilitated the construction of cellular-level connectivity diagrams that can be generalized across individuals within a species. Homologous neurons can also be recognized across species. Here we describe NeuronBank.org, a web-based tool that we are developing for cataloging, searching, and analyzing neuronal circuitry within and across species. Information from a single species is represented in an individual branch of NeuronBank. Users can search within a branch or perform queries across branches to look for similarities in neuronal circuits across species. The branches allow for an extensible ontology so that additional characteristics can be added as knowledge grows. Each entry in NeuronBank generates a unique accession ID, allowing it to be easily cited. There is also an automatic link to a Wiki page allowing an encyclopedic explanation of the entry. All of the 44 previously published neurons plus one previously unpublished neuron from the mollusc, Tritonia diomedea, have been entered into a branch of NeuronBank as have 4 previously published neurons from the mollusc, Melibe leonina. The ability to organize information about neuronal circuits will make this information more accessible, ultimately aiding research on these important models.

  12. Using a hybrid neuron in physiologically inspired models of the basal ganglia

    Directory of Open Access Journals (Sweden)

    Corey Michael Thibeault

    2013-07-01

    Full Text Available Our current understanding of the basal ganglia has facilitated the creation of computational models that have contributed novel theories, explored new functional anatomy and demonstrated results complementing physiological experiments. However, the utility of these models extends beyond these applications. Particularly in neuromorphic engineering, where the basal ganglia's role in computation is important for applications such as power efficient autonomous agents and model-based control strategies. The neurons used in existing computational models of the basal ganglia however, are not amenable for many low-power hardware implementations. Motivated by a need for more hardware accessible networks, we replicate four published models of the basal ganglia, spanning single neuron and small networks, replacing the more computationally expensive neuron models with an Izhikevich hybrid neuron. This begins with a network modeling action-selection, where the basal activity levels and the ability to appropriately select the most salient input is reproduced. A Parkinson's disease model is then explored under normal conditions, Parkinsonian conditions and during subthalamic nucleus deep brain stimulation. The resulting network is capable of replicating the loss of thalamic relay capabilities in the Parkinsonian state and its return under deep brain stimulation. This is also demonstrated using a network capable of action-selection. Finally, a study of correlation transfer under different patterns of Parkinsonian activity is presented. These networks successfully captured the significant results of the originals studies. This not only creates a foundation for neuromorphic hardware implementations but may also support the development of large-scale biophysical models. The former potentially providing a way of improving the efficacy of deep brain stimulation and the latter allowing for the efficient simulation of larger more comprehensive networks.

  13. Pure state consciousness and its local reduction to neuronal space

    Science.gov (United States)

    Duggins, A. J.

    2013-01-01

    The single neuronal state can be represented as a vector in a complex space, spanned by an orthonormal basis of integer spike counts. In this model a scalar element of experience is associated with the instantaneous firing rate of a single sensory neuron over repeated stimulus presentations. Here the model is extended to composite neural systems that are tensor products of single neuronal vector spaces. Depiction of the mental state as a vector on this tensor product space is intended to capture the unity of consciousness. The density operator is introduced as its local reduction to the single neuron level, from which the firing rate can again be derived as the objective correlate of a subjective element. However, the relational structure of perceptual experience only emerges when the non-local mental state is considered. A metric of phenomenal proximity between neuronal elements of experience is proposed, based on the cross-correlation function of neurophysiology, but constrained by the association of theoretical extremes of correlation/anticorrelation in inseparable 2-neuron states with identical and opponent elements respectively.

  14. A novel single neuron perceptron with universal approximation and XOR computation properties.

    Science.gov (United States)

    Lotfi, Ehsan; Akbarzadeh-T, M-R

    2014-01-01

    We propose a biologically motivated brain-inspired single neuron perceptron (SNP) with universal approximation and XOR computation properties. This computational model extends the input pattern and is based on the excitatory and inhibitory learning rules inspired from neural connections in the human brain's nervous system. The resulting architecture of SNP can be trained by supervised excitatory and inhibitory online learning rules. The main features of proposed single layer perceptron are universal approximation property and low computational complexity. The method is tested on 6 UCI (University of California, Irvine) pattern recognition and classification datasets. Various comparisons with multilayer perceptron (MLP) with gradient decent backpropagation (GDBP) learning algorithm indicate the superiority of the approach in terms of higher accuracy, lower time, and spatial complexity, as well as faster training. Hence, we believe the proposed approach can be generally applicable to various problems such as in pattern recognition and classification.

  15. Spiking Activity of a LIF Neuron in Distributed Delay Framework

    Directory of Open Access Journals (Sweden)

    Saket Kumar Choudhary

    2016-06-01

    Full Text Available Evolution of membrane potential and spiking activity for a single leaky integrate-and-fire (LIF neuron in distributed delay framework (DDF is investigated. DDF provides a mechanism to incorporate memory element in terms of delay (kernel function into a single neuron models. This investigation includes LIF neuron model with two different kinds of delay kernel functions, namely, gamma distributed delay kernel function and hypo-exponential distributed delay kernel function. Evolution of membrane potential for considered models is studied in terms of stationary state probability distribution (SPD. Stationary state probability distribution of membrane potential (SPDV for considered neuron models are found asymptotically similar which is Gaussian distributed. In order to investigate the effect of membrane potential delay, rate code scheme for neuronal information processing is applied. Firing rate and Fano-factor for considered neuron models are calculated and standard LIF model is used for comparative study. It is noticed that distributed delay increases the spiking activity of a neuron. Increase in spiking activity of neuron in DDF is larger for hypo-exponential distributed delay function than gamma distributed delay function. Moreover, in case of hypo-exponential delay function, a LIF neuron generates spikes with Fano-factor less than 1.

  16. Computational modeling of seizure dynamics using coupled neuronal networks: factors shaping epileptiform activity.

    Directory of Open Access Journals (Sweden)

    Sebastien Naze

    2015-05-01

    Full Text Available Epileptic seizure dynamics span multiple scales in space and time. Understanding seizure mechanisms requires identifying the relations between seizure components within and across these scales, together with the analysis of their dynamical repertoire. Mathematical models have been developed to reproduce seizure dynamics across scales ranging from the single neuron to the neural population. In this study, we develop a network model of spiking neurons and systematically investigate the conditions, under which the network displays the emergent dynamic behaviors known from the Epileptor, which is a well-investigated abstract model of epileptic neural activity. This approach allows us to study the biophysical parameters and variables leading to epileptiform discharges at cellular and network levels. Our network model is composed of two neuronal populations, characterized by fast excitatory bursting neurons and regular spiking inhibitory neurons, embedded in a common extracellular environment represented by a slow variable. By systematically analyzing the parameter landscape offered by the simulation framework, we reproduce typical sequences of neural activity observed during status epilepticus. We find that exogenous fluctuations from extracellular environment and electro-tonic couplings play a major role in the progression of the seizure, which supports previous studies and further validates our model. We also investigate the influence of chemical synaptic coupling in the generation of spontaneous seizure-like events. Our results argue towards a temporal shift of typical spike waves with fast discharges as synaptic strengths are varied. We demonstrate that spike waves, including interictal spikes, are generated primarily by inhibitory neurons, whereas fast discharges during the wave part are due to excitatory neurons. Simulated traces are compared with in vivo experimental data from rodents at different stages of the disorder. We draw the conclusion

  17. From spiking neuron models to linear-nonlinear models.

    Science.gov (United States)

    Ostojic, Srdjan; Brunel, Nicolas

    2011-01-20

    Neurons transform time-varying inputs into action potentials emitted stochastically at a time dependent rate. The mapping from current input to output firing rate is often represented with the help of phenomenological models such as the linear-nonlinear (LN) cascade, in which the output firing rate is estimated by applying to the input successively a linear temporal filter and a static non-linear transformation. These simplified models leave out the biophysical details of action potential generation. It is not a priori clear to which extent the input-output mapping of biophysically more realistic, spiking neuron models can be reduced to a simple linear-nonlinear cascade. Here we investigate this question for the leaky integrate-and-fire (LIF), exponential integrate-and-fire (EIF) and conductance-based Wang-Buzsáki models in presence of background synaptic activity. We exploit available analytic results for these models to determine the corresponding linear filter and static non-linearity in a parameter-free form. We show that the obtained functions are identical to the linear filter and static non-linearity determined using standard reverse correlation analysis. We then quantitatively compare the output of the corresponding linear-nonlinear cascade with numerical simulations of spiking neurons, systematically varying the parameters of input signal and background noise. We find that the LN cascade provides accurate estimates of the firing rates of spiking neurons in most of parameter space. For the EIF and Wang-Buzsáki models, we show that the LN cascade can be reduced to a firing rate model, the timescale of which we determine analytically. Finally we introduce an adaptive timescale rate model in which the timescale of the linear filter depends on the instantaneous firing rate. This model leads to highly accurate estimates of instantaneous firing rates.

  18. Modeling the diffusion magnetic resonance imaging signal inside neurons

    International Nuclear Information System (INIS)

    Nguyen, D V; Li, J R; Grebenkov, D S; Le Bihan, D

    2014-01-01

    The Bloch-Torrey partial differential equation (PDE) describes the complex transverse water proton magnetization due to diffusion-encoding magnetic field gradient pulses. The integral of the solution of this PDE yields the diffusion magnetic resonance imaging (dMRI) signal. In a complex medium such as cerebral tissue, it is difficult to explicitly link the dMRI signal to biological parameters such as the cellular geometry or the cellular volume fraction. Studying the dMRI signal arising from a single neuron can provide insight into how the geometrical structure of neurons influences the measured signal. We formulate the Bloch-Torrey PDE inside a single neuron, under no water exchange condition with the extracellular space, and show how to reduce the 3D simulation in the full neuron to a 3D simulation around the soma and 1D simulations in the neurites. We show that this latter approach is computationally much faster than full 3D simulation and still gives accurate results over a wide range of diffusion times

  19. A single gene target of an ETS-family transcription factor determines neuronal CO2-chemosensitivity

    DEFF Research Database (Denmark)

    Brandt, Julia P; Aziz-Zaman, Sonya; Juozaityte, Vaida

    2012-01-01

    . We report here a mechanism that endows C. elegans neurons with the ability to detect CO(2). The ETS-5 transcription factor is necessary for the specification of CO(2)-sensing BAG neurons. Expression of a single ETS-5 target gene, gcy-9, which encodes a receptor-type guanylate cyclase, is sufficient...

  20. Simultaneous transcranial magnetic stimulation and single neuron recording in alert non-human primates

    OpenAIRE

    Mueller, Jerel K.; Grigsby, Erinn M.; Prevosto, Vincent; Petraglia, Frank W.; Rao, Hrishikesh; Deng, Zhi-De; Peterchev, Angel V.; Sommer, Marc A.; Egner, Tobias; Platt, Michael L.; Grill, Warren M.

    2014-01-01

    Transcranial magnetic stimulation (TMS) is a widely used, noninvasive method for stimulating nervous tissue, yet its mechanisms of effect are poorly understood. Here we report novel methods for studying the influence of TMS on single neurons in the brain of alert non-human primates. We designed a TMS coil that focuses its effect near the tip of a recording electrode and recording electronics that enable direct acquisition of neuronal signals at the site of peak stimulus strength minimally per...

  1. Mirror neurons: functions, mechanisms and models.

    Science.gov (United States)

    Oztop, Erhan; Kawato, Mitsuo; Arbib, Michael A

    2013-04-12

    Mirror neurons for manipulation fire both when the animal manipulates an object in a specific way and when it sees another animal (or the experimenter) perform an action that is more or less similar. Such neurons were originally found in macaque monkeys, in the ventral premotor cortex, area F5 and later also in the inferior parietal lobule. Recent neuroimaging data indicate that the adult human brain is endowed with a "mirror neuron system," putatively containing mirror neurons and other neurons, for matching the observation and execution of actions. Mirror neurons may serve action recognition in monkeys as well as humans, whereas their putative role in imitation and language may be realized in human but not in monkey. This article shows the important role of computational models in providing sufficient and causal explanations for the observed phenomena involving mirror systems and the learning processes which form them, and underlines the need for additional circuitry to lift up the monkey mirror neuron circuit to sustain the posited cognitive functions attributed to the human mirror neuron system. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

  2. Long-term memory in Aplysia modulates the total number of varicosities of single identified sensory neurons.

    OpenAIRE

    Bailey, C H; Chen, M

    1988-01-01

    The morphological consequences of long-term habituation and sensitization of the gill withdrawal reflex in Aplysia california were explored by examining the total number of presynaptic varicosities of single identified sensory neurons (a critical site of plasticity for the biochemical and biophysical changes that underlie both types of learning) in control and behaviorally trained animals. Sensory neurons from habituated animals had 35% fewer synaptic varicosities than did sensory neurons fro...

  3. Nanosecond laser pulse stimulation of spiral ganglion neurons and model cells.

    Science.gov (United States)

    Rettenmaier, Alexander; Lenarz, Thomas; Reuter, Günter

    2014-04-01

    Optical stimulation of the inner ear has recently attracted attention, suggesting a higher frequency resolution compared to electrical cochlear implants due to its high spatial stimulation selectivity. Although the feasibility of the effect is shown in multiple in vivo experiments, the stimulation mechanism remains open to discussion. Here we investigate in single-cell measurements the reaction of spiral ganglion neurons and model cells to irradiation with a nanosecond-pulsed laser beam over a broad wavelength range from 420 nm up to 1950 nm using the patch clamp technique. Cell reactions were wavelength- and pulse-energy-dependent but too small to elicit action potentials in the investigated spiral ganglion neurons. As the applied radiant exposure was much higher than the reported threshold for in vivo experiments in the same laser regime, we conclude that in a stimulation paradigm with nanosecond-pulses, direct neuronal stimulation is not the main cause of optical cochlea stimulation.

  4. Graph-based unsupervised segmentation algorithm for cultured neuronal networks' structure characterization and modeling.

    Science.gov (United States)

    de Santos-Sierra, Daniel; Sendiña-Nadal, Irene; Leyva, Inmaculada; Almendral, Juan A; Ayali, Amir; Anava, Sarit; Sánchez-Ávila, Carmen; Boccaletti, Stefano

    2015-06-01

    Large scale phase-contrast images taken at high resolution through the life of a cultured neuronal network are analyzed by a graph-based unsupervised segmentation algorithm with a very low computational cost, scaling linearly with the image size. The processing automatically retrieves the whole network structure, an object whose mathematical representation is a matrix in which nodes are identified neurons or neurons' clusters, and links are the reconstructed connections between them. The algorithm is also able to extract any other relevant morphological information characterizing neurons and neurites. More importantly, and at variance with other segmentation methods that require fluorescence imaging from immunocytochemistry techniques, our non invasive measures entitle us to perform a longitudinal analysis during the maturation of a single culture. Such an analysis furnishes the way of individuating the main physical processes underlying the self-organization of the neurons' ensemble into a complex network, and drives the formulation of a phenomenological model yet able to describe qualitatively the overall scenario observed during the culture growth. © 2014 International Society for Advancement of Cytometry.

  5. BigNeuron: Large-scale 3D Neuron Reconstruction from Optical Microscopy Images

    OpenAIRE

    Peng, Hanchuan; Hawrylycz, Michael; Roskams, Jane; Hill, Sean; Spruston, Nelson; Meijering, Erik; Ascoli, Giorgio A.

    2015-01-01

    textabstractUnderstanding the structure of single neurons is critical for understanding how they function within neural circuits. BigNeuron is a new community effort that combines modern bioimaging informatics, recent leaps in labeling and microscopy, and the widely recognized need for openness and standardization to provide a community resource for automated reconstruction of dendritic and axonal morphology of single neurons. Understanding the structure of single neurons is critical for unde...

  6. Effects of dynamic synapses on noise-delayed response latency of a single neuron

    Science.gov (United States)

    Uzuntarla, M.; Ozer, M.; Ileri, U.; Calim, A.; Torres, J. J.

    2015-12-01

    The noise-delayed decay (NDD) phenomenon emerges when the first-spike latency of a periodically forced stochastic neuron exhibits a maximum for a particular range of noise intensity. Here, we investigate the latency response dynamics of a single Hodgkin-Huxley neuron that is subject to both a suprathreshold periodic stimulus and a background activity arriving through dynamic synapses. We study the first-spike latency response as a function of the presynaptic firing rate f . This constitutes a more realistic scenario than previous works, since f provides a suitable biophysically realistic parameter to control the level of activity in actual neural systems. We first report on the emergence of classical NDD behavior as a function of f for the limit of static synapses. Second, we show that when short-term depression and facilitation mechanisms are included at the synapses, different NDD features can be found due to their modulatory effect on synaptic current fluctuations. For example, an intriguing double NDD (DNDD) behavior occurs for different sets of relevant synaptic parameters. Moreover, depending on the balance between synaptic depression and synaptic facilitation, single NDD or DNDD can prevail, in such a way that synaptic facilitation favors the emergence of DNDD whereas synaptic depression favors the existence of single NDD. Here we report the existence of the DNDD effect in the response latency dynamics of a neuron.

  7. Differing effects of attention in single-units and populations are well predicted by heterogeneous tuning and the normalization model of attention.

    Science.gov (United States)

    Hara, Yuko; Pestilli, Franco; Gardner, Justin L

    2014-01-01

    Single-unit measurements have reported many different effects of attention on contrast-response (e.g., contrast-gain, response-gain, additive-offset dependent on visibility), while functional imaging measurements have more uniformly reported increases in response across all contrasts (additive-offset). The normalization model of attention elegantly predicts the diversity of effects of attention reported in single-units well-tuned to the stimulus, but what predictions does it make for more realistic populations of neurons with heterogeneous tuning? Are predictions in accordance with population-scale measurements? We used functional imaging data from humans to determine a realistic ratio of attention-field to stimulus-drive size (a key parameter for the model) and predicted effects of attention in a population of model neurons with heterogeneous tuning. We found that within the population, neurons well-tuned to the stimulus showed a response-gain effect, while less-well-tuned neurons showed a contrast-gain effect. Averaged across the population, these disparate effects of attention gave rise to additive-offsets in contrast-response, similar to reports in human functional imaging as well as population averages of single-units. Differences in predictions for single-units and populations were observed across a wide range of model parameters (ratios of attention-field to stimulus-drive size and the amount of baseline response modifiable by attention), offering an explanation for disparity in physiological reports. Thus, by accounting for heterogeneity in tuning of realistic neuronal populations, the normalization model of attention can not only predict responses of well-tuned neurons, but also the activity of large populations of neurons. More generally, computational models can unify physiological findings across different scales of measurement, and make links to behavior, but only if factors such as heterogeneous tuning within a population are properly accounted for.

  8. Single Neurons in the Avian Auditory Cortex Encode Individual Identity and Propagation Distance in Naturally Degraded Communication Calls.

    Science.gov (United States)

    Mouterde, Solveig C; Elie, Julie E; Mathevon, Nicolas; Theunissen, Frédéric E

    2017-03-29

    One of the most complex tasks performed by sensory systems is "scene analysis": the interpretation of complex signals as behaviorally relevant objects. The study of this problem, universal to species and sensory modalities, is particularly challenging in audition, where sounds from various sources and localizations, degraded by propagation through the environment, sum to form a single acoustical signal. Here we investigated in a songbird model, the zebra finch, the neural substrate for ranging and identifying a single source. We relied on ecologically and behaviorally relevant stimuli, contact calls, to investigate the neural discrimination of individual vocal signature as well as sound source distance when calls have been degraded through propagation in a natural environment. Performing electrophysiological recordings in anesthetized birds, we found neurons in the auditory forebrain that discriminate individual vocal signatures despite long-range degradation, as well as neurons discriminating propagation distance, with varying degrees of multiplexing between both information types. Moreover, the neural discrimination performance of individual identity was not affected by propagation-induced degradation beyond what was induced by the decreased intensity. For the first time, neurons with distance-invariant identity discrimination properties as well as distance-discriminant neurons are revealed in the avian auditory cortex. Because these neurons were recorded in animals that had prior experience neither with the vocalizers of the stimuli nor with long-range propagation of calls, we suggest that this neural population is part of a general-purpose system for vocalizer discrimination and ranging. SIGNIFICANCE STATEMENT Understanding how the brain makes sense of the multitude of stimuli that it continually receives in natural conditions is a challenge for scientists. Here we provide a new understanding of how the auditory system extracts behaviorally relevant information

  9. A phase plane analysis of neuron-astrocyte interactions.

    Science.gov (United States)

    Amiri, Mahmood; Montaseri, Ghazal; Bahrami, Fariba

    2013-08-01

    Intensive experimental studies have shown that astrocytes are active partners in modulation of synaptic transmission. In the present research, we study neuron-astrocyte signaling using a biologically inspired model of one neuron synapsing one astrocyte. In this model, the firing dynamics of the neuron is described by the Morris-Lecar model and the Ca(2+) dynamics of a single astrocyte explained by a functional model introduced by Postnov and colleagues. Using the coupled neuron-astrocyte model and based on the results of the phase plane analyses, it is demonstrated that the astrocyte is able to activate the silent neuron or change the neuron spiking frequency through bidirectional communication. This suggests that astrocyte feedback signaling is capable of modulating spike transmission frequency by changing neuron spiking frequency. This effect is described by a saddle-node on invariant circle bifurcation in the coupled neuron-astrocyte model. In this way, our results suggest that the neuron-astrocyte crosstalk has a fundamental role in producing diverse neuronal activities and therefore enhances the information processing capabilities of the brain. Crown Copyright © 2013. Published by Elsevier Ltd. All rights reserved.

  10. Transcriptional profiling at whole population and single cell levels reveals somatosensory neuron molecular diversity

    Science.gov (United States)

    Chiu, Isaac M; Barrett, Lee B; Williams, Erika K; Strochlic, David E; Lee, Seungkyu; Weyer, Andy D; Lou, Shan; Bryman, Gregory S; Roberson, David P; Ghasemlou, Nader; Piccoli, Cara; Ahat, Ezgi; Wang, Victor; Cobos, Enrique J; Stucky, Cheryl L; Ma, Qiufu; Liberles, Stephen D; Woolf, Clifford J

    2014-01-01

    The somatosensory nervous system is critical for the organism's ability to respond to mechanical, thermal, and nociceptive stimuli. Somatosensory neurons are functionally and anatomically diverse but their molecular profiles are not well-defined. Here, we used transcriptional profiling to analyze the detailed molecular signatures of dorsal root ganglion (DRG) sensory neurons. We used two mouse reporter lines and surface IB4 labeling to purify three major non-overlapping classes of neurons: 1) IB4+SNS-Cre/TdTomato+, 2) IB4−SNS-Cre/TdTomato+, and 3) Parv-Cre/TdTomato+ cells, encompassing the majority of nociceptive, pruriceptive, and proprioceptive neurons. These neurons displayed distinct expression patterns of ion channels, transcription factors, and GPCRs. Highly parallel qRT-PCR analysis of 334 single neurons selected by membership of the three populations demonstrated further diversity, with unbiased clustering analysis identifying six distinct subgroups. These data significantly increase our knowledge of the molecular identities of known DRG populations and uncover potentially novel subsets, revealing the complexity and diversity of those neurons underlying somatosensation. DOI: http://dx.doi.org/10.7554/eLife.04660.001 PMID:25525749

  11. Dynamical analysis of Parkinsonian state emulated by hybrid Izhikevich neuron models

    Science.gov (United States)

    Liu, Chen; Wang, Jiang; Yu, Haitao; Deng, Bin; Wei, Xile; Li, Huiyan; Loparo, Kenneth A.; Fietkiewicz, Chris

    2015-11-01

    Computational models play a significant role in exploring novel theories to complement the findings of physiological experiments. Various computational models have been developed to reveal the mechanisms underlying brain functions. Particularly, in the development of therapies to modulate behavioral and pathological abnormalities, computational models provide the basic foundations to exhibit transitions between physiological and pathological conditions. Considering the significant roles of the intrinsic properties of the globus pallidus and the coupling connections between neurons in determining the firing patterns and the dynamical activities of the basal ganglia neuronal network, we propose a hypothesis that pathological behaviors under the Parkinsonian state may originate from combined effects of intrinsic properties of globus pallidus neurons and synaptic conductances in the whole neuronal network. In order to establish a computational efficient network model, hybrid Izhikevich neuron model is used due to its capacity of capturing the dynamical characteristics of the biological neuronal activities. Detailed analysis of the individual Izhikevich neuron model can assist in understanding the roles of model parameters, which then facilitates the establishment of the basal ganglia-thalamic network model, and contributes to a further exploration of the underlying mechanisms of the Parkinsonian state. Simulation results show that the hybrid Izhikevich neuron model is capable of capturing many of the dynamical properties of the basal ganglia-thalamic neuronal network, such as variations of the firing rates and emergence of synchronous oscillations under the Parkinsonian condition, despite the simplicity of the two-dimensional neuronal model. It may suggest that the computational efficient hybrid Izhikevich neuron model can be used to explore basal ganglia normal and abnormal functions. Especially it provides an efficient way of emulating the large-scale neuron network

  12. Direct Neuronal Reprogramming for Disease Modeling Studies Using Patient-Derived Neurons: What Have We Learned?

    Directory of Open Access Journals (Sweden)

    Janelle Drouin-Ouellet

    2017-09-01

    Full Text Available Direct neuronal reprogramming, by which a neuron is formed via direct conversion from a somatic cell without going through a pluripotent intermediate stage, allows for the possibility of generating patient-derived neurons. A unique feature of these so-called induced neurons (iNs is the potential to maintain aging and epigenetic signatures of the donor, which is critical given that many diseases of the CNS are age related. Here, we review the published literature on the work that has been undertaken using iNs to model human brain disorders. Furthermore, as disease-modeling studies using this direct neuronal reprogramming approach are becoming more widely adopted, it is important to assess the criteria that are used to characterize the iNs, especially in relation to the extent to which they are mature adult neurons. In particular: i what constitutes an iN cell, ii which stages of conversion offer the earliest/optimal time to assess features that are specific to neurons and/or a disorder and iii whether generating subtype-specific iNs is critical to the disease-related features that iNs express. Finally, we discuss the range of potential biomedical applications that can be explored using patient-specific models of neurological disorders with iNs, and the challenges that will need to be overcome in order to realize these applications.

  13. Orientation selectivity in inhibition-dominated networks of spiking neurons: effect of single neuron properties and network dynamics.

    Science.gov (United States)

    Sadeh, Sadra; Rotter, Stefan

    2015-01-01

    The neuronal mechanisms underlying the emergence of orientation selectivity in the primary visual cortex of mammals are still elusive. In rodents, visual neurons show highly selective responses to oriented stimuli, but neighboring neurons do not necessarily have similar preferences. Instead of a smooth map, one observes a salt-and-pepper organization of orientation selectivity. Modeling studies have recently confirmed that balanced random networks are indeed capable of amplifying weakly tuned inputs and generating highly selective output responses, even in absence of feature-selective recurrent connectivity. Here we seek to elucidate the neuronal mechanisms underlying this phenomenon by resorting to networks of integrate-and-fire neurons, which are amenable to analytic treatment. Specifically, in networks of perfect integrate-and-fire neurons, we observe that highly selective and contrast invariant output responses emerge, very similar to networks of leaky integrate-and-fire neurons. We then demonstrate that a theory based on mean firing rates and the detailed network topology predicts the output responses, and explains the mechanisms underlying the suppression of the common-mode, amplification of modulation, and contrast invariance. Increasing inhibition dominance in our networks makes the rectifying nonlinearity more prominent, which in turn adds some distortions to the otherwise essentially linear prediction. An extension of the linear theory can account for all the distortions, enabling us to compute the exact shape of every individual tuning curve in our networks. We show that this simple form of nonlinearity adds two important properties to orientation selectivity in the network, namely sharpening of tuning curves and extra suppression of the modulation. The theory can be further extended to account for the nonlinearity of the leaky model by replacing the rectifier by the appropriate smooth input-output transfer function. These results are robust and do not

  14. Orientation selectivity in inhibition-dominated networks of spiking neurons: effect of single neuron properties and network dynamics.

    Directory of Open Access Journals (Sweden)

    Sadra Sadeh

    2015-01-01

    Full Text Available The neuronal mechanisms underlying the emergence of orientation selectivity in the primary visual cortex of mammals are still elusive. In rodents, visual neurons show highly selective responses to oriented stimuli, but neighboring neurons do not necessarily have similar preferences. Instead of a smooth map, one observes a salt-and-pepper organization of orientation selectivity. Modeling studies have recently confirmed that balanced random networks are indeed capable of amplifying weakly tuned inputs and generating highly selective output responses, even in absence of feature-selective recurrent connectivity. Here we seek to elucidate the neuronal mechanisms underlying this phenomenon by resorting to networks of integrate-and-fire neurons, which are amenable to analytic treatment. Specifically, in networks of perfect integrate-and-fire neurons, we observe that highly selective and contrast invariant output responses emerge, very similar to networks of leaky integrate-and-fire neurons. We then demonstrate that a theory based on mean firing rates and the detailed network topology predicts the output responses, and explains the mechanisms underlying the suppression of the common-mode, amplification of modulation, and contrast invariance. Increasing inhibition dominance in our networks makes the rectifying nonlinearity more prominent, which in turn adds some distortions to the otherwise essentially linear prediction. An extension of the linear theory can account for all the distortions, enabling us to compute the exact shape of every individual tuning curve in our networks. We show that this simple form of nonlinearity adds two important properties to orientation selectivity in the network, namely sharpening of tuning curves and extra suppression of the modulation. The theory can be further extended to account for the nonlinearity of the leaky model by replacing the rectifier by the appropriate smooth input-output transfer function. These results are

  15. Evoking prescribed spike times in stochastic neurons

    Science.gov (United States)

    Doose, Jens; Lindner, Benjamin

    2017-09-01

    Single cell stimulation in vivo is a powerful tool to investigate the properties of single neurons and their functionality in neural networks. We present a method to determine a cell-specific stimulus that reliably evokes a prescribed spike train with high temporal precision of action potentials. We test the performance of this stimulus in simulations for two different stochastic neuron models. For a broad range of parameters and a neuron firing with intermediate firing rates (20-40 Hz) the reliability in evoking the prescribed spike train is close to its theoretical maximum that is mainly determined by the level of intrinsic noise.

  16. A single-trace dual-process model of episodic memory: a novel computational account of familiarity and recollection.

    Science.gov (United States)

    Greve, Andrea; Donaldson, David I; van Rossum, Mark C W

    2010-02-01

    Dual-process theories of episodic memory state that retrieval is contingent on two independent processes: familiarity (providing a sense of oldness) and recollection (recovering events and their context). A variety of studies have reported distinct neural signatures for familiarity and recollection, supporting dual-process theory. One outstanding question is whether these signatures reflect the activation of distinct memory traces or the operation of different retrieval mechanisms on a single memory trace. We present a computational model that uses a single neuronal network to store memory traces, but two distinct and independent retrieval processes access the memory. The model is capable of performing familiarity and recollection-based discrimination between old and new patterns, demonstrating that dual-process models need not to rely on multiple independent memory traces, but can use a single trace. Importantly, our putative familiarity and recollection processes exhibit distinct characteristics analogous to those found in empirical data; they diverge in capacity and sensitivity to sparse and correlated patterns, exhibit distinct ROC curves, and account for performance on both item and associative recognition tests. The demonstration that a single-trace, dual-process model can account for a range of empirical findings highlights the importance of distinguishing between neuronal processes and the neuronal representations on which they operate.

  17. Single Ih channels in pyramidal neuron dendrites: properties, distribution, and impact on action potential output

    NARCIS (Netherlands)

    Kole, Maarten H. P.; Hallermann, Stefan; Stuart, Greg J.

    2006-01-01

    The hyperpolarization-activated cation current (Ih) plays an important role in regulating neuronal excitability, yet its native single-channel properties in the brain are essentially unknown. Here we use variance-mean analysis to study the properties of single Ih channels in the apical dendrites of

  18. Comprehensive Identification and Spatial Mapping of Habenular Neuronal Types Using Single-Cell RNA-Seq.

    Science.gov (United States)

    Pandey, Shristi; Shekhar, Karthik; Regev, Aviv; Schier, Alexander F

    2018-04-02

    The identification of cell types and marker genes is critical for dissecting neural development and function, but the size and complexity of the brain has hindered the comprehensive discovery of cell types. We combined single-cell RNA-seq (scRNA-seq) with anatomical brain registration to create a comprehensive map of the zebrafish habenula, a conserved forebrain hub involved in pain processing and learning. Single-cell transcriptomes of ∼13,000 habenular cells with 4× cellular coverage identified 18 neuronal types and dozens of marker genes. Registration of marker genes onto a reference atlas created a resource for anatomical and functional studies and enabled the mapping of active neurons onto neuronal types following aversive stimuli. Strikingly, despite brain growth and functional maturation, cell types were retained between the larval and adult habenula. This study provides a gene expression atlas to dissect habenular development and function and offers a general framework for the comprehensive characterization of other brain regions. Copyright © 2018 Elsevier Ltd. All rights reserved.

  19. An introduction to modeling neuronal dynamics

    CERN Document Server

    Börgers, Christoph

    2017-01-01

    This book is intended as a text for a one-semester course on Mathematical and Computational Neuroscience for upper-level undergraduate and beginning graduate students of mathematics, the natural sciences, engineering, or computer science. An undergraduate introduction to differential equations is more than enough mathematical background. Only a slim, high school-level background in physics is assumed, and none in biology. Topics include models of individual nerve cells and their dynamics, models of networks of neurons coupled by synapses and gap junctions, origins and functions of population rhythms in neuronal networks, and models of synaptic plasticity. An extensive online collection of Matlab programs generating the figures accompanies the book. .

  20. Computational model of neuron-astrocyte interactions during focal seizure generation

    Directory of Open Access Journals (Sweden)

    Davide eReato

    2012-10-01

    Full Text Available Empirical research in the last decade revealed that astrocytes can respond to neurotransmitters with Ca2+ elevations and generate feedback signals to neurons which modulate synaptic transmission and neuronal excitability. This discovery changed our basic understanding of brain function and provided new perspectives for how astrocytes can participate not only to information processing, but also to the genesis of brain disorders, such as epilepsy. Epilepsy is a neurological disorder characterized by recurrent seizures that can arise focally at restricted areas and propagate throughout the brain. Studies in brain slice models suggest that astrocytes contribute to epileptiform activity by increasing neuronal excitability through a Ca2+-dependent release of glutamate. The underlying mechanism remains, however, unclear. In this study, we implemented a parsimonious network model of neurons and astrocytes. The model consists of excitatory and inhibitory neurons described by Izhikevich's neuron dynamics. The experimentally observed Ca2+ change in astrocytes in response to neuronal activity was modeled with linear equations. We considered that glutamate is released from astrocytes above certain intracellular Ca2+ concentrations thus providing a non-linear positive feedback signal to neurons. Propagating seizure-like ictal discharges (IDs were reliably evoked in our computational model by repeatedly exciting a small area of the network, which replicates experimental results in a slice model of focal ID in entorhinal cortex. We found that the threshold of focal ID generation was lowered when an excitatory feedback-loop between astrocytes and neurons was included. Simulations show that astrocytes can contribute to ID generation by directly affecting the excitatory/inhibitory balance of the neuronal network. Our model can be used to obtain mechanistic insights into the distinct contributions of the different signaling pathways to the generation and

  1. Multistability in a neuron model with extracellular potassium dynamics

    Science.gov (United States)

    Wu, Xing-Xing; Shuai, J. W.

    2012-06-01

    Experiments show a primary role of extracellular potassium concentrations in neuronal hyperexcitability and in the generation of epileptiform bursting and depolarization blocks without synaptic mechanisms. We adopt a physiologically relevant hippocampal CA1 neuron model in a zero-calcium condition to better understand the function of extracellular potassium in neuronal seizurelike activities. The model neuron is surrounded by interstitial space in which potassium ions are able to accumulate. Potassium currents, Na+-K+ pumps, glial buffering, and ion diffusion are regulatory mechanisms of extracellular potassium. We also consider a reduced model with a fixed potassium concentration. The bifurcation structure and spiking frequency of the two models are studied. We show that, besides hyperexcitability and bursting pattern modulation, the potassium dynamics can induce not only bistability but also tristability of different firing patterns. Our results reveal the emergence of the complex behavior of multistability due to the dynamical [K+]o modulation on neuronal activities.

  2. Value encoding in single neurons in the human amygdala during decision making.

    Science.gov (United States)

    Jenison, Rick L; Rangel, Antonio; Oya, Hiroyuki; Kawasaki, Hiroto; Howard, Matthew A

    2011-01-05

    A growing consensus suggests that the brain makes simple choices by assigning values to the stimuli under consideration and then comparing these values to make a decision. However, the network involved in computing the values has not yet been fully characterized. Here, we investigated whether the human amygdala plays a role in the computation of stimulus values at the time of decision making. We recorded single neuron activity from the amygdala of awake patients while they made simple purchase decisions over food items. We found 16 amygdala neurons, located primarily in the basolateral nucleus that responded linearly to the values assigned to individual items.

  3. Elucidating distinct ion channel populations on the surface of hippocampal neurons via single-particle tracking recurrence analysis

    Science.gov (United States)

    Sikora, Grzegorz; Wyłomańska, Agnieszka; Gajda, Janusz; Solé, Laura; Akin, Elizabeth J.; Tamkun, Michael M.; Krapf, Diego

    2017-12-01

    Protein and lipid nanodomains are prevalent on the surface of mammalian cells. In particular, it has been recently recognized that ion channels assemble into surface nanoclusters in the soma of cultured neurons. However, the interactions of these molecules with surface nanodomains display a considerable degree of heterogeneity. Here, we investigate this heterogeneity and develop statistical tools based on the recurrence of individual trajectories to identify subpopulations within ion channels in the neuronal surface. We specifically study the dynamics of the K+ channel Kv1.4 and the Na+ channel Nav1.6 on the surface of cultured hippocampal neurons at the single-molecule level. We find that both these molecules are expressed in two different forms with distinct kinetics with regards to surface interactions, emphasizing the complex proteomic landscape of the neuronal surface. Further, the tools presented in this work provide new methods for the analysis of membrane nanodomains, transient confinement, and identification of populations within single-particle trajectories.

  4. Hyperbolic Plykin attractor can exist in neuron models

    DEFF Research Database (Denmark)

    Belykh, V.; Belykh, I.; Mosekilde, Erik

    2005-01-01

    Strange hyperbolic attractors are hard to find in real physical systems. This paper provides the first example of a realistic system, a canonical three-dimensional (3D) model of bursting neurons, that is likely to have a strange hyperbolic attractor. Using a geometrical approach to the study...... of the neuron model, we derive a flow-defined Poincare map giving ail accurate account of the system's dynamics. In a parameter region where the neuron system undergoes bifurcations causing transitions between tonic spiking and bursting, this two-dimensional map becomes a map of a disk with several periodic...... holes. A particular case is the map of a disk with three holes, matching the Plykin example of a planar hyperbolic attractor. The corresponding attractor of the 3D neuron model appears to be hyperbolic (this property is not verified in the present paper) and arises as a result of a two-loop (secondary...

  5. Towards reproducible descriptions of neuronal network models.

    Directory of Open Access Journals (Sweden)

    Eilen Nordlie

    2009-08-01

    Full Text Available Progress in science depends on the effective exchange of ideas among scientists. New ideas can be assessed and criticized in a meaningful manner only if they are formulated precisely. This applies to simulation studies as well as to experiments and theories. But after more than 50 years of neuronal network simulations, we still lack a clear and common understanding of the role of computational models in neuroscience as well as established practices for describing network models in publications. This hinders the critical evaluation of network models as well as their re-use. We analyze here 14 research papers proposing neuronal network models of different complexity and find widely varying approaches to model descriptions, with regard to both the means of description and the ordering and placement of material. We further observe great variation in the graphical representation of networks and the notation used in equations. Based on our observations, we propose a good model description practice, composed of guidelines for the organization of publications, a checklist for model descriptions, templates for tables presenting model structure, and guidelines for diagrams of networks. The main purpose of this good practice is to trigger a debate about the communication of neuronal network models in a manner comprehensible to humans, as opposed to machine-readable model description languages. We believe that the good model description practice proposed here, together with a number of other recent initiatives on data-, model-, and software-sharing, may lead to a deeper and more fruitful exchange of ideas among computational neuroscientists in years to come. We further hope that work on standardized ways of describing--and thinking about--complex neuronal networks will lead the scientific community to a clearer understanding of high-level concepts in network dynamics, and will thus lead to deeper insights into the function of the brain.

  6. Simple cortical and thalamic neuron models for digital arithmetic circuit implementation

    Directory of Open Access Journals (Sweden)

    Takuya eNanami

    2016-05-01

    Full Text Available Trade-off between reproducibility of neuronal activities and computational efficiency is one ofcrucial subjects in computational neuroscience and neuromorphic engineering. A wide variety ofneuronal models have been studied from different viewpoints. The digital spiking silicon neuron(DSSN model is a qualitative model that focuses on efficient implementation by digital arithmeticcircuits. We expanded the DSSN model and found appropriate parameter sets with which itreproduces the dynamical behaviors of the ionic-conductance models of four classes of corticaland thalamic neurons. We first developed a 4-variable model by reducing the number of variablesin the ionic-conductance models and elucidated its mathematical structures using bifurcationanalysis. Then, expanded DSSN models were constructed that reproduce these mathematicalstructures and capture the characteristic behavior of each neuron class. We confirmed thatstatistics of the neuronal spike sequences are similar in the DSSN and the ionic-conductancemodels. Computational cost of the DSSN model is larger than that of the recent sophisticatedIntegrate-and-Fire-based models, but smaller than the ionic-conductance models. This modelis intended to provide another meeting point for above trade-off that satisfies the demand forlarge-scale neuronal network simulation with closer-to-biology models.

  7. One-dimensional map-based neuron model: A logistic modification

    International Nuclear Information System (INIS)

    Mesbah, Samineh; Moghtadaei, Motahareh; Hashemi Golpayegani, Mohammad Reza; Towhidkhah, Farzad

    2014-01-01

    A one-dimensional map is proposed for modeling some of the neuronal activities, including different spiking and bursting behaviors. The model is obtained by applying some modifications on the well-known Logistic map and is named the Modified and Confined Logistic (MCL) model. Map-based neuron models are known as phenomenological models and recently, they are widely applied in modeling tasks due to their computational efficacy. Most of discrete map-based models involve two variables representing the slow-fast prototype. There are also some one-dimensional maps, which can replicate some of the neuronal activities. However, the existence of four bifurcation parameters in the MCL model gives rise to reproduction of spiking behavior with control over the frequency of the spikes, and imitation of chaotic and regular bursting responses concurrently. It is also shown that the proposed model has the potential to reproduce more realistic bursting activity by adding a second variable. Moreover the MCL model is able to replicate considerable number of experimentally observed neuronal responses introduced in Izhikevich (2004) [23]. Some analytical and numerical analyses of the MCL model dynamics are presented to explain the emersion of complex dynamics from this one-dimensional map

  8. Context-aware modeling of neuronal morphologies

    Directory of Open Access Journals (Sweden)

    Benjamin eTorben-Nielsen

    2014-09-01

    Full Text Available Neuronal morphologies are pivotal for brain functioning: physical overlap between dendrites and axons constrain the circuit topology, and the precise shape and composition of dendrites determine the integration of inputs to produce an output signal. At the same time, morphologies are highly diverse and variant. The variance, presumably, originates from neurons developing in a densely packed brain substrate where they interact (e.g., repulsion or attraction with other actors in this substrate. However, when studying neurons their context is never part of the analysis and they are treated as if they existed in isolation.Here we argue that to fully understand neuronal morphology and its variance it is important to consider neurons in relation to each other and to other actors in the surrounding brain substrate, i.e., their context. We propose a context-aware computational framework, NeuroMaC, in which large numbers of neurons can be grown simultaneously according to growth rules expressed in terms of interactions between the developing neuron and the surrounding brain substrate.As a proof of principle, we demonstrate that by using NeuroMaC we can generate accurate virtual morphologies of distinct classes both in isolation and as part of neuronal forests. Accuracy is validated against population statistics of experimentally reconstructed morphologies. We show that context-aware generation of neurons can explain characteristics of variation. Indeed, plausible variation is an inherent property of the morphologies generated by context-aware rules. We speculate about the applicability of this framework to investigate morphologies and circuits, to classify healthy and pathological morphologies, and to generate large quantities of morphologies for large-scale modeling.

  9. Estimation in the partially observed stochastic Morris-Lecar neuronal model with particle filter and stochastic approximation methods

    DEFF Research Database (Denmark)

    Ditlevsen, Susanne; Samson, Adeline

    2014-01-01

    Parameter estimation in multidimensional diffusion models with only one coordinate observed is highly relevant in many biological applications, but a statistically difficult problem. In neuroscience, the membrane potential evolution in single neurons can be measured at high frequency, but biophys...

  10. Hybrid Scheme for Modeling Local Field Potentials from Point-Neuron Networks

    DEFF Research Database (Denmark)

    Hagen, Espen; Dahmen, David; Stavrinou, Maria L

    2016-01-01

    on populations of network-equivalent multicompartment neuron models with layer-specific synaptic connectivity, can be used with an arbitrary number of point-neuron network populations, and allows for a full separation of simulated network dynamics and LFPs. We apply the scheme to a full-scale cortical network......With rapidly advancing multi-electrode recording technology, the local field potential (LFP) has again become a popular measure of neuronal activity in both research and clinical applications. Proper understanding of the LFP requires detailed mathematical modeling incorporating the anatomical...... and electrophysiological features of neurons near the recording electrode, as well as synaptic inputs from the entire network. Here we propose a hybrid modeling scheme combining efficient point-neuron network models with biophysical principles underlying LFP generation by real neurons. The LFP predictions rely...

  11. A Neuronal Network Model for Pitch Selectivity and Representation

    OpenAIRE

    Huang, Chengcheng; Rinzel, John

    2016-01-01

    Pitch is a perceptual correlate of periodicity. Sounds with distinct spectra can elicit the same pitch. Despite the importance of pitch perception, understanding the cellular mechanism of pitch perception is still a major challenge and a mechanistic model of pitch is lacking. A multi-stage neuronal network model is developed for pitch frequency estimation using biophysically-based, high-resolution coincidence detector neurons. The neuronal units respond only to highly coincident input among c...

  12. Digital hardware implementation of a stochastic two-dimensional neuron model.

    Science.gov (United States)

    Grassia, F; Kohno, T; Levi, T

    2016-11-01

    This study explores the feasibility of stochastic neuron simulation in digital systems (FPGA), which realizes an implementation of a two-dimensional neuron model. The stochasticity is added by a source of current noise in the silicon neuron using an Ornstein-Uhlenbeck process. This approach uses digital computation to emulate individual neuron behavior using fixed point arithmetic operation. The neuron model's computations are performed in arithmetic pipelines. It was designed in VHDL language and simulated prior to mapping in the FPGA. The experimental results confirmed the validity of the developed stochastic FPGA implementation, which makes the implementation of the silicon neuron more biologically plausible for future hybrid experiments. Copyright © 2017 Elsevier Ltd. All rights reserved.

  13. Induction of associative olfactory memory by targeted activation of single olfactory neurons in Drosophila larvae.

    Science.gov (United States)

    Honda, Takato; Lee, Chi-Yu; Yoshida-Kasikawa, Maki; Honjo, Ken; Furukubo-Tokunaga, Katsuo

    2014-04-25

    It has been postulated that associative memory is formed by at least two sets of external stimuli, CS and US, that are transmitted to the memory centers by distinctive conversing pathways. However, whether associative memory can be induced by the activation of only the olfactory CS and a biogenic amine-mediated US pathways remains to be elucidated. In this study, we substituted the reward signals with dTrpA1-mediated thermogenetic activation of octopaminergic neurons and the odor signals by ChR2-mediated optical activation of a specific class of olfactory neurons. We show that targeted activation of the olfactory receptor and the octopaminergic neurons is indeed sufficient for the formation of associative olfactory memory in the larval brain. We also show that targeted stimulation of only a single type of olfactory receptor neurons is sufficient to induce olfactory memory that is indistinguishable from natural memory induced by the activation of multiple olfactory receptor neurons.

  14. A model based approach in observing the activity of neuronal populations for the prediction of epileptic seizures

    International Nuclear Information System (INIS)

    Chong, M.S.; Nesic, D.; Kuhlmann, L.; Postoyan, R.; Varsavsky, A.; Cook, M.

    2010-01-01

    Full text: Epilepsy is a common neurological disease that affects 0.5-1 % of the world's population. In cases where known treatments cannot achieve complete recovery, seizure prediction is essential so that preventive measures can be undertaken to prevent resultant injury. The elcctroencephalogram (EEG) is a widely used diagnostic tool for epilepsy. However, the EEG does not provide a detailed view of the underlying seizure causing neuronal mechanisms. Knowing the dynamics of the neuronal population is useful because tracking the evolution of the neuronal mechanisms will allow us to track the brain's progression from interictal to ictal state. Wendling and colleagues proposed a parameterised mathematical model that represents the activity of interconnected neuronal populations. By modifying the parameters, this model is able to reproduce signals that are very similar to the real EEG depicting commonly observed patterns during interictal and ictal periods. The transition from non-seizure to seizure activity, as seen in the EEG. is hypothesised to be due to the impairment of inhibition. Using Wendling's model, we designed a deterministic nonlinear estimator to recover the average membrane potential of the neuronal populations from a single channel EEG signal. for any fixed and known parameter values. Our nonlinear estimator is analytically proven to asymptotically converge to the true state of the model and illustrated in simulations. We were able to computationally observe the dynamics of the three neuronal populations described in the model: excitatory, fast and slow inhibitory populations. This forms a first step towards the prediction of epileptic seiwres. (author)

  15. Modeling task-specific neuronal ensembles improves decoding of grasp

    Science.gov (United States)

    Smith, Ryan J.; Soares, Alcimar B.; Rouse, Adam G.; Schieber, Marc H.; Thakor, Nitish V.

    2018-06-01

    Objective. Dexterous movement involves the activation and coordination of networks of neuronal populations across multiple cortical regions. Attempts to model firing of individual neurons commonly treat the firing rate as directly modulating with motor behavior. However, motor behavior may additionally be associated with modulations in the activity and functional connectivity of neurons in a broader ensemble. Accounting for variations in neural ensemble connectivity may provide additional information about the behavior being performed. Approach. In this study, we examined neural ensemble activity in primary motor cortex (M1) and premotor cortex (PM) of two male rhesus monkeys during performance of a center-out reach, grasp and manipulate task. We constructed point process encoding models of neuronal firing that incorporated task-specific variations in the baseline firing rate as well as variations in functional connectivity with the neural ensemble. Models were evaluated both in terms of their encoding capabilities and their ability to properly classify the grasp being performed. Main results. Task-specific ensemble models correctly predicted the performed grasp with over 95% accuracy and were shown to outperform models of neuronal activity that assume only a variable baseline firing rate. Task-specific ensemble models exhibited superior decoding performance in 82% of units in both monkeys (p  <  0.01). Inclusion of ensemble activity also broadly improved the ability of models to describe observed spiking. Encoding performance of task-specific ensemble models, measured by spike timing predictability, improved upon baseline models in 62% of units. Significance. These results suggest that additional discriminative information about motor behavior found in the variations in functional connectivity of neuronal ensembles located in motor-related cortical regions is relevant to decode complex tasks such as grasping objects, and may serve the basis for more

  16. Sleep Dependent Synaptic Down-Selection (II: Single Neuron Level Benefits for Matching, Selectivity, and Specificity

    Directory of Open Access Journals (Sweden)

    Atif eHashmi

    2013-10-01

    Full Text Available In a companion paper (Nere et al., this volume, we used computer simulations to show that a strategy of activity-dependent, on-line net synaptic potentiation during wake, followed by off-line synaptic depression during sleep, can provide a parsimonious account for several memory benefits of sleep at the systems level, including the consolidation of procedural and declarative memories, gist extraction, and integration of new with old memories. In this paper, we consider the theoretical benefits of this two-step process at the single neuron level and employ the theoretical notion of Matching between brain and environment to measure how this process increases the ability of the neuron to capture regularities in the environment and model them internally. We show that down-selection during sleep is beneficial for increasing or restoring Matching after learning, after integrating new with old memories, and after forgetting irrelevant material. By contrast, alternative schemes, such as additional potentiation in wake, potentiation in sleep, or synaptic renormalization in wake, decrease Matching. We also argue that, by selecting appropriate loops through the brain that tie feedforward synapses with feedback ones in the same dendritic domain, different subsets of neurons can learn to specialize for different contingencies and form sequences of nested perception-action loops. By potentiating such loops when interacting with the environment in wake, and depressing them when disconnected from the environment in sleep, neurons can learn to match the long-term statistical structure of the environment while avoiding spurious modes of functioning and catastrophic interference. Finally, such a two-step process has the additional benefit of desaturating the neuron's ability to learn and of maintaining cellular homeostasis. Thus, sleep-dependent synaptic renormalization offers a parsimonious account for both cellular and systems-level effects of sleep on learning

  17. Hybrid Scheme for Modeling Local Field Potentials from Point-Neuron Networks.

    Science.gov (United States)

    Hagen, Espen; Dahmen, David; Stavrinou, Maria L; Lindén, Henrik; Tetzlaff, Tom; van Albada, Sacha J; Grün, Sonja; Diesmann, Markus; Einevoll, Gaute T

    2016-12-01

    With rapidly advancing multi-electrode recording technology, the local field potential (LFP) has again become a popular measure of neuronal activity in both research and clinical applications. Proper understanding of the LFP requires detailed mathematical modeling incorporating the anatomical and electrophysiological features of neurons near the recording electrode, as well as synaptic inputs from the entire network. Here we propose a hybrid modeling scheme combining efficient point-neuron network models with biophysical principles underlying LFP generation by real neurons. The LFP predictions rely on populations of network-equivalent multicompartment neuron models with layer-specific synaptic connectivity, can be used with an arbitrary number of point-neuron network populations, and allows for a full separation of simulated network dynamics and LFPs. We apply the scheme to a full-scale cortical network model for a ∼1 mm 2 patch of primary visual cortex, predict laminar LFPs for different network states, assess the relative LFP contribution from different laminar populations, and investigate effects of input correlations and neuron density on the LFP. The generic nature of the hybrid scheme and its public implementation in hybridLFPy form the basis for LFP predictions from other and larger point-neuron network models, as well as extensions of the current application with additional biological detail. © The Author 2016. Published by Oxford University Press.

  18. Beyond the frontiers of neuronal types

    Science.gov (United States)

    Battaglia, Demian; Karagiannis, Anastassios; Gallopin, Thierry; Gutch, Harold W.; Cauli, Bruno

    2012-01-01

    Cortical neurons and, particularly, inhibitory interneurons display a large diversity of morphological, synaptic, electrophysiological, and molecular properties, as well as diverse embryonic origins. Various authors have proposed alternative classification schemes that rely on the concomitant observation of several multimodal features. However, a broad variability is generally observed even among cells that are grouped into a same class. Furthermore, the attribution of specific neurons to a single defined class is often difficult, because individual properties vary in a highly graded fashion, suggestive of continua of features between types. Going beyond the description of representative traits of distinct classes, we focus here on the analysis of atypical cells. We introduce a novel paradigm for neuronal type classification, assuming explicitly the existence of a structured continuum of diversity. Our approach, grounded on the theory of fuzzy sets, identifies a small optimal number of model archetypes. At the same time, it quantifies the degree of similarity between these archetypes and each considered neuron. This allows highlighting archetypal cells, which bear a clear similarity to a single model archetype, and edge cells, which manifest a convergence of traits from multiple archetypes. PMID:23403725

  19. Beyond the frontiers of neuronal types

    Directory of Open Access Journals (Sweden)

    Demian eBattaglia

    2013-02-01

    Full Text Available Cortical neurons and, particularly, inhibitory interneurons display a large diversity of morphological, synaptic, electrophysiological and molecular properties, as well as diverse embryonic origins. Various authors have proposed alternative classification schemes that rely on the concomitant observation of several multimodal features. However, a broad variability is generally observed even among cells that are grouped into a same class. Furthermore, the attribution of specific neurons to a single defined class is often difficult, because individual properties vary in a highly graded fashion, suggestive of continua of features between types. Going beyond the description of representative traits of distinct classes, we focus here on the analysis of atypical cells. We introduce a novel paradigm for neuronal type classification, assuming explicitly the existence of a structured continuum of diversity. Our approach, grounded on the theory of fuzzy sets, identifies a small optimal number of model archetypes. At the same time, it quantifies the degree of similarity between these archetypes and each considered neuron. This allows highlighting archetypal cells, which bear a clear similarity to a single model archetype, and edge cells, which manifest a convergence of traits from multiple archetypes.

  20. Diversity of bilateral synaptic assemblies for binaural computation in midbrain single neurons.

    Science.gov (United States)

    He, Na; Kong, Lingzhi; Lin, Tao; Wang, Shaohui; Liu, Xiuping; Qi, Jiyao; Yan, Jun

    2017-11-01

    Binaural hearing confers many beneficial functions but our understanding of its underlying neural substrates is limited. This study examines the bilateral synaptic assemblies and binaural computation (or integration) in the central nucleus of the inferior colliculus (ICc) of the auditory midbrain, a key convergent center. Using in-vivo whole-cell patch-clamp, the excitatory and inhibitory postsynaptic potentials (EPSPs/IPSPs) of single ICc neurons to contralateral, ipsilateral and bilateral stimulation were recorded. According to the contralateral and ipsilateral EPSP/IPSP, 7 types of bilateral synaptic assemblies were identified. These include EPSP-EPSP (EE), E-IPSP (EI), E-no response (EO), II, IE, IO and complex-mode (CM) neurons. The CM neurons showed frequency- and/or amplitude-dependent EPSPs/IPSPs to contralateral or ipsilateral stimulation. Bilateral stimulation induced EPSPs/IPSPs that could be larger than (facilitation), similar to (ineffectiveness) or smaller than (suppression) those induced by contralateral stimulation. Our findings have allowed our group to characterize novel neural circuitry for binaural computation in the midbrain. Copyright © 2017 Elsevier B.V. All rights reserved.

  1. Stochastic optimal control of single neuron spike trains

    DEFF Research Database (Denmark)

    Iolov, Alexandre; Ditlevsen, Susanne; Longtin, Andrë

    2014-01-01

    stimulation of a neuron to achieve a target spike train under the physiological constraint to not damage tissue. Approach. We pose a stochastic optimal control problem to precisely specify the spike times in a leaky integrate-and-fire (LIF) model of a neuron with noise assumed to be of intrinsic or synaptic...... origin. In particular, we allow for the noise to be of arbitrary intensity. The optimal control problem is solved using dynamic programming when the controller has access to the voltage (closed-loop control), and using a maximum principle for the transition density when the controller only has access...... to the spike times (open-loop control). Main results. We have developed a stochastic optimal control algorithm to obtain precise spike times. It is applicable in both the supra-threshold and sub-threshold regimes, under open-loop and closed-loop conditions and with an arbitrary noise intensity; the accuracy...

  2. Novel animal model defines genetic contributions for neuron-to-neuron transfer of α-synuclein.

    Science.gov (United States)

    Tyson, Trevor; Senchuk, Megan; Cooper, Jason F; George, Sonia; Van Raamsdonk, Jeremy M; Brundin, Patrik

    2017-08-08

    Cell-to-cell spreading of misfolded α-synuclein (α-syn) is suggested to contribute to the progression of neuropathology in Parkinson's disease (PD). Compelling evidence supports the hypothesis that misfolded α-syn transmits from neuron-to-neuron and seeds aggregation of the protein in the recipient cells. Furthermore, α-syn frequently appears to propagate in the brains of PD patients following a stereotypic pattern consistent with progressive spreading along anatomical pathways. We have generated a C. elegans model that mirrors this progression and allows us to monitor α-syn neuron-to-neuron transmission in a live animal over its lifespan. We found that modulation of autophagy or exo/endocytosis, affects α-syn transfer. Furthermore, we demonstrate that silencing C. elegans orthologs of PD-related genes also increases the accumulation of α-syn. This novel worm model is ideal for screening molecules and genes to identify those that modulate prion-like spreading of α-syn in order to target novel strategies for disease modification in PD and other synucleinopathies.

  3. Complete Neuron-Astrocyte Interaction Model: Digital Multiplierless Design and Networking Mechanism.

    Science.gov (United States)

    Haghiri, Saeed; Ahmadi, Arash; Saif, Mehrdad

    2017-02-01

    Glial cells, also known as neuroglia or glia, are non-neuronal cells providing support and protection for neurons in the central nervous system (CNS). They also act as supportive cells in the brain. Among a variety of glial cells, the star-shaped glial cells, i.e., astrocytes, are the largest cell population in the brain. The important role of astrocyte such as neuronal synchronization, synaptic information regulation, feedback to neural activity and extracellular regulation make the astrocytes play a vital role in brain disease. This paper presents a modified complete neuron-astrocyte interaction model that is more suitable for efficient and large scale biological neural network realization on digital platforms. Simulation results show that the modified complete interaction model can reproduce biological-like behavior of the original neuron-astrocyte mechanism. The modified interaction model is investigated in terms of digital realization feasibility and cost targeting a low cost hardware implementation. Networking behavior of this interaction is investigated and compared between two cases: i) the neuron spiking mechanism without astrocyte effects, and ii) the effect of astrocyte in regulating the neurons behavior and synaptic transmission via controlling the LTP and LTD processes. Hardware implementation on FPGA shows that the modified model mimics the main mechanism of neuron-astrocyte communication with higher performance and considerably lower hardware overhead cost compared with the original interaction model.

  4. A new model of strabismic amblyopia: Loss of spatial acuity due to increased temporal dispersion of geniculate X-cell afferents on to cortical neurons.

    Science.gov (United States)

    Crewther, D P; Crewther, S G

    2015-09-01

    Although the neural locus of strabismic amblyopia has been shown to lie at the first site of binocular integration, first in cat and then in primate, an adequate mechanism is still lacking. Here we hypothesise that increased temporal dispersion of LGN X-cell afferents driven by the deviating eye onto single cortical neurons may provide a neural mechanism for strabismic amblyopia. This idea was investigated via single cell extracellular recordings of 93 X and 50 Y type LGN neurons from strabismic and normal cats. Both X and Y neurons driven by the non-deviating eye showed shorter latencies than those driven by either the strabismic or normal eyes. Also the mean latency difference between X and Y neurons was much greater for the strabismic cells compared with the other two groups. The incidence of lagged X-cells driven by the deviating eye of the strabismic cats was higher than that of LGN X-cells from normal animals. Remarkably, none of the cells recorded from the laminae driven by the non-deviating eye were of the lagged class. A simple computational model was constructed in which a mixture of lagged and non-lagged afferents converge on to single cortical neurons. Model cut-off spatial frequencies to a moving grating stimulus were sensitive to the temporal dispersion of the geniculate afferents. Thus strabismic amblyopia could be viewed as a lack of developmental tuning of geniculate lags for neurons driven by the amblyopic eye. Monocular control of fixation by the non-deviating eye is associated with reduced incidence of lagged neurons, suggesting that in normal vision, lagged neurons might play a role in maintaining binocular connections for cortical neurons. Copyright © 2014 Elsevier Ltd. All rights reserved.

  5. Stochastic resonance in models of neuronal ensembles

    International Nuclear Information System (INIS)

    Chialvo, D.R.; Longtin, A.; Mueller-Gerkin, J.

    1997-01-01

    Two recently suggested mechanisms for the neuronal encoding of sensory information involving the effect of stochastic resonance with aperiodic time-varying inputs are considered. It is shown, using theoretical arguments and numerical simulations, that the nonmonotonic behavior with increasing noise of the correlation measures used for the so-called aperiodic stochastic resonance (ASR) scenario does not rely on the cooperative effect typical of stochastic resonance in bistable and excitable systems. Rather, ASR with slowly varying signals is more properly interpreted as linearization by noise. Consequently, the broadening of the open-quotes resonance curveclose quotes in the multineuron stochastic resonance without tuning scenario can also be explained by this linearization. Computation of the input-output correlation as a function of both signal frequency and noise for the model system further reveals conditions where noise-induced firing with aperiodic inputs will benefit from stochastic resonance rather than linearization by noise. Thus, our study clarifies the tuning requirements for the optimal transduction of subthreshold aperiodic signals. It also shows that a single deterministic neuron can perform as well as a network when biased into a suprathreshold regime. Finally, we show that the inclusion of a refractory period in the spike-detection scheme produces a better correlation between instantaneous firing rate and input signal. copyright 1997 The American Physical Society

  6. Modeling the Development of Goal-Specificity in Mirror Neurons.

    Science.gov (United States)

    Thill, Serge; Svensson, Henrik; Ziemke, Tom

    2011-12-01

    Neurophysiological studies have shown that parietal mirror neurons encode not only actions but also the goal of these actions. Although some mirror neurons will fire whenever a certain action is perceived (goal-independently), most will only fire if the motion is perceived as part of an action with a specific goal. This result is important for the action-understanding hypothesis as it provides a potential neurological basis for such a cognitive ability. It is also relevant for the design of artificial cognitive systems, in particular robotic systems that rely on computational models of the mirror system in their interaction with other agents. Yet, to date, no computational model has explicitly addressed the mechanisms that give rise to both goal-specific and goal-independent parietal mirror neurons. In the present paper, we present a computational model based on a self-organizing map, which receives artificial inputs representing information about both the observed or executed actions and the context in which they were executed. We show that the map develops a biologically plausible organization in which goal-specific mirror neurons emerge. We further show that the fundamental cause for both the appearance and the number of goal-specific neurons can be found in geometric relationships between the different inputs to the map. The results are important to the action-understanding hypothesis as they provide a mechanism for the emergence of goal-specific parietal mirror neurons and lead to a number of predictions: (1) Learning of new goals may mostly reassign existing goal-specific neurons rather than recruit new ones; (2) input differences between executed and observed actions can explain observed corresponding differences in the number of goal-specific neurons; and (3) the percentage of goal-specific neurons may differ between motion primitives.

  7. Lateral Information Processing by Spiking Neurons: A Theoretical Model of the Neural Correlate of Consciousness

    Directory of Open Access Journals (Sweden)

    Marc Ebner

    2011-01-01

    Full Text Available Cognitive brain functions, for example, sensory perception, motor control and learning, are understood as computation by axonal-dendritic chemical synapses in networks of integrate-and-fire neurons. Cognitive brain functions may occur either consciously or nonconsciously (on “autopilot”. Conscious cognition is marked by gamma synchrony EEG, mediated largely by dendritic-dendritic gap junctions, sideways connections in input/integration layers. Gap-junction-connected neurons define a sub-network within a larger neural network. A theoretical model (the “conscious pilot” suggests that as gap junctions open and close, a gamma-synchronized subnetwork, or zone moves through the brain as an executive agent, converting nonconscious “auto-pilot” cognition to consciousness, and enhancing computation by coherent processing and collective integration. In this study we implemented sideways “gap junctions” in a single-layer artificial neural network to perform figure/ground separation. The set of neurons connected through gap junctions form a reconfigurable resistive grid or sub-network zone. In the model, outgoing spikes are temporally integrated and spatially averaged using the fixed resistive grid set up by neurons of similar function which are connected through gap-junctions. This spatial average, essentially a feedback signal from the neuron's output, determines whether particular gap junctions between neurons will open or close. Neurons connected through open gap junctions synchronize their output spikes. We have tested our gap-junction-defined sub-network in a one-layer neural network on artificial retinal inputs using real-world images. Our system is able to perform figure/ground separation where the laterally connected sub-network of neurons represents a perceived object. Even though we only show results for visual stimuli, our approach should generalize to other modalities. The system demonstrates a moving sub-network zone of

  8. Coherent and intermittent ensemble oscillations emerge from networks of irregular spiking neurons.

    Science.gov (United States)

    Hoseini, Mahmood S; Wessel, Ralf

    2016-01-01

    Local field potential (LFP) recordings from spatially distant cortical circuits reveal episodes of coherent gamma oscillations that are intermittent, and of variable peak frequency and duration. Concurrently, single neuron spiking remains largely irregular and of low rate. The underlying potential mechanisms of this emergent network activity have long been debated. Here we reproduce such intermittent ensemble oscillations in a model network, consisting of excitatory and inhibitory model neurons with the characteristics of regular-spiking (RS) pyramidal neurons, and fast-spiking (FS) and low-threshold spiking (LTS) interneurons. We find that fluctuations in the external inputs trigger reciprocally connected and irregularly spiking RS and FS neurons in episodes of ensemble oscillations, which are terminated by the recruitment of the LTS population with concurrent accumulation of inhibitory conductance in both RS and FS neurons. The model qualitatively reproduces experimentally observed phase drift, oscillation episode duration distributions, variation in the peak frequency, and the concurrent irregular single-neuron spiking at low rate. Furthermore, consistent with previous experimental studies using optogenetic manipulation, periodic activation of FS, but not RS, model neurons causes enhancement of gamma oscillations. In addition, increasing the coupling between two model networks from low to high reveals a transition from independent intermittent oscillations to coherent intermittent oscillations. In conclusion, the model network suggests biologically plausible mechanisms for the generation of episodes of coherent intermittent ensemble oscillations with irregular spiking neurons in cortical circuits. Copyright © 2016 the American Physiological Society.

  9. Towards a theory of cortical columns: From spiking neurons to interacting neural populations of finite size.

    Science.gov (United States)

    Schwalger, Tilo; Deger, Moritz; Gerstner, Wulfram

    2017-04-01

    Neural population equations such as neural mass or field models are widely used to study brain activity on a large scale. However, the relation of these models to the properties of single neurons is unclear. Here we derive an equation for several interacting populations at the mesoscopic scale starting from a microscopic model of randomly connected generalized integrate-and-fire neuron models. Each population consists of 50-2000 neurons of the same type but different populations account for different neuron types. The stochastic population equations that we find reveal how spike-history effects in single-neuron dynamics such as refractoriness and adaptation interact with finite-size fluctuations on the population level. Efficient integration of the stochastic mesoscopic equations reproduces the statistical behavior of the population activities obtained from microscopic simulations of a full spiking neural network model. The theory describes nonlinear emergent dynamics such as finite-size-induced stochastic transitions in multistable networks and synchronization in balanced networks of excitatory and inhibitory neurons. The mesoscopic equations are employed to rapidly integrate a model of a cortical microcircuit consisting of eight neuron types, which allows us to predict spontaneous population activities as well as evoked responses to thalamic input. Our theory establishes a general framework for modeling finite-size neural population dynamics based on single cell and synapse parameters and offers an efficient approach to analyzing cortical circuits and computations.

  10. Biophysical synaptic dynamics in an analog VLSI network of Hodgkin-Huxley neurons.

    Science.gov (United States)

    Yu, Theodore; Cauwenberghs, Gert

    2009-01-01

    We study synaptic dynamics in a biophysical network of four coupled spiking neurons implemented in an analog VLSI silicon microchip. The four neurons implement a generalized Hodgkin-Huxley model with individually configurable rate-based kinetics of opening and closing of Na+ and K+ ion channels. The twelve synapses implement a rate-based first-order kinetic model of neurotransmitter and receptor dynamics, accounting for NMDA and non-NMDA type chemical synapses. The implemented models on the chip are fully configurable by 384 parameters accounting for conductances, reversal potentials, and pre/post-synaptic voltage-dependence of the channel kinetics. We describe the models and present experimental results from the chip characterizing single neuron dynamics, single synapse dynamics, and multi-neuron network dynamics showing phase-locking behavior as a function of synaptic coupling strength. The 3mm x 3mm microchip consumes 1.29 mW power making it promising for applications including neuromorphic modeling and neural prostheses.

  11. Thalamic neuron models encode stimulus information by burst-size modulation

    Directory of Open Access Journals (Sweden)

    Daniel Henry Elijah

    2015-09-01

    Full Text Available Thalamic neurons have been long assumed to fire in tonic mode during perceptive states, and in burst mode during sleep and unconsciousness. However, recent evidence suggests that bursts may also be relevant in the encoding of sensory information. Here we explore the neural code of such thalamic bursts. In order to assess whether the burst code is generic or whether it depends on the detailed properties of each bursting neuron, we analyzed two neuron models incorporating different levels of biological detail. One of the models contained no information of the biophysical processes entailed in spike generation, and described neuron activity at a phenomenological level. The second model represented the evolution of the individual ionic conductances involved in spiking and bursting, and required a large number of parameters. We analyzed the models' input selectivity using reverse correlation methods and information theory. We found that n-spike bursts from both models transmit information by modulating their spike count in response to changes to instantaneous input features, such as slope, phase, amplitude, etc. The stimulus feature that is most efficiently encoded by bursts, however, need not coincide with one of such classical features. We therefore searched for the optimal feature among all those that could be expressed as a linear transformation of the time-dependent input current. We found that bursting neurons transmitted 6 times more information about such more general features. The relevant events in the stimulus were located in a time window spanning ~100 ms before and ~20 ms after burst onset. Most importantly, the neural code employed by the simple and the biologically realistic models was largely the same, implying that the simple thalamic neuron model contains the essential ingredients that account for the computational properties of the thalamic burst code. Thus, our results suggest the n-spike burst code is a general property of

  12. Thalamic neuron models encode stimulus information by burst-size modulation.

    Science.gov (United States)

    Elijah, Daniel H; Samengo, Inés; Montemurro, Marcelo A

    2015-01-01

    Thalamic neurons have been long assumed to fire in tonic mode during perceptive states, and in burst mode during sleep and unconsciousness. However, recent evidence suggests that bursts may also be relevant in the encoding of sensory information. Here, we explore the neural code of such thalamic bursts. In order to assess whether the burst code is generic or whether it depends on the detailed properties of each bursting neuron, we analyzed two neuron models incorporating different levels of biological detail. One of the models contained no information of the biophysical processes entailed in spike generation, and described neuron activity at a phenomenological level. The second model represented the evolution of the individual ionic conductances involved in spiking and bursting, and required a large number of parameters. We analyzed the models' input selectivity using reverse correlation methods and information theory. We found that n-spike bursts from both models transmit information by modulating their spike count in response to changes to instantaneous input features, such as slope, phase, amplitude, etc. The stimulus feature that is most efficiently encoded by bursts, however, need not coincide with one of such classical features. We therefore searched for the optimal feature among all those that could be expressed as a linear transformation of the time-dependent input current. We found that bursting neurons transmitted 6 times more information about such more general features. The relevant events in the stimulus were located in a time window spanning ~100 ms before and ~20 ms after burst onset. Most importantly, the neural code employed by the simple and the biologically realistic models was largely the same, implying that the simple thalamic neuron model contains the essential ingredients that account for the computational properties of the thalamic burst code. Thus, our results suggest the n-spike burst code is a general property of thalamic neurons.

  13. Neurons other than motor neurons in motor neuron disease.

    Science.gov (United States)

    Ruffoli, Riccardo; Biagioni, Francesca; Busceti, Carla L; Gaglione, Anderson; Ryskalin, Larisa; Gambardella, Stefano; Frati, Alessandro; Fornai, Francesco

    2017-11-01

    Amyotrophic lateral sclerosis (ALS) is typically defined by a loss of motor neurons in the central nervous system. Accordingly, morphological analysis for decades considered motor neurons (in the cortex, brainstem and spinal cord) as the neuronal population selectively involved in ALS. Similarly, this was considered the pathological marker to score disease severity ex vivo both in patients and experimental models. However, the concept of non-autonomous motor neuron death was used recently to indicate the need for additional cell types to produce motor neuron death in ALS. This means that motor neuron loss occurs only when they are connected with other cell types. This concept originally emphasized the need for resident glia as well as non-resident inflammatory cells. Nowadays, the additional role of neurons other than motor neurons emerged in the scenario to induce non-autonomous motor neuron death. In fact, in ALS neurons diverse from motor neurons are involved. These cells play multiple roles in ALS: (i) they participate in the chain of events to produce motor neuron loss; (ii) they may even degenerate more than and before motor neurons. In the present manuscript evidence about multi-neuronal involvement in ALS patients and experimental models is discussed. Specific sub-classes of neurons in the whole spinal cord are reported either to degenerate or to trigger neuronal degeneration, thus portraying ALS as a whole spinal cord disorder rather than a disease affecting motor neurons solely. This is associated with a novel concept in motor neuron disease which recruits abnormal mechanisms of cell to cell communication.

  14. Single-cell analysis of peptide expression and electrophysiology of right parietal neurons involved in male copulation behavior of a simultaneous hermaphrodite.

    Science.gov (United States)

    El Filali, Z; de Boer, P A C M; Pieneman, A W; de Lange, R P J; Jansen, R F; Ter Maat, A; van der Schors, R C; Li, K W; van Straalen, N M; Koene, J M

    2015-12-01

    Male copulation is a complex behavior that requires coordinated communication between the nervous system and the peripheral reproductive organs involved in mating. In hermaphroditic animals, such as the freshwater snail Lymnaea stagnalis, this complexity increases since the animal can behave both as male and female. The performance of the sexual role as a male is coordinated via a neuronal communication regulated by many peptidergic neurons, clustered in the cerebral and pedal ganglia and dispersed in the pleural and parietal ganglia. By combining single-cell matrix-assisted laser mass spectrometry with retrograde staining and electrophysiology, we analyzed neuropeptide expression of single neurons of the right parietal ganglion and their axonal projections into the penial nerve. Based on the neuropeptide profile of these neurons, we were able to reconstruct a chemical map of the right parietal ganglion revealing a striking correlation with the earlier electrophysiological and neuroanatomical studies. Neurons can be divided into two main groups: (i) neurons that express heptapeptides and (ii) neurons that do not. The neuronal projection of the different neurons into the penial nerve reveals a pattern where (spontaneous) activity is related to branching pattern. This heterogeneity in both neurochemical anatomy and branching pattern of the parietal neurons reflects the complexity of the peptidergic neurotransmission involved in the regulation of male mating behavior in this simultaneous hermaphrodite.

  15. A distance constrained synaptic plasticity model of C. elegans neuronal network

    Science.gov (United States)

    Badhwar, Rahul; Bagler, Ganesh

    2017-03-01

    Brain research has been driven by enquiry for principles of brain structure organization and its control mechanisms. The neuronal wiring map of C. elegans, the only complete connectome available till date, presents an incredible opportunity to learn basic governing principles that drive structure and function of its neuronal architecture. Despite its apparently simple nervous system, C. elegans is known to possess complex functions. The nervous system forms an important underlying framework which specifies phenotypic features associated to sensation, movement, conditioning and memory. In this study, with the help of graph theoretical models, we investigated the C. elegans neuronal network to identify network features that are critical for its control. The 'driver neurons' are associated with important biological functions such as reproduction, signalling processes and anatomical structural development. We created 1D and 2D network models of C. elegans neuronal system to probe the role of features that confer controllability and small world nature. The simple 1D ring model is critically poised for the number of feed forward motifs, neuronal clustering and characteristic path-length in response to synaptic rewiring, indicating optimal rewiring. Using empirically observed distance constraint in the neuronal network as a guiding principle, we created a distance constrained synaptic plasticity model that simultaneously explains small world nature, saturation of feed forward motifs as well as observed number of driver neurons. The distance constrained model suggests optimum long distance synaptic connections as a key feature specifying control of the network.

  16. The Limited Utility of Multiunit Data in Differentiating Neuronal Population Activity.

    Directory of Open Access Journals (Sweden)

    Corey J Keller

    Full Text Available To date, single neuron recordings remain the gold standard for monitoring the activity of neuronal populations. Since obtaining single neuron recordings is not always possible, high frequency or 'multiunit activity' (MUA is often used as a surrogate. Although MUA recordings allow one to monitor the activity of a large number of neurons, they do not allow identification of specific neuronal subtypes, the knowledge of which is often critical for understanding electrophysiological processes. Here, we explored whether prior knowledge of the single unit waveform of specific neuron types is sufficient to permit the use of MUA to monitor and distinguish differential activity of individual neuron types. We used an experimental and modeling approach to determine if components of the MUA can monitor medium spiny neurons (MSNs and fast-spiking interneurons (FSIs in the mouse dorsal striatum. We demonstrate that when well-isolated spikes are recorded, the MUA at frequencies greater than 100Hz is correlated with single unit spiking, highly dependent on the waveform of each neuron type, and accurately reflects the timing and spectral signature of each neuron. However, in the absence of well-isolated spikes (the norm in most MUA recordings, the MUA did not typically contain sufficient information to permit accurate prediction of the respective population activity of MSNs and FSIs. Thus, even under ideal conditions for the MUA to reliably predict the moment-to-moment activity of specific local neuronal ensembles, knowledge of the spike waveform of the underlying neuronal populations is necessary, but not sufficient.

  17. Neuronal avalanches and learning

    Energy Technology Data Exchange (ETDEWEB)

    Arcangelis, Lucilla de, E-mail: dearcangelis@na.infn.it [Department of Information Engineering and CNISM, Second University of Naples, 81031 Aversa (Italy)

    2011-05-01

    Networks of living neurons represent one of the most fascinating systems of biology. If the physical and chemical mechanisms at the basis of the functioning of a single neuron are quite well understood, the collective behaviour of a system of many neurons is an extremely intriguing subject. Crucial ingredient of this complex behaviour is the plasticity property of the network, namely the capacity to adapt and evolve depending on the level of activity. This plastic ability is believed, nowadays, to be at the basis of learning and memory in real brains. Spontaneous neuronal activity has recently shown features in common to other complex systems. Experimental data have, in fact, shown that electrical information propagates in a cortex slice via an avalanche mode. These avalanches are characterized by a power law distribution for the size and duration, features found in other problems in the context of the physics of complex systems and successful models have been developed to describe their behaviour. In this contribution we discuss a statistical mechanical model for the complex activity in a neuronal network. The model implements the main physiological properties of living neurons and is able to reproduce recent experimental results. Then, we discuss the learning abilities of this neuronal network. Learning occurs via plastic adaptation of synaptic strengths by a non-uniform negative feedback mechanism. The system is able to learn all the tested rules, in particular the exclusive OR (XOR) and a random rule with three inputs. The learning dynamics exhibits universal features as function of the strength of plastic adaptation. Any rule could be learned provided that the plastic adaptation is sufficiently slow.

  18. Neuronal avalanches and learning

    International Nuclear Information System (INIS)

    Arcangelis, Lucilla de

    2011-01-01

    Networks of living neurons represent one of the most fascinating systems of biology. If the physical and chemical mechanisms at the basis of the functioning of a single neuron are quite well understood, the collective behaviour of a system of many neurons is an extremely intriguing subject. Crucial ingredient of this complex behaviour is the plasticity property of the network, namely the capacity to adapt and evolve depending on the level of activity. This plastic ability is believed, nowadays, to be at the basis of learning and memory in real brains. Spontaneous neuronal activity has recently shown features in common to other complex systems. Experimental data have, in fact, shown that electrical information propagates in a cortex slice via an avalanche mode. These avalanches are characterized by a power law distribution for the size and duration, features found in other problems in the context of the physics of complex systems and successful models have been developed to describe their behaviour. In this contribution we discuss a statistical mechanical model for the complex activity in a neuronal network. The model implements the main physiological properties of living neurons and is able to reproduce recent experimental results. Then, we discuss the learning abilities of this neuronal network. Learning occurs via plastic adaptation of synaptic strengths by a non-uniform negative feedback mechanism. The system is able to learn all the tested rules, in particular the exclusive OR (XOR) and a random rule with three inputs. The learning dynamics exhibits universal features as function of the strength of plastic adaptation. Any rule could be learned provided that the plastic adaptation is sufficiently slow.

  19. Silencing neuronal mutant androgen receptor in a mouse model of spinal and bulbar muscular atrophy.

    Science.gov (United States)

    Sahashi, Kentaro; Katsuno, Masahisa; Hung, Gene; Adachi, Hiroaki; Kondo, Naohide; Nakatsuji, Hideaki; Tohnai, Genki; Iida, Madoka; Bennett, C Frank; Sobue, Gen

    2015-11-01

    Spinal and bulbar muscular atrophy (SBMA), an adult-onset neurodegenerative disease that affects males, results from a CAG triplet repeat/polyglutamine expansions in the androgen receptor (AR) gene. Patients develop progressive muscular weakness and atrophy, and no effective therapy is currently available. The tissue-specific pathogenesis, especially relative pathological contributions between degenerative motor neurons and muscles, remains inconclusive. Though peripheral pathology in skeletal muscle caused by toxic AR protein has been recently reported to play a pivotal role in the pathogenesis of SBMA using mouse models, the role of motor neuron degeneration in SBMA has not been rigorously investigated. Here, we exploited synthetic antisense oligonucleotides to inhibit the RNA levels of mutant AR in the central nervous system (CNS) and explore its therapeutic effects in our SBMA mouse model that harbors a mutant AR gene with 97 CAG expansions and characteristic SBMA-like neurogenic phenotypes. A single intracerebroventricular administration of the antisense oligonucleotides in the presymptomatic phase efficiently suppressed the mutant gene expression in the CNS, and delayed the onset and progression of motor dysfunction, improved body weight gain and survival with the amelioration of neuronal histopathology in motor units such as spinal motor neurons, neuromuscular junctions and skeletal muscle. These findings highlight the importance of the neurotoxicity of mutant AR protein in motor neurons as a therapeutic target. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  20. Co-release of glutamate and GABA from single vesicles in GABAergic neurons exogenously expressing VGLUT3

    Directory of Open Access Journals (Sweden)

    Johannes eZimmermann

    2015-09-01

    Full Text Available The identity of the vesicle neurotransmitter transporter expressed by a neuron largely corresponds with the primary neurotransmitter that cell releases. However, the vesicular glutamate transporter subtype 3 (VGLUT3 is mainly expressed in non-glutamatergic neurons, including cholinergic, serotonergic, or GABAergic neurons. Though a functional role for glutamate release from these non-glutamatergic neurons has been demonstrated, the interplay between VGLUT3 and the neuron’s characteristic neurotransmitter transporter, particularly in the case of GABAergic neurons, at the synaptic and vesicular level is less clear. In this study, we explore how exogenous expression of VGLUT3 in striatal GABAergic neurons affects the packaging and release of glutamate and GABA in synaptic vesicles. We found that VGLUT3 expression in isolated, autaptic GABAergic neurons leads to action potential evoked release of glutamate. Under these conditions, glutamate and GABA could be packaged together in single vesicles release either spontaneously or asynchronously. However, the presence of glutamate in GABAergic vesicles did not affect uptake of GABA itself, suggesting a lack of synergy in vesicle filling for these transmitters. Finally, we found postsynaptic detection of glutamate released from GABAergic terminals difficult when bona fide glutamatergic synapses were present, suggesting that co-released glutamate cannot induce postsynaptic glutamate receptor clustering.

  1. Dynamical mean-field theory of noisy spiking neuron ensembles: Application to the Hodgkin-Huxley model

    International Nuclear Information System (INIS)

    Hasegawa, Hideo

    2003-01-01

    A dynamical mean-field approximation (DMA) previously proposed by the present author [H. Hasegawa, Phys. Rev E 67, 041903 (2003)] has been extended to ensembles described by a general noisy spiking neuron model. Ensembles of N-unit neurons, each of which is expressed by coupled K-dimensional differential equations (DEs), are assumed to be subject to spatially correlated white noises. The original KN-dimensional stochastic DEs have been replaced by K(K+2)-dimensional deterministic DEs expressed in terms of means and the second-order moments of local and global variables: the fourth-order contributions are taken into account by the Gaussian decoupling approximation. Our DMA has been applied to an ensemble of Hodgkin-Huxley (HH) neurons (K=4), for which effects of the noise, the coupling strength, and the ensemble size on the response to a single-spike input have been investigated. Numerical results calculated by the DMA theory are in good agreement with those obtained by direct simulations, although the former computation is about a thousand times faster than the latter for a typical HH neuron ensemble with N=100

  2. Phase-locking of bursting neuronal firing to dominant LFP frequency components.

    Science.gov (United States)

    Constantinou, Maria; Elijah, Daniel H; Squirrell, Daniel; Gigg, John; Montemurro, Marcelo A

    2015-10-01

    Neuronal firing in the hippocampal formation relative to the phase of local field potentials (LFP) has a key role in memory processing and spatial navigation. Firing can be in either tonic or burst mode. Although bursting neurons are common in the hippocampal formation, the characteristics of their locking to LFP phase are not completely understood. We investigated phase-locking properties of bursting neurons using simulations generated by a dual compartmental model of a pyramidal neuron adapted to match the bursting activity in the subiculum of a rat. The model was driven with stochastic input signals containing a power spectral profile consistent with physiologically relevant frequencies observed in LFP. The single spikes and spike bursts fired by the model were locked to a preferred phase of the predominant frequency band where there was a peak in the power of the driving signal. Moreover, the preferred phase of locking shifted with increasing burst size, providing evidence that LFP phase can be encoded by burst size. We also provide initial support for the model results by analysing example data of spontaneous LFP and spiking activity recorded from the subiculum of a single urethane-anaesthetised rat. Subicular neurons fired single spikes, two-spike bursts and larger bursts that locked to a preferred phase of either dominant slow oscillations or theta rhythms within the LFP, according to the model prediction. Both power-modulated phase-locking and gradual shift in the preferred phase of locking as a function of burst size suggest that neurons can use bursts to encode timing information contained in LFP phase into a spike-count code. Copyright © 2015 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.

  3. Towards a theory of cortical columns: From spiking neurons to interacting neural populations of finite size

    Science.gov (United States)

    Gerstner, Wulfram

    2017-01-01

    Neural population equations such as neural mass or field models are widely used to study brain activity on a large scale. However, the relation of these models to the properties of single neurons is unclear. Here we derive an equation for several interacting populations at the mesoscopic scale starting from a microscopic model of randomly connected generalized integrate-and-fire neuron models. Each population consists of 50–2000 neurons of the same type but different populations account for different neuron types. The stochastic population equations that we find reveal how spike-history effects in single-neuron dynamics such as refractoriness and adaptation interact with finite-size fluctuations on the population level. Efficient integration of the stochastic mesoscopic equations reproduces the statistical behavior of the population activities obtained from microscopic simulations of a full spiking neural network model. The theory describes nonlinear emergent dynamics such as finite-size-induced stochastic transitions in multistable networks and synchronization in balanced networks of excitatory and inhibitory neurons. The mesoscopic equations are employed to rapidly integrate a model of a cortical microcircuit consisting of eight neuron types, which allows us to predict spontaneous population activities as well as evoked responses to thalamic input. Our theory establishes a general framework for modeling finite-size neural population dynamics based on single cell and synapse parameters and offers an efficient approach to analyzing cortical circuits and computations. PMID:28422957

  4. Memristors Empower Spiking Neurons With Stochasticity

    KAUST Repository

    Al-Shedivat, Maruan

    2015-06-01

    Recent theoretical studies have shown that probabilistic spiking can be interpreted as learning and inference in cortical microcircuits. This interpretation creates new opportunities for building neuromorphic systems driven by probabilistic learning algorithms. However, such systems must have two crucial features: 1) the neurons should follow a specific behavioral model, and 2) stochastic spiking should be implemented efficiently for it to be scalable. This paper proposes a memristor-based stochastically spiking neuron that fulfills these requirements. First, the analytical model of the memristor is enhanced so it can capture the behavioral stochasticity consistent with experimentally observed phenomena. The switching behavior of the memristor model is demonstrated to be akin to the firing of the stochastic spike response neuron model, the primary building block for probabilistic algorithms in spiking neural networks. Furthermore, the paper proposes a neural soma circuit that utilizes the intrinsic nondeterminism of memristive switching for efficient spike generation. The simulations and analysis of the behavior of a single stochastic neuron and a winner-take-all network built of such neurons and trained on handwritten digits confirm that the circuit can be used for building probabilistic sampling and pattern adaptation machinery in spiking networks. The findings constitute an important step towards scalable and efficient probabilistic neuromorphic platforms. © 2011 IEEE.

  5. A Neuron Model Based Ultralow Current Sensor System for Bioapplications

    Directory of Open Access Journals (Sweden)

    A. K. M. Arifuzzman

    2016-01-01

    Full Text Available An ultralow current sensor system based on the Izhikevich neuron model is presented in this paper. The Izhikevich neuron model has been used for its superior computational efficiency and greater biological plausibility over other well-known neuron spiking models. Of the many biological neuron spiking features, regular spiking, chattering, and neostriatal spiny projection spiking have been reproduced by adjusting the parameters associated with the model at hand. This paper also presents a modified interpretation of the regular spiking feature in which the firing pattern is similar to that of the regular spiking but with improved dynamic range offering. The sensor current ranges between 2 pA and 8 nA and exhibits linearity in the range of 0.9665 to 0.9989 for different spiking features. The efficacy of the sensor system in detecting low amount of current along with its high linearity attribute makes it very suitable for biomedical applications.

  6. A digital implementation of neuron-astrocyte interaction for neuromorphic applications.

    Science.gov (United States)

    Nazari, Soheila; Faez, Karim; Amiri, Mahmood; Karami, Ehsan

    2015-06-01

    Recent neurophysiologic findings have shown that astrocytes play important roles in information processing and modulation of neuronal activity. Motivated by these findings, in the present research, a digital neuromorphic circuit to study neuron-astrocyte interaction is proposed. In this digital circuit, the firing dynamics of the neuron is described by Izhikevich model and the calcium dynamics of a single astrocyte is explained by a functional model introduced by Postnov and colleagues. For digital implementation of the neuron-astrocyte signaling, Single Constant Multiply (SCM) technique and several linear approximations are used for efficient low-cost hardware implementation on digital platforms. Using the proposed neuron-astrocyte circuit and based on the results of MATLAB simulations, hardware synthesis and FPGA implementation, it is demonstrated that the proposed digital astrocyte is able to change the firing patterns of the neuron through bidirectional communication. Utilizing the proposed digital circuit, it will be illustrated that information processing in synaptic clefts is strongly regulated by astrocyte. Moreover, our results suggest that the digital circuit of neuron-astrocyte crosstalk produces diverse neural responses and therefore enhances the information processing capabilities of the neuromorphic circuits. This is suitable for applications in reconfigurable neuromorphic devices which implement biologically brain circuits. Copyright © 2015 Elsevier Ltd. All rights reserved.

  7. A chimeric path to neuronal synchronization

    Science.gov (United States)

    Essaki Arumugam, Easwara Moorthy; Spano, Mark L.

    2015-01-01

    Synchronization of neuronal activity is associated with neurological disorders such as epilepsy. This process of neuronal synchronization is not fully understood. To further our understanding, we have experimentally studied the progression of this synchronization from normal neuronal firing to full synchronization. We implemented nine FitzHugh-Nagumo neurons (a simplified Hodgkin-Huxley model) via discrete electronics. For different coupling parameters (synaptic strengths), the neurons in the ring were either unsynchronized or completely synchronized when locally coupled in a ring. When a single long-range connection (nonlocal coupling) was introduced, an intermediate state known as a chimera appeared. The results indicate that (1) epilepsy is likely not only a dynamical disease but also a topological disease, strongly tied to the connectivity of the underlying network of neurons, and (2) the synchronization process in epilepsy may not be an "all or none" phenomenon, but can pass through an intermediate stage (chimera).

  8. A chimeric path to neuronal synchronization

    Energy Technology Data Exchange (ETDEWEB)

    Essaki Arumugam, Easwara Moorthy; Spano, Mark L. [School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona 85287-9709 (United States)

    2015-01-15

    Synchronization of neuronal activity is associated with neurological disorders such as epilepsy. This process of neuronal synchronization is not fully understood. To further our understanding, we have experimentally studied the progression of this synchronization from normal neuronal firing to full synchronization. We implemented nine FitzHugh-Nagumo neurons (a simplified Hodgkin-Huxley model) via discrete electronics. For different coupling parameters (synaptic strengths), the neurons in the ring were either unsynchronized or completely synchronized when locally coupled in a ring. When a single long-range connection (nonlocal coupling) was introduced, an intermediate state known as a chimera appeared. The results indicate that (1) epilepsy is likely not only a dynamical disease but also a topological disease, strongly tied to the connectivity of the underlying network of neurons, and (2) the synchronization process in epilepsy may not be an “all or none” phenomenon, but can pass through an intermediate stage (chimera)

  9. A chimeric path to neuronal synchronization

    International Nuclear Information System (INIS)

    Essaki Arumugam, Easwara Moorthy; Spano, Mark L.

    2015-01-01

    Synchronization of neuronal activity is associated with neurological disorders such as epilepsy. This process of neuronal synchronization is not fully understood. To further our understanding, we have experimentally studied the progression of this synchronization from normal neuronal firing to full synchronization. We implemented nine FitzHugh-Nagumo neurons (a simplified Hodgkin-Huxley model) via discrete electronics. For different coupling parameters (synaptic strengths), the neurons in the ring were either unsynchronized or completely synchronized when locally coupled in a ring. When a single long-range connection (nonlocal coupling) was introduced, an intermediate state known as a chimera appeared. The results indicate that (1) epilepsy is likely not only a dynamical disease but also a topological disease, strongly tied to the connectivity of the underlying network of neurons, and (2) the synchronization process in epilepsy may not be an “all or none” phenomenon, but can pass through an intermediate stage (chimera)

  10. A single GABAergic neuron mediates feedback of odor-evoked signals in the mushroom body of larval Drosophila

    Directory of Open Access Journals (Sweden)

    Liria Monica Masuda-Nakagawa

    2014-04-01

    Full Text Available Inhibition has a central role in defining the selectivity of the responses of higher order neurons to sensory stimuli. However, the circuit mechanisms of regulation of these responses by inhibitory neurons are still unclear. In Drosophila, the mushroom bodies (MBs are necessary for olfactory memory, and by implication for the selectivity of learned responses to specific odors. To understand the circuitry of inhibition in the calyx (the input dendritic region of the MBs, and its relationship with MB excitatory activity, we used the simple anatomy of the Drosophila larval olfactory system to identify any inhibitory inputs that could contribute to the selectivity of MB odor responses. We found that a single neuron accounts for all detectable GABA innervation in the calyx of the MBs, and that this neuron has presynaptic terminals in the calyx and postsynaptic branches in the MB lobes (output axonal area. We call this neuron the larval anterior paired lateral (APL neuron, because of its similarity to the previously described adult APL neuron. Reconstitution of GFP partners (GRASP suggests that the larval APL makes extensive contacts with the MB intrinsic neurons, Kenyon Cells (KCs, but few contacts with incoming projection neurons. Using calcium imaging of neuronal activity in live larvae, we show that the larval APL responds to odors, in a mannner that requires output from KCs. Our data suggest that the larval APL is the sole GABAergic neuron that innervates the MB input region and carries inhibitory feedback from the MB output region, consistent with a role in modulating the olfactory selectivity of MB neurons.

  11. A Neuron-Based Model of Sleep-Wake Cycles

    Science.gov (United States)

    Postnova, Svetlana; Peters, Achim; Braun, Hans

    2008-03-01

    In recent years it was discovered that a neuropeptide orexin/hypocretin plays a main role in sleep processes. This peptide is produced by the neurons in the lateral hypothalamus, which project to almost all brain areas. We present a computational model of sleep-wake cycles, which is based on the Hodgkin-Huxley type neurons and considers reciprocal glutaminergic projections between the lateral hypothalamus and the prefrontal cortex. Orexin is released as a neuromodulator and is required to keep the neurons firing, which corresponds to the wake state. When orexin is depleted the neurons are getting silent as observed in the sleep state. They can be reactivated by the circadian signal from the suprachiasmatic nucleus and/or external stimuli (alarm clock). Orexin projections to the thalamocortical neurons also can account for their transition from tonic firing activity during wakefulness to synchronized burst discharges during sleep.

  12. All-Optical Electrophysiology for Disease Modeling and Pharmacological Characterization of Neurons.

    Science.gov (United States)

    Werley, Christopher A; Brookings, Ted; Upadhyay, Hansini; Williams, Luis A; McManus, Owen B; Dempsey, Graham T

    2017-09-11

    A key challenge for establishing a phenotypic screen for neuronal excitability is measurement of membrane potential changes with high throughput and accuracy. Most approaches for probing excitability rely on low-throughput, invasive methods or lack cell-specific information. These limitations stimulated the development of novel strategies for characterizing the electrical properties of cultured neurons. Among these was the development of optogenetic technologies (Optopatch) that allow for stimulation and recording of membrane voltage signals from cultured neurons with single-cell sensitivity and millisecond temporal resolution. Neuronal activity is elicited using blue light activation of the channelrhodopsin variant 'CheRiff'. Action potentials and synaptic signals are measured with 'QuasAr', a rapid and sensitive voltage-indicating protein with near-infrared fluorescence that scales proportionately with transmembrane potential. This integrated technology of optical stimulation and recording of electrical signals enables investigation of neuronal electrical function with unprecedented scale and precision. © 2017 by John Wiley & Sons, Inc. Copyright © 2017 John Wiley & Sons, Inc.

  13. I h and HCN channels in murine spiral ganglion neurons: tonotopic variation, local heterogeneity, and kinetic model.

    Science.gov (United States)

    Liu, Qing; Manis, Paul B; Davis, Robin L

    2014-08-01

    One of the major contributors to the response profile of neurons in the auditory pathways is the I h current. Its properties such as magnitude, activation, and kinetics not only vary among different types of neurons (Banks et al., J Neurophysiol 70:1420-1432, 1993; Fu et al., J Neurophysiol 78:2235-2245, 1997; Bal and Oertel, J Neurophysiol 84:806-817, 2000; Cao and Oertel, J Neurophysiol 94:821-832, 2005; Rodrigues and Oertel, J Neurophysiol 95:76-87, 2006; Yi et al., J Neurophysiol 103:2532-2543, 2010), but they also display notable diversity in a single population of spiral ganglion neurons (Mo and Davis, J Neurophysiol 78:3019-3027, 1997), the first neural element in the auditory periphery. In this study, we found from somatic recordings that part of the heterogeneity can be attributed to variation along the tonotopic axis because I h in the apical neurons have more positive half-activation voltage levels than basal neurons. Even within a single cochlear region, however, I h current properties are not uniform. To account for this heterogeneity, we provide immunocytochemical evidence for variance in the intracellular density of the hyperpolarization-activated cyclic nucleotide-gated channel α-subunit 1 (HCN1), which mediates I h current. We also observed different combinations of HCN1 and HCN4 α-subunits from cell to cell. Lastly, based on the physiological data, we performed kinetic analysis for the I h current and generated a mathematical model to better understand varied I h on spiral ganglion function. Regardless of whether I h currents are recorded at the nerve terminals (Yi et al., J Neurophysiol 103:2532-2543, 2010) or at the somata of spiral ganglion neurons, they have comparable mean half-activation voltage and induce similar resting membrane potential changes, and thus our model may also provide insights into the impact of I h on synaptic physiology.

  14. The Effect of Single Pyramidal Neuron Firing Within Layer 2/3 and Layer 4 in Mouse V1.

    Science.gov (United States)

    Meyer, Jochen F; Golshani, Peyman; Smirnakis, Stelios M

    2018-01-01

    The influence of cortical cell spiking activity on nearby cells has been studied extensively in vitro . Less is known, however, about the impact of single cell firing on local cortical networks in vivo . In a pioneering study, Kwan and Dan (Kwan and Dan, 2012) reported that in mouse layer 2/3 (L2/3), under anesthesia , stimulating a single pyramidal cell recruits ~2.1% of neighboring units. Here we employ two-photon calcium imaging in layer 2/3 of mouse V1, in conjunction with single-cell patch clamp stimulation in layer 2/3 or layer 4, to probe, in both the awake and lightly anesthetized states , how (i) activating single L2/3 pyramidal neurons recruits neighboring units within L2/3 and from layer 4 (L4) to L2/3, and whether (ii) activating single pyramidal neurons changes population activity in local circuit. To do this, it was essential to develop an algorithm capable of quantifying how sensitive the calcium signal is at detecting effectively recruited units ("followers"). This algorithm allowed us to estimate the chance of detecting a follower as a function of the probability that an epoch of stimulation elicits one extra action potential (AP) in the follower cell. Using this approach, we found only a small fraction (layer-2/3 or layer-4 pyramidal neurons produces few (<1% of local units) reliable single-cell followers in L2/3 of mouse area V1, either under light anesthesia or in quiet wakefulness: instead, single cell stimulation was found to elevate aggregate population activity in a weak but highly distributed fashion.

  15. Challenging the neuronal MIBG uptake by pharmacological intervention: effect of a single dose of oral amitriptyline on regional cardiac MIBG uptake

    Energy Technology Data Exchange (ETDEWEB)

    Estorch, Montserrat; Carrio, Ignasi; Mena, Esther; Flotats, Albert; Camacho, Valle; Fuertes, Jordi [Autonomous University of Barcelona, Department of Nuclear Medicine, Hospital Sant Pau, Barcelona (Spain); Kulisewsky, Jaume [Autonomous University of Barcelona, Department of Neurology, Hospital Sant Pau, Barcelona (Spain); Narula, Jagat [Irvine College of Medicine, Division of Cardiology, University of California, Irvine, CA (United States)

    2004-12-01

    Imaging with metaiodobenzylguanidine (MIBG) is used for the assessment of neuronal dysfunction in various cardiovascular disorders. Although valuable information is obtained by resting MIBG imaging, it is conceivable that competitive interference with the re-uptake mechanism would exaggerate MIBG defects and might unmask subclinical neuronal dysfunction. Tricyclic antidepressants, such as amitriptyline, have been reported to significantly increase cardiac MIBG washout and inhibit uptake into presynaptic neurons. This study was undertaken to assess whether a single oral dose of amitriptyline could influence cardiac MIBG distribution. Six patients (aged 62-81 years; four males, two females) who had demonstrated a normal cardiac MIBG scan during work-up for movement disorders were studied. The patients underwent a second {sup 123}I-MIBG study after oral administration of 25 mg amitriptyline within 1 week. Single-photon emission computed tomography images were acquired at 4 h to assess the regional distribution of MIBG, after generation of polar maps and employing a 20-segment model. Mean percentage of peak activity was calculated for each segment at rest and after amitriptyline administration. After amitriptyline administration, there was a decrease in regional MIBG uptake in 10{+-}4 segments per patient [62/120 segments (52%): 37 segments with a 5-10% decrease, 25 segments with a >10% decrease]. This change was statistically significant in lateral (P=0.003), apical (P<0.0001) and inferior (P=0.03) regions. A single oral dose of amitriptyline can induce changes in the uptake and retention of cardiac MIBG, indicating the feasibility of use of pharmacological intervention in cardiac neurotransmission imaging. (orig.)

  16. Complex Behavior in a Selective Aging Neuron Model Based on Small World Networks

    International Nuclear Information System (INIS)

    Zhang Guiqing; Chen Tianlun

    2008-01-01

    Complex behavior in a selective aging simple neuron model based on small world networks is investigated. The basic elements of the model are endowed with the main features of a neuron function. The structure of the selective aging neuron model is discussed. We also give some properties of the new network and find that the neuron model displays a power-law behavior. If the brain network is small world-like network, the mean avalanche size is almost the same unless the aging parameter is big enough.

  17. Electrical Activity in a Time-Delay Four-Variable Neuron Model under Electromagnetic Induction

    Directory of Open Access Journals (Sweden)

    Keming Tang

    2017-11-01

    Full Text Available To investigate the effect of electromagnetic induction on the electrical activity of neuron, the variable for magnetic flow is used to improve Hindmarsh–Rose neuron model. Simultaneously, due to the existence of time-delay when signals are propagated between neurons or even in one neuron, it is important to study the role of time-delay in regulating the electrical activity of the neuron. For this end, a four-variable neuron model is proposed to investigate the effects of electromagnetic induction and time-delay. Simulation results suggest that the proposed neuron model can show multiple modes of electrical activity, which is dependent on the time-delay and external forcing current. It means that suitable discharge mode can be obtained by selecting the time-delay or external forcing current, which could be helpful for further investigation of electromagnetic radiation on biological neuronal system.

  18. NBLAST: Rapid, Sensitive Comparison of Neuronal Structure and Construction of Neuron Family Databases.

    Science.gov (United States)

    Costa, Marta; Manton, James D; Ostrovsky, Aaron D; Prohaska, Steffen; Jefferis, Gregory S X E

    2016-07-20

    Neural circuit mapping is generating datasets of tens of thousands of labeled neurons. New computational tools are needed to search and organize these data. We present NBLAST, a sensitive and rapid algorithm, for measuring pairwise neuronal similarity. NBLAST considers both position and local geometry, decomposing neurons into short segments; matched segments are scored using a probabilistic scoring matrix defined by statistics of matches and non-matches. We validated NBLAST on a published dataset of 16,129 single Drosophila neurons. NBLAST can distinguish neuronal types down to the finest level (single identified neurons) without a priori information. Cluster analysis of extensively studied neuronal classes identified new types and unreported topographical features. Fully automated clustering organized the validation dataset into 1,052 clusters, many of which map onto previously described neuronal types. NBLAST supports additional query types, including searching neurons against transgene expression patterns. Finally, we show that NBLAST is effective with data from other invertebrates and zebrafish. VIDEO ABSTRACT. Copyright © 2016 MRC Laboratory of Molecular Biology. Published by Elsevier Inc. All rights reserved.

  19. A self-resetting spiking phase-change neuron

    Science.gov (United States)

    Cobley, R. A.; Hayat, H.; Wright, C. D.

    2018-05-01

    Neuromorphic, or brain-inspired, computing applications of phase-change devices have to date concentrated primarily on the implementation of phase-change synapses. However, the so-called accumulation mode of operation inherent in phase-change materials and devices can also be used to mimic the integrative properties of a biological neuron. Here we demonstrate, using physical modelling of nanoscale devices and SPICE modelling of associated circuits, that a single phase-change memory cell integrated into a comparator type circuit can deliver a basic hardware mimic of an integrate-and-fire spiking neuron with self-resetting capabilities. Such phase-change neurons, in combination with phase-change synapses, can potentially open a new route for the realisation of all-phase-change neuromorphic computing.

  20. A self-resetting spiking phase-change neuron.

    Science.gov (United States)

    Cobley, R A; Hayat, H; Wright, C D

    2018-05-11

    Neuromorphic, or brain-inspired, computing applications of phase-change devices have to date concentrated primarily on the implementation of phase-change synapses. However, the so-called accumulation mode of operation inherent in phase-change materials and devices can also be used to mimic the integrative properties of a biological neuron. Here we demonstrate, using physical modelling of nanoscale devices and SPICE modelling of associated circuits, that a single phase-change memory cell integrated into a comparator type circuit can deliver a basic hardware mimic of an integrate-and-fire spiking neuron with self-resetting capabilities. Such phase-change neurons, in combination with phase-change synapses, can potentially open a new route for the realisation of all-phase-change neuromorphic computing.

  1. An experimental electronic model for a neuronal cell

    International Nuclear Information System (INIS)

    Campos-Cantón, I; Martel-Gallegos, G; Rangel-López, A; Vertiz-Hérnandez, A; Zarazúa, S

    2014-01-01

    Over the last two decades, the study of information transmission in living beings has acquired great relevance, because it regulates and conducts the functioning of all of the organs in the body. In information transmission pathways, the neuron plays an important role in that it receives, transmits, and processes electrical signals from different parts of the human body; these signals are transmitted as electrical impulses called action potentials, and they transmit information from one neuron to another. In this work, and with the aim of developing experiments for teaching biological processes, we implemented an electronic circuit of the neuron cell device and its mathematical model based on piecewise linear functions. (paper)

  2. Noise-Induced Transition in a Voltage-Controlled Oscillator Neuron Model

    International Nuclear Information System (INIS)

    Xie Huizhang; Liu Xuemei; Li Zhibing; Ai Baoquan; Liu Lianggang

    2008-01-01

    In the presence of Gaussian white noise, we study the properties of voltage-controlled oscillator neuron model and discuss the effects of the additive and multiplicative noise. It is found that the additive noise can accelerate and counterwork the firing of neuron, which depends on the value of central frequency of neuron itself, while multiplicative noise can induce the continuous change or mutation of membrane potential

  3. Kappe neurons, a novel population of olfactory sensory neurons.

    Science.gov (United States)

    Ahuja, Gaurav; Bozorg Nia, Shahrzad; Zapilko, Veronika; Shiriagin, Vladimir; Kowatschew, Daniel; Oka, Yuichiro; Korsching, Sigrun I

    2014-02-10

    Perception of olfactory stimuli is mediated by distinct populations of olfactory sensory neurons, each with a characteristic set of morphological as well as functional parameters. Beyond two large populations of ciliated and microvillous neurons, a third population, crypt neurons, has been identified in teleost and cartilaginous fishes. We report here a novel, fourth olfactory sensory neuron population in zebrafish, which we named kappe neurons for their characteristic shape. Kappe neurons are identified by their Go-like immunoreactivity, and show a distinct spatial distribution within the olfactory epithelium, similar to, but significantly different from that of crypt neurons. Furthermore, kappe neurons project to a single identified target glomerulus within the olfactory bulb, mdg5 of the mediodorsal cluster, whereas crypt neurons are known to project exclusively to the mdg2 glomerulus. Kappe neurons are negative for established markers of ciliated, microvillous and crypt neurons, but appear to have microvilli. Kappe neurons constitute the fourth type of olfactory sensory neurons reported in teleost fishes and their existence suggests that encoding of olfactory stimuli may require a higher complexity than hitherto assumed already in the peripheral olfactory system.

  4. A Neuronal Network Model for Pitch Selectivity and Representation.

    Science.gov (United States)

    Huang, Chengcheng; Rinzel, John

    2016-01-01

    Pitch is a perceptual correlate of periodicity. Sounds with distinct spectra can elicit the same pitch. Despite the importance of pitch perception, understanding the cellular mechanism of pitch perception is still a major challenge and a mechanistic model of pitch is lacking. A multi-stage neuronal network model is developed for pitch frequency estimation using biophysically-based, high-resolution coincidence detector neurons. The neuronal units respond only to highly coincident input among convergent auditory nerve fibers across frequency channels. Their selectivity for only very fast rising slopes of convergent input enables these slope-detectors to distinguish the most prominent coincidences in multi-peaked input time courses. Pitch can then be estimated from the first-order interspike intervals of the slope-detectors. The regular firing pattern of the slope-detector neurons are similar for sounds sharing the same pitch despite the distinct timbres. The decoded pitch strengths also correlate well with the salience of pitch perception as reported by human listeners. Therefore, our model can serve as a neural representation for pitch. Our model performs successfully in estimating the pitch of missing fundamental complexes and reproducing the pitch variation with respect to the frequency shift of inharmonic complexes. It also accounts for the phase sensitivity of pitch perception in the cases of Schroeder phase, alternating phase and random phase relationships. Moreover, our model can also be applied to stochastic sound stimuli, iterated-ripple-noise, and account for their multiple pitch perceptions.

  5. A metabolomic comparison of mouse models of the Neuronal Ceroid Lipofuscinoses

    Energy Technology Data Exchange (ETDEWEB)

    Salek, Reza M.; Pears, Michael R. [University of Cambridge, Department of Biochemistry and Cambridge Systems Biology Centre (United Kingdom); Cooper, Jonathan D. [King' s College London, Pediatric Storage Disorders Laboratory, Department of Neuroscience, Institute of Psychiatry (United Kingdom); Mitchison, Hannah M. [Royal Free and University College Medical School, Department of Paediatrics and Child Health (United Kingdom); Pearce, David A. [Sanford School of Medicine of the University of South Dakota, Department of Pediatrics (United States); Mortishire-Smith, Russell J. [Johnson and Johnson PR and D (Belgium); Griffin, Julian L., E-mail: jlg40@mole.bio.cam.ac.uk [University of Cambridge, Department of Biochemistry and the Cambridge Systems Biology Centre (United Kingdom)

    2011-04-15

    The Neuronal Ceroid Lipofuscinoses (NCL) are a group of fatal inherited neurodegenerative diseases in humans distinguished by a common clinical pathology, characterized by the accumulation of storage body material in cells and gross brain atrophy. In this study, metabolic changes in three NCL mouse models were examined looking for pathways correlated with neurodegeneration. Two mouse models; motor neuron degeneration (mnd) mouse and a variant model of late infantile NCL, termed the neuronal ceroid lipofuscinosis (nclf) mouse were investigated experimentally. Both models exhibit a characteristic accumulation of autofluorescent lipopigment in neuronal and non neuronal cells. The NMR profiles derived from extracts of the cortex and cerebellum from mnd and nclf mice were distinguished according to disease/wildtype status. In particular, a perturbation in glutamine and glutamate metabolism, and a decrease in {gamma}-amino butyric acid (GABA) in the cerebellum and cortices of mnd (adolescent mice) and nclf mice relative to wildtype at all ages were detected. Our results were compared to the Cln3 mouse model of NCL. The metabolism of mnd mice resembled older (6 month) Cln3 mice, where the disease is relatively advanced, while the metabolism of nclf mice was more akin to younger (1-2 months) Cln3 mice, where the disease is in its early stages of progression. Overall, our results allowed the identification of metabolic traits common to all NCL subtypes for the three animal models.

  6. A metabolomic comparison of mouse models of the Neuronal Ceroid Lipofuscinoses

    International Nuclear Information System (INIS)

    Salek, Reza M.; Pears, Michael R.; Cooper, Jonathan D.; Mitchison, Hannah M.; Pearce, David A.; Mortishire-Smith, Russell J.; Griffin, Julian L.

    2011-01-01

    The Neuronal Ceroid Lipofuscinoses (NCL) are a group of fatal inherited neurodegenerative diseases in humans distinguished by a common clinical pathology, characterized by the accumulation of storage body material in cells and gross brain atrophy. In this study, metabolic changes in three NCL mouse models were examined looking for pathways correlated with neurodegeneration. Two mouse models; motor neuron degeneration (mnd) mouse and a variant model of late infantile NCL, termed the neuronal ceroid lipofuscinosis (nclf) mouse were investigated experimentally. Both models exhibit a characteristic accumulation of autofluorescent lipopigment in neuronal and non neuronal cells. The NMR profiles derived from extracts of the cortex and cerebellum from mnd and nclf mice were distinguished according to disease/wildtype status. In particular, a perturbation in glutamine and glutamate metabolism, and a decrease in γ-amino butyric acid (GABA) in the cerebellum and cortices of mnd (adolescent mice) and nclf mice relative to wildtype at all ages were detected. Our results were compared to the Cln3 mouse model of NCL. The metabolism of mnd mice resembled older (6 month) Cln3 mice, where the disease is relatively advanced, while the metabolism of nclf mice was more akin to younger (1-2 months) Cln3 mice, where the disease is in its early stages of progression. Overall, our results allowed the identification of metabolic traits common to all NCL subtypes for the three animal models.

  7. Neuron specific metabolic adaptations following multi-day exposures to oxygen glucose deprivation.

    Science.gov (United States)

    Zeiger, Stephanie L H; McKenzie, Jennifer R; Stankowski, Jeannette N; Martin, Jacob A; Cliffel, David E; McLaughlin, BethAnn

    2010-11-01

    Prior exposure to sub toxic insults can induce a powerful endogenous neuroprotective program known as ischemic preconditioning. Current models typically rely on a single stress episode to induce neuroprotection whereas the clinical reality is that patients may experience multiple transient ischemic attacks (TIAs) prior to suffering a stroke. We sought to develop a neuron-enriched preconditioning model using multiple oxygen glucose deprivation (OGD) episodes to assess the endogenous protective mechanisms neurons implement at the metabolic and cellular level. We found that neurons exposed to a five minute period of glucose deprivation recovered oxygen utilization and lactate production using novel microphysiometry techniques. Using the non-toxic and energetically favorable five minute exposure, we developed a preconditioning paradigm where neurons are exposed to this brief OGD for three consecutive days. These cells experienced a 45% greater survival following an otherwise lethal event and exhibited a longer lasting window of protection in comparison to our previous in vitro preconditioning model using a single stress. As in other models, preconditioned cells exhibited mild caspase activation, an increase in oxidized proteins and a requirement for reactive oxygen species for neuroprotection. Heat shock protein 70 was upregulated during preconditioning, yet the majority of this protein was released extracellularly. We believe coupling this neuron-enriched multi-day model with microphysiometry will allow us to assess neuronal specific real-time metabolic adaptations necessary for preconditioning. Copyright © 2010 Elsevier B.V. All rights reserved.

  8. Functional crosstalk in culture between macrophages and trigeminal sensory neurons of a mouse genetic model of migraine.

    Science.gov (United States)

    Franceschini, Alessia; Nair, Asha; Bele, Tanja; van den Maagdenberg, Arn Mjm; Nistri, Andrea; Fabbretti, Elsa

    2012-11-21

    Enhanced activity of trigeminal ganglion neurons is thought to underlie neuronal sensitization facilitating the onset of chronic pain attacks, including migraine. Recurrent headache attacks might establish a chronic neuroinflammatory ganglion profile contributing to the hypersensitive phenotype. Since it is difficult to study this process in vivo, we investigated functional crosstalk between macrophages and sensory neurons in primary cultures from trigeminal sensory ganglia of wild-type (WT) or knock-in (KI) mice expressing the Cacna1a gene mutation (R192Q) found in familial hemiplegic migraine-type 1. After studying the number and morphology of resident macrophages in culture, the consequences of adding host macrophages on macrophage phagocytosis and membrane currents mediated by pain-transducing P2X3 receptors on sensory neurons were examined. KI ganglion cultures constitutively contained a larger number of active macrophages, although no difference in P2X3 receptor expression was found. Co-culturing WT or KI ganglia with host macrophages (active as much as resident cells) strongly stimulated single cell phagocytosis. The same protocol had no effect on P2X3 receptor expression in WT or KI co-cultures, but it largely enhanced WT neuron currents that grew to the high amplitude constitutively seen for KI neurons. No further potentiation of KI neuronal currents was observed. Trigeminal ganglion cultures from a genetic mouse model of migraine showed basal macrophage activation together with enhanced neuronal currents mediated by P2X3 receptors. This phenotype could be replicated in WT cultures by adding host macrophages, indicating an important functional crosstalk between macrophages and sensory neurons.

  9. Neuronal model with distributed delay: analysis and simulation study for gamma distribution memory kernel.

    Science.gov (United States)

    Karmeshu; Gupta, Varun; Kadambari, K V

    2011-06-01

    A single neuronal model incorporating distributed delay (memory)is proposed. The stochastic model has been formulated as a Stochastic Integro-Differential Equation (SIDE) which results in the underlying process being non-Markovian. A detailed analysis of the model when the distributed delay kernel has exponential form (weak delay) has been carried out. The selection of exponential kernel has enabled the transformation of the non-Markovian model to a Markovian model in an extended state space. For the study of First Passage Time (FPT) with exponential delay kernel, the model has been transformed to a system of coupled Stochastic Differential Equations (SDEs) in two-dimensional state space. Simulation studies of the SDEs provide insight into the effect of weak delay kernel on the Inter-Spike Interval(ISI) distribution. A measure based on Jensen-Shannon divergence is proposed which can be used to make a choice between two competing models viz. distributed delay model vis-á-vis LIF model. An interesting feature of the model is that the behavior of (CV(t))((ISI)) (Coefficient of Variation) of the ISI distribution with respect to memory kernel time constant parameter η reveals that neuron can switch from a bursting state to non-bursting state as the noise intensity parameter changes. The membrane potential exhibits decaying auto-correlation structure with or without damped oscillatory behavior depending on the choice of parameters. This behavior is in agreement with empirically observed pattern of spike count in a fixed time window. The power spectral density derived from the auto-correlation function is found to exhibit single and double peaks. The model is also examined for the case of strong delay with memory kernel having the form of Gamma distribution. In contrast to fast decay of damped oscillations of the ISI distribution for the model with weak delay kernel, the decay of damped oscillations is found to be slower for the model with strong delay kernel.

  10. Bayesian population decoding of spiking neurons.

    Science.gov (United States)

    Gerwinn, Sebastian; Macke, Jakob; Bethge, Matthias

    2009-01-01

    The timing of action potentials in spiking neurons depends on the temporal dynamics of their inputs and contains information about temporal fluctuations in the stimulus. Leaky integrate-and-fire neurons constitute a popular class of encoding models, in which spike times depend directly on the temporal structure of the inputs. However, optimal decoding rules for these models have only been studied explicitly in the noiseless case. Here, we study decoding rules for probabilistic inference of a continuous stimulus from the spike times of a population of leaky integrate-and-fire neurons with threshold noise. We derive three algorithms for approximating the posterior distribution over stimuli as a function of the observed spike trains. In addition to a reconstruction of the stimulus we thus obtain an estimate of the uncertainty as well. Furthermore, we derive a 'spike-by-spike' online decoding scheme that recursively updates the posterior with the arrival of each new spike. We use these decoding rules to reconstruct time-varying stimuli represented by a Gaussian process from spike trains of single neurons as well as neural populations.

  11. Bayesian population decoding of spiking neurons

    Directory of Open Access Journals (Sweden)

    Sebastian Gerwinn

    2009-10-01

    Full Text Available The timing of action potentials in spiking neurons depends on the temporal dynamics of their inputs and contains information about temporal fluctuations in the stimulus. Leaky integrate-and-fire neurons constitute a popular class of encoding models, in which spike times depend directly on the temporal structure of the inputs. However, optimal decoding rules for these models have only been studied explicitly in the noiseless case. Here, we study decoding rules for probabilistic inference of a continuous stimulus from the spike times of a population of leaky integrate-and-fire neurons with threshold noise. We derive three algorithms for approximating the posterior distribution over stimuli as a function of the observed spike trains. In addition to a reconstruction of the stimulus we thus obtain an estimate of the uncertainty as well. Furthermore, we derive a `spike-by-spike' online decoding scheme that recursively updates the posterior with the arrival of each new spike. We use these decoding rules to reconstruct time-varying stimuli represented by a Gaussian process from spike trains of single neurons as well as neural populations.

  12. A flexible, interactive software tool for fitting the parameters of neuronal models.

    Science.gov (United States)

    Friedrich, Péter; Vella, Michael; Gulyás, Attila I; Freund, Tamás F; Káli, Szabolcs

    2014-01-01

    The construction of biologically relevant neuronal models as well as model-based analysis of experimental data often requires the simultaneous fitting of multiple model parameters, so that the behavior of the model in a certain paradigm matches (as closely as possible) the corresponding output of a real neuron according to some predefined criterion. Although the task of model optimization is often computationally hard, and the quality of the results depends heavily on technical issues such as the appropriate choice (and implementation) of cost functions and optimization algorithms, no existing program provides access to the best available methods while also guiding the user through the process effectively. Our software, called Optimizer, implements a modular and extensible framework for the optimization of neuronal models, and also features a graphical interface which makes it easy for even non-expert users to handle many commonly occurring scenarios. Meanwhile, educated users can extend the capabilities of the program and customize it according to their needs with relatively little effort. Optimizer has been developed in Python, takes advantage of open-source Python modules for nonlinear optimization, and interfaces directly with the NEURON simulator to run the models. Other simulators are supported through an external interface. We have tested the program on several different types of problems of varying complexity, using different model classes. As targets, we used simulated traces from the same or a more complex model class, as well as experimental data. We successfully used Optimizer to determine passive parameters and conductance densities in compartmental models, and to fit simple (adaptive exponential integrate-and-fire) neuronal models to complex biological data. Our detailed comparisons show that Optimizer can handle a wider range of problems, and delivers equally good or better performance than any other existing neuronal model fitting tool.

  13. A flexible, interactive software tool for fitting the parameters of neuronal models

    Directory of Open Access Journals (Sweden)

    Péter eFriedrich

    2014-07-01

    Full Text Available The construction of biologically relevant neuronal models as well as model-based analysis of experimental data often requires the simultaneous fitting of multiple model parameters, so that the behavior of the model in a certain paradigm matches (as closely as possible the corresponding output of a real neuron according to some predefined criterion. Although the task of model optimization is often computationally hard, and the quality of the results depends heavily on technical issues such as the appropriate choice (and implementation of cost functions and optimization algorithms, no existing program provides access to the best available methods while also guiding the user through the process effectively. Our software, called Optimizer, implements a modular and extensible framework for the optimization of neuronal models, and also features a graphical interface which makes it easy for even non-expert users to handle many commonly occurring scenarios. Meanwhile, educated users can extend the capabilities of the program and customize it according to their needs with relatively little effort. Optimizer has been developed in Python, takes advantage of open-source Python modules for nonlinear optimization, and interfaces directly with the NEURON simulator to run the models. Other simulators are supported through an external interface. We have tested the program on several different types of problem of varying complexity, using different model classes. As targets, we used simulated traces from the same or a more complex model class, as well as experimental data. We successfully used Optimizer to determine passive parameters and conductance densities in compartmental models, and to fit simple (adaptive exponential integrate-and-fire neuronal models to complex biological data. Our detailed comparisons show that Optimizer can handle a wider range of problems, and delivers equally good or better performance than any other existing neuronal model fitting

  14. Auditory information coding by modeled cochlear nucleus neurons.

    Science.gov (United States)

    Wang, Huan; Isik, Michael; Borst, Alexander; Hemmert, Werner

    2011-06-01

    In this paper we use information theory to quantify the information in the output spike trains of modeled cochlear nucleus globular bushy cells (GBCs). GBCs are part of the sound localization pathway. They are known for their precise temporal processing, and they code amplitude modulations with high fidelity. Here we investigated the information transmission for a natural sound, a recorded vowel. We conclude that the maximum information transmission rate for a single neuron was close to 1,050 bits/s, which corresponds to a value of approximately 5.8 bits per spike. For quasi-periodic signals like voiced speech, the transmitted information saturated as word duration increased. In general, approximately 80% of the available information from the spike trains was transmitted within about 20 ms. Transmitted information for speech signals concentrated around formant frequency regions. The efficiency of neural coding was above 60% up to the highest temporal resolution we investigated (20 μs). The increase in transmitted information to that precision indicates that these neurons are able to code information with extremely high fidelity, which is required for sound localization. On the other hand, only 20% of the information was captured when the temporal resolution was reduced to 4 ms. As the temporal resolution of most speech recognition systems is limited to less than 10 ms, this massive information loss might be one of the reasons which are responsible for the lack of noise robustness of these systems.

  15. Spontaneous neuronal activity as a self-organized critical phenomenon

    Science.gov (United States)

    de Arcangelis, L.; Herrmann, H. J.

    2013-01-01

    Neuronal avalanches are a novel mode of activity in neuronal networks, experimentally found in vitro and in vivo, and exhibit a robust critical behaviour. Avalanche activity can be modelled within the self-organized criticality framework, including threshold firing, refractory period and activity-dependent synaptic plasticity. The size and duration distributions confirm that the system acts in a critical state, whose scaling behaviour is very robust. Next, we discuss the temporal organization of neuronal avalanches. This is given by the alternation between states of high and low activity, named up and down states, leading to a balance between excitation and inhibition controlled by a single parameter. During these periods both the single neuron state and the network excitability level, keeping memory of past activity, are tuned by homeostatic mechanisms. Finally, we verify if a system with no characteristic response can ever learn in a controlled and reproducible way. Learning in the model occurs via plastic adaptation of synaptic strengths by a non-uniform negative feedback mechanism. Learning is a truly collective process and the learning dynamics exhibits universal features. Even complex rules can be learned provided that the plastic adaptation is sufficiently slow.

  16. Reinforcement learning of targeted movement in a spiking neuronal model of motor cortex.

    Directory of Open Access Journals (Sweden)

    George L Chadderdon

    Full Text Available Sensorimotor control has traditionally been considered from a control theory perspective, without relation to neurobiology. In contrast, here we utilized a spiking-neuron model of motor cortex and trained it to perform a simple movement task, which consisted of rotating a single-joint "forearm" to a target. Learning was based on a reinforcement mechanism analogous to that of the dopamine system. This provided a global reward or punishment signal in response to decreasing or increasing distance from hand to target, respectively. Output was partially driven by Poisson motor babbling, creating stochastic movements that could then be shaped by learning. The virtual forearm consisted of a single segment rotated around an elbow joint, controlled by flexor and extensor muscles. The model consisted of 144 excitatory and 64 inhibitory event-based neurons, each with AMPA, NMDA, and GABA synapses. Proprioceptive cell input to this model encoded the 2 muscle lengths. Plasticity was only enabled in feedforward connections between input and output excitatory units, using spike-timing-dependent eligibility traces for synaptic credit or blame assignment. Learning resulted from a global 3-valued signal: reward (+1, no learning (0, or punishment (-1, corresponding to phasic increases, lack of change, or phasic decreases of dopaminergic cell firing, respectively. Successful learning only occurred when both reward and punishment were enabled. In this case, 5 target angles were learned successfully within 180 s of simulation time, with a median error of 8 degrees. Motor babbling allowed exploratory learning, but decreased the stability of the learned behavior, since the hand continued moving after reaching the target. Our model demonstrated that a global reinforcement signal, coupled with eligibility traces for synaptic plasticity, can train a spiking sensorimotor network to perform goal-directed motor behavior.

  17. Reinforcement learning of targeted movement in a spiking neuronal model of motor cortex.

    Science.gov (United States)

    Chadderdon, George L; Neymotin, Samuel A; Kerr, Cliff C; Lytton, William W

    2012-01-01

    Sensorimotor control has traditionally been considered from a control theory perspective, without relation to neurobiology. In contrast, here we utilized a spiking-neuron model of motor cortex and trained it to perform a simple movement task, which consisted of rotating a single-joint "forearm" to a target. Learning was based on a reinforcement mechanism analogous to that of the dopamine system. This provided a global reward or punishment signal in response to decreasing or increasing distance from hand to target, respectively. Output was partially driven by Poisson motor babbling, creating stochastic movements that could then be shaped by learning. The virtual forearm consisted of a single segment rotated around an elbow joint, controlled by flexor and extensor muscles. The model consisted of 144 excitatory and 64 inhibitory event-based neurons, each with AMPA, NMDA, and GABA synapses. Proprioceptive cell input to this model encoded the 2 muscle lengths. Plasticity was only enabled in feedforward connections between input and output excitatory units, using spike-timing-dependent eligibility traces for synaptic credit or blame assignment. Learning resulted from a global 3-valued signal: reward (+1), no learning (0), or punishment (-1), corresponding to phasic increases, lack of change, or phasic decreases of dopaminergic cell firing, respectively. Successful learning only occurred when both reward and punishment were enabled. In this case, 5 target angles were learned successfully within 180 s of simulation time, with a median error of 8 degrees. Motor babbling allowed exploratory learning, but decreased the stability of the learned behavior, since the hand continued moving after reaching the target. Our model demonstrated that a global reinforcement signal, coupled with eligibility traces for synaptic plasticity, can train a spiking sensorimotor network to perform goal-directed motor behavior.

  18. Multi-timescale Modeling of Activity-Dependent Metabolic Coupling in the Neuron-Glia-Vasculature Ensemble

    KAUST Repository

    Jolivet, Renaud

    2015-02-26

    Glucose is the main energy substrate in the adult brain under normal conditions. Accumulating evidence, however, indicates that lactate produced in astrocytes (a type of glial cell) can also fuel neuronal activity. The quantitative aspects of this so-called astrocyte-neuron lactate shuttle (ANLS) are still debated. To address this question, we developed a detailed biophysical model of the brain’s metabolic interactions. Our model integrates three modeling approaches, the Buxton-Wang model of vascular dynamics, the Hodgkin-Huxley formulation of neuronal membrane excitability and a biophysical model of metabolic pathways. This approach provides a template for large-scale simulations of the neuron-glia-vasculature (NGV) ensemble, and for the first time integrates the respective timescales at which energy metabolism and neuronal excitability occur. The model is constrained by relative neuronal and astrocytic oxygen and glucose utilization, by the concentration of metabolites at rest and by the temporal dynamics of NADH upon activation. These constraints produced four observations. First, a transfer of lactate from astrocytes to neurons emerged in response to activity. Second, constrained by activity-dependent NADH transients, neuronal oxidative metabolism increased first upon activation with a subsequent delayed astrocytic glycolysis increase. Third, the model correctly predicted the dynamics of extracellular lactate and oxygen as observed in vivo in rats. Fourth, the model correctly predicted the temporal dynamics of tissue lactate, of tissue glucose and oxygen consumption, and of the BOLD signal as reported in human studies. These findings not only support the ANLS hypothesis but also provide a quantitative mathematical description of the metabolic activation in neurons and glial cells, as well as of the macroscopic measurements obtained during brain imaging.

  19. Multi-timescale Modeling of Activity-Dependent Metabolic Coupling in the Neuron-Glia-Vasculature Ensemble

    Science.gov (United States)

    Jolivet, Renaud; Coggan, Jay S.; Allaman, Igor; Magistretti, Pierre J.

    2015-01-01

    Glucose is the main energy substrate in the adult brain under normal conditions. Accumulating evidence, however, indicates that lactate produced in astrocytes (a type of glial cell) can also fuel neuronal activity. The quantitative aspects of this so-called astrocyte-neuron lactate shuttle (ANLS) are still debated. To address this question, we developed a detailed biophysical model of the brain’s metabolic interactions. Our model integrates three modeling approaches, the Buxton-Wang model of vascular dynamics, the Hodgkin-Huxley formulation of neuronal membrane excitability and a biophysical model of metabolic pathways. This approach provides a template for large-scale simulations of the neuron-glia-vasculature (NGV) ensemble, and for the first time integrates the respective timescales at which energy metabolism and neuronal excitability occur. The model is constrained by relative neuronal and astrocytic oxygen and glucose utilization, by the concentration of metabolites at rest and by the temporal dynamics of NADH upon activation. These constraints produced four observations. First, a transfer of lactate from astrocytes to neurons emerged in response to activity. Second, constrained by activity-dependent NADH transients, neuronal oxidative metabolism increased first upon activation with a subsequent delayed astrocytic glycolysis increase. Third, the model correctly predicted the dynamics of extracellular lactate and oxygen as observed in vivo in rats. Fourth, the model correctly predicted the temporal dynamics of tissue lactate, of tissue glucose and oxygen consumption, and of the BOLD signal as reported in human studies. These findings not only support the ANLS hypothesis but also provide a quantitative mathematical description of the metabolic activation in neurons and glial cells, as well as of the macroscopic measurements obtained during brain imaging. PMID:25719367

  20. Multi-timescale modeling of activity-dependent metabolic coupling in the neuron-glia-vasculature ensemble.

    Directory of Open Access Journals (Sweden)

    Renaud Jolivet

    2015-02-01

    Full Text Available Glucose is the main energy substrate in the adult brain under normal conditions. Accumulating evidence, however, indicates that lactate produced in astrocytes (a type of glial cell can also fuel neuronal activity. The quantitative aspects of this so-called astrocyte-neuron lactate shuttle (ANLS are still debated. To address this question, we developed a detailed biophysical model of the brain's metabolic interactions. Our model integrates three modeling approaches, the Buxton-Wang model of vascular dynamics, the Hodgkin-Huxley formulation of neuronal membrane excitability and a biophysical model of metabolic pathways. This approach provides a template for large-scale simulations of the neuron-glia-vasculature (NGV ensemble, and for the first time integrates the respective timescales at which energy metabolism and neuronal excitability occur. The model is constrained by relative neuronal and astrocytic oxygen and glucose utilization, by the concentration of metabolites at rest and by the temporal dynamics of NADH upon activation. These constraints produced four observations. First, a transfer of lactate from astrocytes to neurons emerged in response to activity. Second, constrained by activity-dependent NADH transients, neuronal oxidative metabolism increased first upon activation with a subsequent delayed astrocytic glycolysis increase. Third, the model correctly predicted the dynamics of extracellular lactate and oxygen as observed in vivo in rats. Fourth, the model correctly predicted the temporal dynamics of tissue lactate, of tissue glucose and oxygen consumption, and of the BOLD signal as reported in human studies. These findings not only support the ANLS hypothesis but also provide a quantitative mathematical description of the metabolic activation in neurons and glial cells, as well as of the macroscopic measurements obtained during brain imaging.

  1. The fractional-order modeling and synchronization of electrically coupled neuron systems

    KAUST Repository

    Moaddy, K.

    2012-11-01

    In this paper, we generalize the integer-order cable model of the neuron system into the fractional-order domain, where the long memory dependence of the fractional derivative can be a better fit for the neuron response. Furthermore, the chaotic synchronization with a gap junction of two or multi-coupled-neurons of fractional-order are discussed. The circuit model, fractional-order state equations and the numerical technique are introduced in this paper for individual and multiple coupled neuron systems with different fractional-orders. Various examples are introduced with different fractional orders using the non-standard finite difference scheme together with the Grünwald-Letnikov discretization process which is easily implemented and reliably accurate. © 2011 Elsevier Ltd. All rights reserved.

  2. The fractional-order modeling and synchronization of electrically coupled neuron systems

    KAUST Repository

    Moaddy, K.; Radwan, Ahmed G.; Salama, Khaled N.; Momani, Shaher M.; Hashim, Ishak

    2012-01-01

    In this paper, we generalize the integer-order cable model of the neuron system into the fractional-order domain, where the long memory dependence of the fractional derivative can be a better fit for the neuron response. Furthermore, the chaotic synchronization with a gap junction of two or multi-coupled-neurons of fractional-order are discussed. The circuit model, fractional-order state equations and the numerical technique are introduced in this paper for individual and multiple coupled neuron systems with different fractional-orders. Various examples are introduced with different fractional orders using the non-standard finite difference scheme together with the Grünwald-Letnikov discretization process which is easily implemented and reliably accurate. © 2011 Elsevier Ltd. All rights reserved.

  3. Simultaneous transcranial magnetic stimulation and single-neuron recording in alert non-human primates.

    Science.gov (United States)

    Mueller, Jerel K; Grigsby, Erinn M; Prevosto, Vincent; Petraglia, Frank W; Rao, Hrishikesh; Deng, Zhi-De; Peterchev, Angel V; Sommer, Marc A; Egner, Tobias; Platt, Michael L; Grill, Warren M

    2014-08-01

    Transcranial magnetic stimulation (TMS) is a widely used, noninvasive method for stimulating nervous tissue, yet its mechanisms of effect are poorly understood. Here we report new methods for studying the influence of TMS on single neurons in the brain of alert non-human primates. We designed a TMS coil that focuses its effect near the tip of a recording electrode and recording electronics that enable direct acquisition of neuronal signals at the site of peak stimulus strength minimally perturbed by stimulation artifact in awake monkeys (Macaca mulatta). We recorded action potentials within ∼1 ms after 0.4-ms TMS pulses and observed changes in activity that differed significantly for active stimulation as compared with sham stimulation. This methodology is compatible with standard equipment in primate laboratories, allowing easy implementation. Application of these tools will facilitate the refinement of next generation TMS devices, experiments and treatment protocols.

  4. Simultaneous transcranial magnetic stimulation and single neuron recording in alert non-human primates

    Science.gov (United States)

    Mueller, Jerel K.; Grigsby, Erinn M.; Prevosto, Vincent; Petraglia, Frank W.; Rao, Hrishikesh; Deng, Zhi-De; Peterchev, Angel V.; Sommer, Marc A.; Egner, Tobias; Platt, Michael L.; Grill, Warren M.

    2014-01-01

    Transcranial magnetic stimulation (TMS) is a widely used, noninvasive method for stimulating nervous tissue, yet its mechanisms of effect are poorly understood. Here we report novel methods for studying the influence of TMS on single neurons in the brain of alert non-human primates. We designed a TMS coil that focuses its effect near the tip of a recording electrode and recording electronics that enable direct acquisition of neuronal signals at the site of peak stimulus strength minimally perturbed by stimulation artifact in intact, awake monkeys (Macaca mulatta). We recorded action potentials within ~1 ms after 0.4 ms TMS pulses and observed changes in activity that differed significantly for active stimulation as compared to sham stimulation. The methodology is compatible with standard equipment in primate laboratories, allowing for easy implementation. Application of these new tools will facilitate the refinement of next generation TMS devices, experiments, and treatment protocols. PMID:24974797

  5. Integration of Plasticity Mechanisms within a Single Sensory Neuron of C. elegans Actuates a Memory.

    Science.gov (United States)

    Hawk, Josh D; Calvo, Ana C; Liu, Ping; Almoril-Porras, Agustin; Aljobeh, Ahmad; Torruella-Suárez, María Luisa; Ren, Ivy; Cook, Nathan; Greenwood, Joel; Luo, Linjiao; Wang, Zhao-Wen; Samuel, Aravinthan D T; Colón-Ramos, Daniel A

    2018-01-17

    Neural plasticity, the ability of neurons to change their properties in response to experiences, underpins the nervous system's capacity to form memories and actuate behaviors. How different plasticity mechanisms act together in vivo and at a cellular level to transform sensory information into behavior is not well understood. We show that in Caenorhabditis elegans two plasticity mechanisms-sensory adaptation and presynaptic plasticity-act within a single cell to encode thermosensory information and actuate a temperature preference memory. Sensory adaptation adjusts the temperature range of the sensory neuron (called AFD) to optimize detection of temperature fluctuations associated with migration. Presynaptic plasticity in AFD is regulated by the conserved kinase nPKCε and transforms thermosensory information into a behavioral preference. Bypassing AFD presynaptic plasticity predictably changes learned behavioral preferences without affecting sensory responses. Our findings indicate that two distinct neuroplasticity mechanisms function together through a single-cell logic system to enact thermotactic behavior. VIDEO ABSTRACT. Copyright © 2017 Elsevier Inc. All rights reserved.

  6. Enhanced Sensitivity to Rapid Input Fluctuations by Nonlinear Threshold Dynamics in Neocortical Pyramidal Neurons.

    Science.gov (United States)

    Mensi, Skander; Hagens, Olivier; Gerstner, Wulfram; Pozzorini, Christian

    2016-02-01

    The way in which single neurons transform input into output spike trains has fundamental consequences for network coding. Theories and modeling studies based on standard Integrate-and-Fire models implicitly assume that, in response to increasingly strong inputs, neurons modify their coding strategy by progressively reducing their selective sensitivity to rapid input fluctuations. Combining mathematical modeling with in vitro experiments, we demonstrate that, in L5 pyramidal neurons, the firing threshold dynamics adaptively adjust the effective timescale of somatic integration in order to preserve sensitivity to rapid signals over a broad range of input statistics. For that, a new Generalized Integrate-and-Fire model featuring nonlinear firing threshold dynamics and conductance-based adaptation is introduced that outperforms state-of-the-art neuron models in predicting the spiking activity of neurons responding to a variety of in vivo-like fluctuating currents. Our model allows for efficient parameter extraction and can be analytically mapped to a Generalized Linear Model in which both the input filter--describing somatic integration--and the spike-history filter--accounting for spike-frequency adaptation--dynamically adapt to the input statistics, as experimentally observed. Overall, our results provide new insights on the computational role of different biophysical processes known to underlie adaptive coding in single neurons and support previous theoretical findings indicating that the nonlinear dynamics of the firing threshold due to Na+-channel inactivation regulate the sensitivity to rapid input fluctuations.

  7. [Compared Markov with fractal models by using single-channel experimental and simulation data].

    Science.gov (United States)

    Lan, Tonghan; Wu, Hongxiu; Lin, Jiarui

    2006-10-01

    The gating mechanical kinetical of ion channels has been modeled as a Markov process. In these models it is assumed that the channel protein has a small number of discrete conformational states and kinetic rate constants connecting these states are constant, the transition rate constants among the states is independent both of time and of the previous channel activity. It is assumed in Liebovitch's fractal model that the channel exists in an infinite number of energy states, consequently, transitions from one conductance state to another would be governed by a continuum of rate constants. In this paper, a statistical comparison is presented of Markov and fractal models of ion channel gating, the analysis is based on single-channel data from ion channel voltage-dependence K+ single channel of neuron cell and simulation data from three-states Markov model.

  8. Effects of inhibitory neurons on the quorum percolation model and dynamical extension with the Brette-Gerstner model

    Science.gov (United States)

    Fardet, Tanguy; Bottani, Samuel; Métens, Stéphane; Monceau, Pascal

    2018-06-01

    The Quorum Percolation model (QP) has been designed in the context of neurobiology to describe the initiation of activity bursts occurring in neuronal cultures from the point of view of statistical physics rather than from a dynamical synchronization approach. This paper aims at investigating an extension of the original QP model by taking into account the presence of inhibitory neurons in the cultures (IQP model). The first part of this paper is focused on an equivalence between the presence of inhibitory neurons and a reduction of the network connectivity. By relying on a simple topological argument, we show that the mean activation behavior of networks containing a fraction η of inhibitory neurons can be mapped onto purely excitatory networks with an appropriately modified wiring, provided that η remains in the range usually observed in neuronal cultures, namely η ⪅ 20%. As a striking result, we show that such a mapping enables to predict the evolution of the critical point of the IQP model with the fraction of inhibitory neurons. In a second part, we bridge the gap between the description of bursts in the framework of percolation and the temporal description of neural networks activity by showing how dynamical simulations of bursts with an adaptive exponential integrate-and-fire model lead to a mean description of bursts activation which is captured by Quorum Percolation.

  9. Is the Langevin phase equation an efficient model for oscillating neurons?

    Science.gov (United States)

    Ota, Keisuke; Tsunoda, Takamasa; Omori, Toshiaki; Watanabe, Shigeo; Miyakawa, Hiroyoshi; Okada, Masato; Aonishi, Toru

    2009-12-01

    The Langevin phase model is an important canonical model for capturing coherent oscillations of neural populations. However, little attention has been given to verifying its applicability. In this paper, we demonstrate that the Langevin phase equation is an efficient model for neural oscillators by using the machine learning method in two steps: (a) Learning of the Langevin phase model. We estimated the parameters of the Langevin phase equation, i.e., a phase response curve and the intensity of white noise from physiological data measured in the hippocampal CA1 pyramidal neurons. (b) Test of the estimated model. We verified whether a Fokker-Planck equation derived from the Langevin phase equation with the estimated parameters could capture the stochastic oscillatory behavior of the same neurons disturbed by periodic perturbations. The estimated model could predict the neural behavior, so we can say that the Langevin phase equation is an efficient model for oscillating neurons.

  10. Is the Langevin phase equation an efficient model for oscillating neurons?

    International Nuclear Information System (INIS)

    Ota, Keisuke; Tsunoda, Takamasa; Aonishi, Toru; Omori, Toshiaki; Okada, Masato; Watanabe, Shigeo; Miyakawa, Hiroyoshi

    2009-01-01

    The Langevin phase model is an important canonical model for capturing coherent oscillations of neural populations. However, little attention has been given to verifying its applicability. In this paper, we demonstrate that the Langevin phase equation is an efficient model for neural oscillators by using the machine learning method in two steps: (a) Learning of the Langevin phase model. We estimated the parameters of the Langevin phase equation, i.e., a phase response curve and the intensity of white noise from physiological data measured in the hippocampal CA1 pyramidal neurons. (b) Test of the estimated model. We verified whether a Fokker-Planck equation derived from the Langevin phase equation with the estimated parameters could capture the stochastic oscillatory behavior of the same neurons disturbed by periodic perturbations. The estimated model could predict the neural behavior, so we can say that the Langevin phase equation is an efficient model for oscillating neurons.

  11. Phase Locking of Multiple Single Neurons to the Local Field Potential in Cat V1.

    Science.gov (United States)

    Martin, Kevan A C; Schröder, Sylvia

    2016-02-24

    The local field potential (LFP) is thought to reflect a temporal reference for neuronal spiking, which may facilitate information coding and orchestrate the communication between neural populations. To explore this proposed role, we recorded the LFP and simultaneously the spike activity of one to three nearby neurons in V1 of anesthetized cats during the presentation of drifting sinusoidal gratings, binary dense noise stimuli, and natural movies. In all stimulus conditions and during spontaneous activity, the average LFP power at frequencies >20 Hz was higher when neurons were spiking versus not spiking. The spikes were weakly but significantly phase locked to all frequencies of the LFP. The average spike phase of the LFP was stable across high and low levels of LFP power, but the strength of phase locking at low frequencies (≤10 Hz) increased with increasing LFP power. In a next step, we studied how strong stimulus responses of single neurons are reflected in the LFP and the LFP-spike relationship. We found that LFP power was slightly increased and phase locking was slightly stronger during strong compared with weak stimulus-locked responses. In summary, the coupling strength between high frequencies of the LFP and spikes was not strongly modulated by LFP power, which is thought to reflect spiking synchrony, nor was it strongly influenced by how strongly the neuron was driven by the stimulus. Furthermore, a comparison between neighboring neurons showed no clustering of preferred LFP phase. We argue that hypotheses on the relevance of phase locking in their current form are inconsistent with our findings. Copyright © 2016 the authors 0270-6474/16/362494-09$15.00/0.

  12. Efficient and Cost-Effective Generation of Mature Neurons From Human Induced Pluripotent Stem Cells

    OpenAIRE

    Badja , Cherif; Maleeva , Galyna; El-Yazidi , Claire; Barruet , Emilie; Lasserre , Manon; Tropel , Philippe; Binetruy , Bernard; Bregestovski , Piotr; Magdinier , Frédérique

    2014-01-01

    The authors describe a feeder-free method of generating induced pluripotent stem cells by relying on the use of a chemically defined medium that overcomes the need for embryoid body formation and neuronal rosette isolation for neuronal precursors and terminally differentiated neuron production. This specific and efficient single-step strategy allows the production of mature neurons in 20–40 days with multiple applications, especially for modeling human pathologies.

  13. LFPy: A tool for biophysical simulation of extracellular potentials generated by detailed model neurons

    Directory of Open Access Journals (Sweden)

    Henrik eLindén

    2014-01-01

    Full Text Available Electrical extracellular recordings, i.e., recordings of the electrical potentials in the extracellular medium between cells, have been a main work-horse in electrophysiology for almost a century. The high-frequency part of the signal (>=500 Hz, i.e., themulti-unit activity (MUA, contains information about the firing of action potentials in surrounding neurons, while the low-frequency part, the local field potential (LFP, contains information about how these neurons integrate synaptic inputs. As the recorded extracellular signals arise from multiple neural processes, their interpretation is typically ambiguous and difficult. Fortunately, a precise biophysical modeling scheme linking activity at the cellular level and the recorded signal has been established: the extracellular potential can be calculated as a weighted sum of all transmembrane currents in all cells located in the vicinity of the electrode. This computational scheme can considerably aid the modeling and analysis of MUA and LFP signals.Here, we describe LFPy, an open source Python package for numerical simulations of extracellular potentials. LFPy consists of a set of easy-to-use classes for defining cells, synapses and recording electrodes as Python objects, implementing this biophysical modeling scheme. It runs on top of the widely used NEURON simulation environment, which allows for flexible usage of both new and existing cell models.Further, calculation of extracellular potentials using the line-source-method is efficiently implemented.We describe the theoretical framework underlying the extracellular potential calculations and illustrate by examples how LFPy can be used both for simulating LFPs, i.e., synaptic contributions from single cells as well a populations of cells, and MUAs, i.e., extracellular signatures of action potentials.

  14. LFPy: a tool for biophysical simulation of extracellular potentials generated by detailed model neurons.

    Science.gov (United States)

    Lindén, Henrik; Hagen, Espen; Lęski, Szymon; Norheim, Eivind S; Pettersen, Klas H; Einevoll, Gaute T

    2013-01-01

    Electrical extracellular recordings, i.e., recordings of the electrical potentials in the extracellular medium between cells, have been a main work-horse in electrophysiology for almost a century. The high-frequency part of the signal (≳500 Hz), i.e., the multi-unit activity (MUA), contains information about the firing of action potentials in surrounding neurons, while the low-frequency part, the local field potential (LFP), contains information about how these neurons integrate synaptic inputs. As the recorded extracellular signals arise from multiple neural processes, their interpretation is typically ambiguous and difficult. Fortunately, a precise biophysical modeling scheme linking activity at the cellular level and the recorded signal has been established: the extracellular potential can be calculated as a weighted sum of all transmembrane currents in all cells located in the vicinity of the electrode. This computational scheme can considerably aid the modeling and analysis of MUA and LFP signals. Here, we describe LFPy, an open source Python package for numerical simulations of extracellular potentials. LFPy consists of a set of easy-to-use classes for defining cells, synapses and recording electrodes as Python objects, implementing this biophysical modeling scheme. It runs on top of the widely used NEURON simulation environment, which allows for flexible usage of both new and existing cell models. Further, calculation of extracellular potentials using the line-source-method is efficiently implemented. We describe the theoretical framework underlying the extracellular potential calculations and illustrate by examples how LFPy can be used both for simulating LFPs, i.e., synaptic contributions from single cells as well a populations of cells, and MUAs, i.e., extracellular signatures of action potentials.

  15. Model-Based Design of Stimulus Trains for Selective Microstimulation of Targeted Neuronal Populations

    National Research Council Canada - National Science Library

    McIntyre, Cameron

    2001-01-01

    ... that accurately reproduced the dynamic firing properties of mammalian neurons, The neuron models were coupled to a three-dimensional finite element model of the spinal cord that solved for the potentials...

  16. Functional crosstalk in culture between macrophages and trigeminal sensory neurons of a mouse genetic model of migraine

    Directory of Open Access Journals (Sweden)

    Franceschini Alessia

    2012-11-01

    Full Text Available Abstract Background Enhanced activity of trigeminal ganglion neurons is thought to underlie neuronal sensitization facilitating the onset of chronic pain attacks, including migraine. Recurrent headache attacks might establish a chronic neuroinflammatory ganglion profile contributing to the hypersensitive phenotype. Since it is difficult to study this process in vivo, we investigated functional crosstalk between macrophages and sensory neurons in primary cultures from trigeminal sensory ganglia of wild-type (WT or knock-in (KI mice expressing the Cacna1a gene mutation (R192Q found in familial hemiplegic migraine-type 1. After studying the number and morphology of resident macrophages in culture, the consequences of adding host macrophages on macrophage phagocytosis and membrane currents mediated by pain-transducing P2X3 receptors on sensory neurons were examined. Results KI ganglion cultures constitutively contained a larger number of active macrophages, although no difference in P2X3 receptor expression was found. Co-culturing WT or KI ganglia with host macrophages (active as much as resident cells strongly stimulated single cell phagocytosis. The same protocol had no effect on P2X3 receptor expression in WT or KI co-cultures, but it largely enhanced WT neuron currents that grew to the high amplitude constitutively seen for KI neurons. No further potentiation of KI neuronal currents was observed. Conclusions Trigeminal ganglion cultures from a genetic mouse model of migraine showed basal macrophage activation together with enhanced neuronal currents mediated by P2X3 receptors. This phenotype could be replicated in WT cultures by adding host macrophages, indicating an important functional crosstalk between macrophages and sensory neurons.

  17. Reciprocal cholinergic and GABAergic modulation of the small ventrolateral pacemaker neurons of Drosophila's circadian clock neuron network.

    Science.gov (United States)

    Lelito, Katherine R; Shafer, Orie T

    2012-04-01

    The relatively simple clock neuron network of Drosophila is a valuable model system for the neuronal basis of circadian timekeeping. Unfortunately, many key neuronal classes of this network are inaccessible to electrophysiological analysis. We have therefore adopted the use of genetically encoded sensors to address the physiology of the fly's circadian clock network. Using genetically encoded Ca(2+) and cAMP sensors, we have investigated the physiological responses of two specific classes of clock neuron, the large and small ventrolateral neurons (l- and s-LN(v)s), to two neurotransmitters implicated in their modulation: acetylcholine (ACh) and γ-aminobutyric acid (GABA). Live imaging of l-LN(v) cAMP and Ca(2+) dynamics in response to cholinergic agonist and GABA application were well aligned with published electrophysiological data, indicating that our sensors were capable of faithfully reporting acute physiological responses to these transmitters within single adult clock neuron soma. We extended these live imaging methods to s-LN(v)s, critical neuronal pacemakers whose physiological properties in the adult brain are largely unknown. Our s-LN(v) experiments revealed the predicted excitatory responses to bath-applied cholinergic agonists and the predicted inhibitory effects of GABA and established that the antagonism of ACh and GABA extends to their effects on cAMP signaling. These data support recently published but physiologically untested models of s-LN(v) modulation and lead to the prediction that cholinergic and GABAergic inputs to s-LN(v)s will have opposing effects on the phase and/or period of the molecular clock within these critical pacemaker neurons.

  18. Single-photon emission computed tomographic findings and motor neuron signs in amyotrophic lateral sclerosis

    Energy Technology Data Exchange (ETDEWEB)

    Terao, Shin-ichi; Sobue, Gen; Higashi, Naoki; Takahashi, Masahiko; Suga, Hidemichi; Mitsuma, Terunori [Aichi Medical Univ., Nagakute (Japan)

    1995-03-01

    {sup 123}I-amphetamine-single photon emission computed tomography (SPECT) was performed on 16 patients with amyotrophic lateral sclerosis (ALS) to investigate the correlation between regional cerebral blood flow (rCBF) and upper motor neuron signs. Significant decreased blood flow less than 2 SDs below the mean of controls was observed in the frontal lobe in 4 patients (25%) and in the frontoparietal lobe including the cortical motor area in 4 patients, respectively. The severity of extermity muscular weakness was significantly correlate with decrease in blood flow through the frontal lobe (p<0.05) and through the frontoparietal lobe (p<0.001). A significant correlation was also noted to exist between the severity of bulbar paralysis and decrease in blood flow through the frontoparietal lobe. No correlation, however, was observed between rCBF and severity of spasticity, presence or absence of Babinski`s sign and the duration of illness. Although muscular weakness in the limbs and bulbar paralysis are not pure upper motor neuron signs, the observed reduction in blood flow through the frontal or frontoparietal lobes appears to reflect extensive progression of functional or organic lesions of cortical neurons including the motor area. (author).

  19. Single-photon emission computed tomographic findings and motor neuron signs in amyotrophic lateral sclerosis

    International Nuclear Information System (INIS)

    Terao, Shin-ichi; Sobue, Gen; Higashi, Naoki; Takahashi, Masahiko; Suga, Hidemichi; Mitsuma, Terunori

    1995-01-01

    123 I-amphetamine-single photon emission computed tomography (SPECT) was performed on 16 patients with amyotrophic lateral sclerosis (ALS) to investigate the correlation between regional cerebral blood flow (rCBF) and upper motor neuron signs. Significant decreased blood flow less than 2 SDs below the mean of controls was observed in the frontal lobe in 4 patients (25%) and in the frontoparietal lobe including the cortical motor area in 4 patients, respectively. The severity of extermity muscular weakness was significantly correlate with decrease in blood flow through the frontal lobe (p<0.05) and through the frontoparietal lobe (p<0.001). A significant correlation was also noted to exist between the severity of bulbar paralysis and decrease in blood flow through the frontoparietal lobe. No correlation, however, was observed between rCBF and severity of spasticity, presence or absence of Babinski's sign and the duration of illness. Although muscular weakness in the limbs and bulbar paralysis are not pure upper motor neuron signs, the observed reduction in blood flow through the frontal or frontoparietal lobes appears to reflect extensive progression of functional or organic lesions of cortical neurons including the motor area. (author)

  20. Dynamical modeling of the moth pheromone-sensitive olfactory receptor neuron within its sensillar environment.

    Directory of Open Access Journals (Sweden)

    Yuqiao Gu

    Full Text Available In insects, olfactory receptor neurons (ORNs, surrounded with auxiliary cells and protected by a cuticular wall, form small discrete sensory organs--the sensilla. The moth pheromone-sensitive sensillum is a well studied example of hair-like sensillum that is favorable to both experimental and modeling investigations. The model presented takes into account both the molecular processes of ORNs, i.e. the biochemical reactions and ionic currents giving rise to the receptor potential, and the cellular organization and compartmentalization of the organ represented by an electrical circuit. The number of isopotential compartments needed to describe the long dendrite bearing pheromone receptors was determined. The transduction parameters that must be modified when the number of compartments is increased were identified. This model reproduces the amplitude and time course of the experimentally recorded receptor potential. A first complete version of the model was analyzed in response to pheromone pulses of various strengths. It provided a quantitative description of the spatial and temporal evolution of the pheromone-dependent conductances, currents and potentials along the outer dendrite and served to determine the contribution of the various steps in the cascade to its global sensitivity. A second simplified version of the model, utilizing a single depolarizing conductance and leak conductances for repolarizing the ORN, was derived from the first version. It served to analyze the effects on the sensory properties of varying the electrical parameters and the size of the main sensillum parts. The consequences of the results obtained on the still uncertain mechanisms of olfactory transduction in moth ORNs--involvement or not of G-proteins, role of chloride and potassium currents--are discussed as well as the optimality of the sensillum organization, the dependence of biochemical parameters on the neuron spatial extension and the respective contributions

  1. A principled dimension-reduction method for the population density approach to modeling networks of neurons with synaptic dynamics.

    Science.gov (United States)

    Ly, Cheng

    2013-10-01

    The population density approach to neural network modeling has been utilized in a variety of contexts. The idea is to group many similar noisy neurons into populations and track the probability density function for each population that encompasses the proportion of neurons with a particular state rather than simulating individual neurons (i.e., Monte Carlo). It is commonly used for both analytic insight and as a time-saving computational tool. The main shortcoming of this method is that when realistic attributes are incorporated in the underlying neuron model, the dimension of the probability density function increases, leading to intractable equations or, at best, computationally intensive simulations. Thus, developing principled dimension-reduction methods is essential for the robustness of these powerful methods. As a more pragmatic tool, it would be of great value for the larger theoretical neuroscience community. For exposition of this method, we consider a single uncoupled population of leaky integrate-and-fire neurons receiving external excitatory synaptic input only. We present a dimension-reduction method that reduces a two-dimensional partial differential-integral equation to a computationally efficient one-dimensional system and gives qualitatively accurate results in both the steady-state and nonequilibrium regimes. The method, termed modified mean-field method, is based entirely on the governing equations and not on any auxiliary variables or parameters, and it does not require fine-tuning. The principles of the modified mean-field method have potential applicability to more realistic (i.e., higher-dimensional) neural networks.

  2. Population density models of integrate-and-fire neurons with jumps: well-posedness.

    Science.gov (United States)

    Dumont, Grégory; Henry, Jacques

    2013-09-01

    In this paper we study the well-posedness of different models of population of leaky integrate-and-fire neurons with a population density approach. The synaptic interaction between neurons is modeled by a potential jump at the reception of a spike. We study populations that are self excitatory or self inhibitory. We distinguish the cases where this interaction is instantaneous from the one where there is a repartition of conduction delays. In the case of a bounded density of delays both excitatory and inhibitory population models are shown to be well-posed. But without conduction delay the solution of the model of self excitatory neurons may blow up. We analyze the different behaviours of the model with jumps compared to its diffusion approximation.

  3. Mirror Neurons Modeled Through Spike-Timing-Dependent Plasticity are Affected by Channelopathies Associated with Autism Spectrum Disorder.

    Science.gov (United States)

    Antunes, Gabriela; Faria da Silva, Samuel F; Simoes de Souza, Fabio M

    2018-06-01

    Mirror neurons fire action potentials both when the agent performs a certain behavior and watches someone performing a similar action. Here, we present an original mirror neuron model based on the spike-timing-dependent plasticity (STDP) between two morpho-electrical models of neocortical pyramidal neurons. Both neurons fired spontaneously with basal firing rate that follows a Poisson distribution, and the STDP between them was modeled by the triplet algorithm. Our simulation results demonstrated that STDP is sufficient for the rise of mirror neuron function between the pairs of neocortical neurons. This is a proof of concept that pairs of neocortical neurons associating sensory inputs to motor outputs could operate like mirror neurons. In addition, we used the mirror neuron model to investigate whether channelopathies associated with autism spectrum disorder could impair the modeled mirror function. Our simulation results showed that impaired hyperpolarization-activated cationic currents (Ih) affected the mirror function between the pairs of neocortical neurons coupled by STDP.

  4. A Dynamic Bayesian Model for Characterizing Cross-Neuronal Interactions During Decision-Making.

    Science.gov (United States)

    Zhou, Bo; Moorman, David E; Behseta, Sam; Ombao, Hernando; Shahbaba, Babak

    2016-01-01

    The goal of this paper is to develop a novel statistical model for studying cross-neuronal spike train interactions during decision making. For an individual to successfully complete the task of decision-making, a number of temporally-organized events must occur: stimuli must be detected, potential outcomes must be evaluated, behaviors must be executed or inhibited, and outcomes (such as reward or no-reward) must be experienced. Due to the complexity of this process, it is likely the case that decision-making is encoded by the temporally-precise interactions between large populations of neurons. Most existing statistical models, however, are inadequate for analyzing such a phenomenon because they provide only an aggregated measure of interactions over time. To address this considerable limitation, we propose a dynamic Bayesian model which captures the time-varying nature of neuronal activity (such as the time-varying strength of the interactions between neurons). The proposed method yielded results that reveal new insight into the dynamic nature of population coding in the prefrontal cortex during decision making. In our analysis, we note that while some neurons in the prefrontal cortex do not synchronize their firing activity until the presence of a reward, a different set of neurons synchronize their activity shortly after stimulus onset. These differentially synchronizing sub-populations of neurons suggests a continuum of population representation of the reward-seeking task. Secondly, our analyses also suggest that the degree of synchronization differs between the rewarded and non-rewarded conditions. Moreover, the proposed model is scalable to handle data on many simultaneously-recorded neurons and is applicable to analyzing other types of multivariate time series data with latent structure. Supplementary materials (including computer codes) for our paper are available online.

  5. Automatic parameter estimation of multicompartmental neuron models via minimization of trace error with control adjustment.

    Science.gov (United States)

    Brookings, Ted; Goeritz, Marie L; Marder, Eve

    2014-11-01

    We describe a new technique to fit conductance-based neuron models to intracellular voltage traces from isolated biological neurons. The biological neurons are recorded in current-clamp with pink (1/f) noise injected to perturb the activity of the neuron. The new algorithm finds a set of parameters that allows a multicompartmental model neuron to match the recorded voltage trace. Attempting to match a recorded voltage trace directly has a well-known problem: mismatch in the timing of action potentials between biological and model neuron is inevitable and results in poor phenomenological match between the model and data. Our approach avoids this by applying a weak control adjustment to the model to promote alignment during the fitting procedure. This approach is closely related to the control theoretic concept of a Luenberger observer. We tested this approach on synthetic data and on data recorded from an anterior gastric receptor neuron from the stomatogastric ganglion of the crab Cancer borealis. To test the flexibility of this approach, the synthetic data were constructed with conductance models that were different from the ones used in the fitting model. For both synthetic and biological data, the resultant models had good spike-timing accuracy. Copyright © 2014 the American Physiological Society.

  6. Minimal time spiking in various ChR2-controlled neuron models.

    Science.gov (United States)

    Renault, Vincent; Thieullen, Michèle; Trélat, Emmanuel

    2018-02-01

    We use conductance based neuron models, and the mathematical modeling of optogenetics to define controlled neuron models and we address the minimal time control of these affine systems for the first spike from equilibrium. We apply tools of geometric optimal control theory to study singular extremals, and we implement a direct method to compute optimal controls. When the system is too large to theoretically investigate the existence of singular optimal controls, we observe numerically the optimal bang-bang controls.

  7. A map of octopaminergic neurons in the Drosophila brain.

    Science.gov (United States)

    Busch, Sebastian; Selcho, Mareike; Ito, Kei; Tanimoto, Hiromu

    2009-04-20

    The biogenic amine octopamine modulates diverse behaviors in invertebrates. At the single neuron level, the mode of action is well understood in the peripheral nervous system owing to its simple structure and accessibility. For elucidating the role of individual octopaminergic neurons in the modulation of complex behaviors, a detailed analysis of the connectivity in the central nervous system is required. Here we present a comprehensive anatomical map of candidate octopaminergic neurons in the adult Drosophila brain: including the supra- and subesophageal ganglia. Application of the Flp-out technique enabled visualization of 27 types of individual octopaminergic neurons. Based on their morphology and distribution of genetic markers, we found that most octopaminergic neurons project to multiple brain structures with a clear separation of dendritic and presynaptic regions. Whereas their major dendrites are confined to specific brain regions, each cell type targets different, yet defined, neuropils distributed throughout the central nervous system. This would allow them to constitute combinatorial modules assigned to the modulation of distinct neuronal processes. The map may provide an anatomical framework for the functional constitution of the octopaminergic system. It also serves as a model for the single-cell organization of a particular neurotransmitter in the brain. 2009 Wiley-Liss, Inc.

  8. Single-Cell RNA-Seq of Mouse Dopaminergic Neurons Informs Candidate Gene Selection for Sporadic Parkinson Disease.

    Science.gov (United States)

    Hook, Paul W; McClymont, Sarah A; Cannon, Gabrielle H; Law, William D; Morton, A Jennifer; Goff, Loyal A; McCallion, Andrew S

    2018-03-01

    Genetic variation modulating risk of sporadic Parkinson disease (PD) has been primarily explored through genome-wide association studies (GWASs). However, like many other common genetic diseases, the impacted genes remain largely unknown. Here, we used single-cell RNA-seq to characterize dopaminergic (DA) neuron populations in the mouse brain at embryonic and early postnatal time points. These data facilitated unbiased identification of DA neuron subpopulations through their unique transcriptional profiles, including a postnatal neuroblast population and substantia nigra (SN) DA neurons. We use these population-specific data to develop a scoring system to prioritize candidate genes in all 49 GWAS intervals implicated in PD risk, including genes with known PD associations and many with extensive supporting literature. As proof of principle, we confirm that the nigrostriatal pathway is compromised in Cplx1-null mice. Ultimately, this systematic approach establishes biologically pertinent candidates and testable hypotheses for sporadic PD, informing a new era of PD genetic research. Copyright © 2018 American Society of Human Genetics. All rights reserved.

  9. Colored noise and memory effects on formal spiking neuron models

    Science.gov (United States)

    da Silva, L. A.; Vilela, R. D.

    2015-06-01

    Simplified neuronal models capture the essence of the electrical activity of a generic neuron, besides being more interesting from the computational point of view when compared to higher-dimensional models such as the Hodgkin-Huxley one. In this work, we propose a generalized resonate-and-fire model described by a generalized Langevin equation that takes into account memory effects and colored noise. We perform a comprehensive numerical analysis to study the dynamics and the point process statistics of the proposed model, highlighting interesting new features such as (i) nonmonotonic behavior (emergence of peak structures, enhanced by the choice of colored noise characteristic time scale) of the coefficient of variation (CV) as a function of memory characteristic time scale, (ii) colored noise-induced shift in the CV, and (iii) emergence and suppression of multimodality in the interspike interval (ISI) distribution due to memory-induced subthreshold oscillations. Moreover, in the noise-induced spike regime, we study how memory and colored noise affect the coherence resonance (CR) phenomenon. We found that for sufficiently long memory, not only is CR suppressed but also the minimum of the CV-versus-noise intensity curve that characterizes the presence of CR may be replaced by a maximum. The aforementioned features allow to interpret the interplay between memory and colored noise as an effective control mechanism to neuronal variability. Since both variability and nontrivial temporal patterns in the ISI distribution are ubiquitous in biological cells, we hope the present model can be useful in modeling real aspects of neurons.

  10. Studying the Behaviour of Model of Mirror Neuron System in Case of Autism

    Directory of Open Access Journals (Sweden)

    Shikha Anirban

    2012-04-01

    Full Text Available Several experiment done by the researchers conducted that autism is caused by the dysfunctional mirror neuron system and the dysfunctions of mirror neuron system is proportional to the symptom severity of autism. In the present work those experiments were studied as well as studying a model of mirror neuron system called MNS2 developed by a research group. This research examined the behavior of the model in case of autism and compared the result with those studies conducting dysfunctions of mirror neuron system in autism. To perform this, a neural network employing the model was developed which recognized the three types of grasping (faster, normal and slower. The network was implemented with back propagation through time learning algorithm. The whole grasping process was divided into 30 time steps and different hand and object states at each time step was used as the input of the network. Normally the network successfully recognized all of the three types of grasps. The network required more times as the number of inactive neurons increased. And in case of maximum inactive neurons of the mirror neuron system the network became unable to recognize the types of grasp. As the time to recognize the types of grasp is proportional to the number of inactive neurons, the experiment result supports the hypothesis that dysfunctions of MNS is proportional to the symptom severity of autism. Keywords— Autism, MNS, mirror neuron, neural network, BPTT

  11. Codimension-two bifurcation analysis on firing activities in Chay neuron model

    International Nuclear Information System (INIS)

    Duan Lixia; Lu Qishao

    2006-01-01

    Using codimension-two bifurcation analysis in the Chay neuron model, the relationship between the electric activities and the parameters of neurons is revealed. The whole parameter space is divided into two parts, that is, the firing and silence regions of neurons. It is found that the transition sets between firing and silence regions are composed of the Hopf bifurcation curves of equilibrium states and the saddle-node bifurcation curves of limit cycles, with some codimension-two bifurcation points. The transitions from silence to firing in neurons are due to the Hopf bifurcation or the fold limit cycle bifurcation, but the codimension-two singularities lead to complexity in dynamical behaviour of neuronal firing

  12. Codimension-two bifurcation analysis on firing activities in Chay neuron model

    Energy Technology Data Exchange (ETDEWEB)

    Duan Lixia [School of Science, Beijing University of Aeronautics and Astronautics, Beijing 100083 (China); Lu Qishao [School of Science, Beijing University of Aeronautics and Astronautics, Beijing 100083 (China)]. E-mail: qishaolu@hotmail.com

    2006-12-15

    Using codimension-two bifurcation analysis in the Chay neuron model, the relationship between the electric activities and the parameters of neurons is revealed. The whole parameter space is divided into two parts, that is, the firing and silence regions of neurons. It is found that the transition sets between firing and silence regions are composed of the Hopf bifurcation curves of equilibrium states and the saddle-node bifurcation curves of limit cycles, with some codimension-two bifurcation points. The transitions from silence to firing in neurons are due to the Hopf bifurcation or the fold limit cycle bifurcation, but the codimension-two singularities lead to complexity in dynamical behaviour of neuronal firing.

  13. A single hidden layer feedforward network with only one neuron in the hidden layer can approximate any univariate function

    OpenAIRE

    Guliyev , Namig; Ismailov , Vugar

    2016-01-01

    The possibility of approximating a continuous function on a compact subset of the real line by a feedforward single hidden layer neural network with a sigmoidal activation function has been studied in many papers. Such networks can approximate an arbitrary continuous function provided that an unlimited number of neurons in a hidden layer is permitted. In this paper, we consider constructive approximation on any finite interval of $\\mathbb{R}$ by neural networks with only one neuron in the hid...

  14. Emphasis of spatial cues in the temporal fine structure during the rising segments of amplitude-modulated sounds II: single-neuron recordings

    Science.gov (United States)

    Marquardt, Torsten; Stange, Annette; Pecka, Michael; Grothe, Benedikt; McAlpine, David

    2014-01-01

    Recently, with the use of an amplitude-modulated binaural beat (AMBB), in which sound amplitude and interaural-phase difference (IPD) were modulated with a fixed mutual relationship (Dietz et al. 2013b), we demonstrated that the human auditory system uses interaural timing differences in the temporal fine structure of modulated sounds only during the rising portion of each modulation cycle. However, the degree to which peripheral or central mechanisms contribute to the observed strong dominance of the rising slope remains to be determined. Here, by recording responses of single neurons in the medial superior olive (MSO) of anesthetized gerbils and in the inferior colliculus (IC) of anesthetized guinea pigs to AMBBs, we report a correlation between the position within the amplitude-modulation (AM) cycle generating the maximum response rate and the position at which the instantaneous IPD dominates the total neural response. The IPD during the rising segment dominates the total response in 78% of MSO neurons and 69% of IC neurons, with responses of the remaining neurons predominantly coding the IPD around the modulation maximum. The observed diversity of dominance regions within the AM cycle, especially in the IC, and its comparison with the human behavioral data suggest that only the subpopulation of neurons with rising slope dominance codes the sound-source location in complex listening conditions. A comparison of two models to account for the data suggests that emphasis on IPDs during the rising slope of the AM cycle depends on adaptation processes occurring before binaural interaction. PMID:24554782

  15. A codimension-2 bifurcation controlling endogenous bursting activity and pulse-triggered responses of a neuron model.

    Science.gov (United States)

    Barnett, William H; Cymbalyuk, Gennady S

    2014-01-01

    The dynamics of individual neurons are crucial for producing functional activity in neuronal networks. An open question is how temporal characteristics can be controlled in bursting activity and in transient neuronal responses to synaptic input. Bifurcation theory provides a framework to discover generic mechanisms addressing this question. We present a family of mechanisms organized around a global codimension-2 bifurcation. The cornerstone bifurcation is located at the intersection of the border between bursting and spiking and the border between bursting and silence. These borders correspond to the blue sky catastrophe bifurcation and the saddle-node bifurcation on an invariant circle (SNIC) curves, respectively. The cornerstone bifurcation satisfies the conditions for both the blue sky catastrophe and SNIC. The burst duration and interburst interval increase as the inverse of the square root of the difference between the corresponding bifurcation parameter and its bifurcation value. For a given set of burst duration and interburst interval, one can find the parameter values supporting these temporal characteristics. The cornerstone bifurcation also determines the responses of silent and spiking neurons. In a silent neuron with parameters close to the SNIC, a pulse of current triggers a single burst. In a spiking neuron with parameters close to the blue sky catastrophe, a pulse of current temporarily silences the neuron. These responses are stereotypical: the durations of the transient intervals-the duration of the burst and the duration of latency to spiking-are governed by the inverse-square-root laws. The mechanisms described here could be used to coordinate neuromuscular control in central pattern generators. As proof of principle, we construct small networks that control metachronal-wave motor pattern exhibited in locomotion. This pattern is determined by the phase relations of bursting neurons in a simple central pattern generator modeled by a chain of

  16. BigNeuron: Large-Scale 3D Neuron Reconstruction from Optical Microscopy Images

    NARCIS (Netherlands)

    H. Peng (Hanchuan); M. Hawrylycz (Michael); J. Roskams (Jane); S. Hill (Sean); N. Spruston (Nelson); E. Meijering (Erik); G.A. Ascoli (Giorgio A.)

    2015-01-01

    textabstractUnderstanding the structure of single neurons is critical for understanding how they function within neural circuits. BigNeuron is a new community effort that combines modern bioimaging informatics, recent leaps in labeling and microscopy, and the widely recognized need for openness and

  17. Spike timing precision of neuronal circuits.

    Science.gov (United States)

    Kilinc, Deniz; Demir, Alper

    2018-04-17

    Spike timing is believed to be a key factor in sensory information encoding and computations performed by the neurons and neuronal circuits. However, the considerable noise and variability, arising from the inherently stochastic mechanisms that exist in the neurons and the synapses, degrade spike timing precision. Computational modeling can help decipher the mechanisms utilized by the neuronal circuits in order to regulate timing precision. In this paper, we utilize semi-analytical techniques, which were adapted from previously developed methods for electronic circuits, for the stochastic characterization of neuronal circuits. These techniques, which are orders of magnitude faster than traditional Monte Carlo type simulations, can be used to directly compute the spike timing jitter variance, power spectral densities, correlation functions, and other stochastic characterizations of neuronal circuit operation. We consider three distinct neuronal circuit motifs: Feedback inhibition, synaptic integration, and synaptic coupling. First, we show that both the spike timing precision and the energy efficiency of a spiking neuron are improved with feedback inhibition. We unveil the underlying mechanism through which this is achieved. Then, we demonstrate that a neuron can improve on the timing precision of its synaptic inputs, coming from multiple sources, via synaptic integration: The phase of the output spikes of the integrator neuron has the same variance as that of the sample average of the phases of its inputs. Finally, we reveal that weak synaptic coupling among neurons, in a fully connected network, enables them to behave like a single neuron with a larger membrane area, resulting in an improvement in the timing precision through cooperation.

  18. Generalized rate-code model for neuron ensembles with finite populations

    International Nuclear Information System (INIS)

    Hasegawa, Hideo

    2007-01-01

    We have proposed a generalized Langevin-type rate-code model subjected to multiplicative noise, in order to study stationary and dynamical properties of an ensemble containing a finite number N of neurons. Calculations using the Fokker-Planck equation have shown that, owing to the multiplicative noise, our rate model yields various kinds of stationary non-Gaussian distributions such as Γ, inverse-Gaussian-like, and log-normal-like distributions, which have been experimentally observed. The dynamical properties of the rate model have been studied with the use of the augmented moment method (AMM), which was previously proposed by the author from a macroscopic point of view for finite-unit stochastic systems. In the AMM, the original N-dimensional stochastic differential equations (DEs) are transformed into three-dimensional deterministic DEs for the means and fluctuations of local and global variables. The dynamical responses of the neuron ensemble to pulse and sinusoidal inputs calculated by the AMM are in good agreement with those obtained by direct simulation. The synchronization in the neuronal ensemble is discussed. The variabilities of the firing rate and of the interspike interval are shown to increase with increasing magnitude of multiplicative noise, which may be a conceivable origin of the observed large variability in cortical neurons

  19. Operant conditioning of synaptic and spiking activity patterns in single hippocampal neurons.

    Science.gov (United States)

    Ishikawa, Daisuke; Matsumoto, Nobuyoshi; Sakaguchi, Tetsuya; Matsuki, Norio; Ikegaya, Yuji

    2014-04-02

    Learning is a process of plastic adaptation through which a neural circuit generates a more preferable outcome; however, at a microscopic level, little is known about how synaptic activity is patterned into a desired configuration. Here, we report that animals can generate a specific form of synaptic activity in a given neuron in the hippocampus. In awake, head-restricted mice, we applied electrical stimulation to the lateral hypothalamus, a reward-associated brain region, when whole-cell patch-clamped CA1 neurons exhibited spontaneous synaptic activity that met preset criteria. Within 15 min, the mice learned to generate frequently the excitatory synaptic input pattern that satisfied the criteria. This reinforcement learning of synaptic activity was not observed for inhibitory input patterns. When a burst unit activity pattern was conditioned in paired and nonpaired paradigms, the frequency of burst-spiking events increased and decreased, respectively. The burst reinforcement occurred in the conditioned neuron but not in other adjacent neurons; however, ripple field oscillations were concomitantly reinforced. Neural conditioning depended on activation of NMDA receptors and dopamine D1 receptors. Acutely stressed mice and depression model mice that were subjected to forced swimming failed to exhibit the neural conditioning. This learning deficit was rescued by repetitive treatment with fluoxetine, an antidepressant. Therefore, internally motivated animals are capable of routing an ongoing action potential series into a specific neural pathway of the hippocampal network.

  20. Diffusion approximation of neuronal models revisited

    Czech Academy of Sciences Publication Activity Database

    Čupera, Jakub

    2014-01-01

    Roč. 11, č. 1 (2014), s. 11-25 ISSN 1547-1063. [International Workshop on Neural Coding (NC) /10./. Praha, 02.09.2012-07.09.2012] R&D Projects: GA ČR(CZ) GAP103/11/0282 Institutional support: RVO:67985823 Keywords : stochastic model * neuronal activity * first-passage time Subject RIV: JD - Computer Applications, Robotics Impact factor: 0.840, year: 2014

  1. Not a single but multiple populations of GABAergic neurons control sleep.

    Science.gov (United States)

    Luppi, Pierre-Hervé; Peyron, Christelle; Fort, Patrice

    2017-04-01

    The role of gamma-amino butyric acid (GABA) in sleep induction and maintenance is well accepted since most insomnia treatments target GABAa receptors. However, the population(s) of GABAergic neurons involved in the beneficial effect of GABA on sleep remains to be identified. This is not an easy task since GABAergic neurons are widely distributed in all brain structures. A recently growing number of populations of GABAergic neurons have been involved in sleep control. We first review here possible candidates for inducing non-rapid eye movement (NREM) sleep including the GABAergic neurons of the ventrolateral preoptic area, the parafacial zone in the brainstem, the nucleus accumbens and the cortex. We also discuss the role of several populations of GABAergic neurons in rapid eye movement (REM) sleep control. Indeed, it is well accepted that muscle atonia occurring during REM sleep is due to a GABA/glycinergic hyperpolarization of motoneurons. Recent evidence strongly suggests that these neurons are located in the ventral medullary reticular formation. It has also recently been shown that neurons containing the neuropeptide melanin concentrating hormone and GABA located in the lateral hypothalamic area control REM sleep expression. Finally, a population of REM-off GABAergic neurons located in the ventrolateral periaqueductal gray has been shown to gate REM sleep by inhibiting glutamatergic neurons located in the sublaterodorsal tegmental nucleus. In summary, recent data clearly indicate that multiple populations of GABAergic neurons located throughout the brain from the cortex to the medulla oblongata control NREM and REM sleep. Copyright © 2016 Elsevier Ltd. All rights reserved.

  2. Stochastic biomathematical models with applications to neuronal modeling

    CERN Document Server

    Batzel, Jerry; Ditlevsen, Susanne

    2013-01-01

    Stochastic biomathematical models are becoming increasingly important as new light is shed on the role of noise in living systems. In certain biological systems, stochastic effects may even enhance a signal, thus providing a biological motivation for the noise observed in living systems. Recent advances in stochastic analysis and increasing computing power facilitate the analysis of more biophysically realistic models, and this book provides researchers in computational neuroscience and stochastic systems with an overview of recent developments. Key concepts are developed in chapters written by experts in their respective fields. Topics include: one-dimensional homogeneous diffusions and their boundary behavior, large deviation theory and its application in stochastic neurobiological models, a review of mathematical methods for stochastic neuronal integrate-and-fire models, stochastic partial differential equation models in neurobiology, and stochastic modeling of spreading cortical depression.

  3. Growth of cortical neuronal network in vitro: Modeling and analysis

    International Nuclear Information System (INIS)

    Lai, P.-Y.; Jia, L. C.; Chan, C. K.

    2006-01-01

    We present a detailed analysis and theoretical growth models to account for recent experimental data on the growth of cortical neuronal networks in vitro [Phys. Rev. Lett. 93, 088101 (2004)]. The experimentally observed synchronized firing frequency of a well-connected neuronal network is shown to be proportional to the mean network connectivity. The growth of the network is consistent with the model of an early enhanced growth of connection, but followed by a retarded growth once the synchronized cluster is formed. Microscopic models with dominant excluded volume interactions are consistent with the observed exponential decay of the mean connection probability as a function of the mean network connectivity. The biological implications of the growth model are also discussed

  4. Building functional networks of spiking model neurons.

    Science.gov (United States)

    Abbott, L F; DePasquale, Brian; Memmesheimer, Raoul-Martin

    2016-03-01

    Most of the networks used by computer scientists and many of those studied by modelers in neuroscience represent unit activities as continuous variables. Neurons, however, communicate primarily through discontinuous spiking. We review methods for transferring our ability to construct interesting networks that perform relevant tasks from the artificial continuous domain to more realistic spiking network models. These methods raise a number of issues that warrant further theoretical and experimental study.

  5. Molecular and cellular organization of taste neurons in adult Drosophila pharynx

    Science.gov (United States)

    Chen, Yu-Chieh (David); Dahanukar, Anupama

    2017-01-01

    SUMMARY The Drosophila pharyngeal taste organs are poorly characterized despite their location at important sites for monitoring food quality. Functional analysis of pharyngeal neurons has been hindered by the paucity of molecular tools to manipulate them, as well as their relative inaccessibility for neurophysiological investigations. Here, we generate receptor-to-neuron maps of all three pharyngeal taste organs by performing a comprehensive chemoreceptor-GAL4/LexA expression analysis. The organization of pharyngeal neurons reveals similarities and distinctions in receptor repertoires and neuronal groupings compared to external taste neurons. We validate the mapping results by pinpointing a single pharyngeal neuron required for feeding avoidance of L-canavanine. Inducible activation of pharyngeal taste neurons reveals functional differences between external and internal taste neurons and functional subdivision within pharyngeal sweet neurons. Our results provide road maps of pharyngeal taste organs in an insect model system for probing the role of these understudied neurons in controlling feeding behaviors. PMID:29212040

  6. Modeling chemotherapeutic neurotoxicity with human induced pluripotent stem cell-derived neuronal cells.

    Directory of Open Access Journals (Sweden)

    Heather E Wheeler

    Full Text Available There are no effective agents to prevent or treat chemotherapy-induced peripheral neuropathy (CIPN, the most common non-hematologic toxicity of chemotherapy. Therefore, we sought to evaluate the utility of human neuron-like cells derived from induced pluripotent stem cells (iPSCs as a means to study CIPN. We used high content imaging measurements of neurite outgrowth phenotypes to compare the changes that occur to iPSC-derived neuronal cells among drugs and among individuals in response to several classes of chemotherapeutics. Upon treatment of these neuronal cells with the neurotoxic drug paclitaxel, vincristine or cisplatin, we identified significant differences in five morphological phenotypes among drugs, including total outgrowth, mean/median/maximum process length, and mean outgrowth intensity (P < 0.05. The differences in damage among drugs reflect differences in their mechanisms of action and clinical CIPN manifestations. We show the potential of the model for gene perturbation studies by demonstrating decreased expression of TUBB2A results in significantly increased sensitivity of neurons to paclitaxel (0.23 ± 0.06 decrease in total neurite outgrowth, P = 0.011. The variance in several neurite outgrowth and apoptotic phenotypes upon treatment with one of the neurotoxic drugs is significantly greater between than within neurons derived from four different individuals (P < 0.05, demonstrating the potential of iPSC-derived neurons as a genetically diverse model for CIPN. The human neuron model will allow both for mechanistic studies of specific genes and genetic variants discovered in clinical studies and for screening of new drugs to prevent or treat CIPN.

  7. Generalized synchronization induced by noise and parameter mismatching in Hindmarsh-Rose neurons

    International Nuclear Information System (INIS)

    Wu Ying; Xu Jianxue; He Daihai; Earn, David J.D.

    2005-01-01

    Synchronization of two simple neuron models has been investigated in many studies. Thresholds for complete synchronization (CS) and phase synchronization (PS) have been obtained for coupling by diffusion or noise. In addition, it has been shown that it is possible for directional diffusion to induce generalized synchronization (GS) in a pair of neuron models even if the neurons are not identical (and differ in a single parameter). We study a system of two uncoupled, nonidentical Hindmarsh-Rose (HR) neurons and show that GS can be achieved by a combination of noise and changing the value of a second parameter in one of the neurons (the second parameter mismatch cancels the first). The significance of this approach will be the greatest in situations where the parameter that is originally mismatched cannot be controlled, but a suitable controllable parameter can be identified

  8. Studying the Behaviour of Model of Mirror Neuron System in Case of Autism

    OpenAIRE

    Anirban, Shikha; Hanif Ali, Mohammad

    2012-01-01

    Several experiment done by the researchers conducted that autism is caused by the dysfunctional mirror neuron system and the dysfunctions of mirror neuron system is proportional to the symptom severity of autism. In the present work those experiments were studied as well as studying a model of mirror neuron system called MNS2 developed by a research group. This research examined the behavior of the model in case of autism and compared the result with those studies conducting dysfunctions of m...

  9. Increasing inhibitory input increases neuronal firing rate: why and when? Diffusion process cases

    Energy Technology Data Exchange (ETDEWEB)

    Feng Jianfeng [COGS, Sussex University (United Kingdom)]. E-mail: jf218@cam.ac.uk; Wei Gang [Department of Mathematics, Hong Kong Baptist University, Hong Kong (China)]. E-mail gwei@math.hkbu.edu.hk

    2001-09-21

    Increasing inhibitory input to single neuronal models, such as the FitzHugh-Nagumo model and the Hodgkin-Huxley model, can sometimes increase their firing rates, a phenomenon which we term inhibition-boosted firing (IBF). Here we consider neuronal models with diffusion approximation inputs, i.e. they share the identical first- and second-order statistics of the corresponding Poisson process inputs. Using the integrate-and-fire model and the IF-FHN model, we explore theoretically how and when IBF can happen. For both models, it is shown that there is a critical input frequency at which the efferent firing rate is identical when the neuron receives purely excitatory inputs or exactly balanced inhibitory and excitatory inputs. When the input frequency is lower than the critical frequency, IBF occurs. (author)

  10. Single-Cell Detection of Secreted Aβ and sAPPα from Human IPSC-Derived Neurons and Astrocytes.

    Science.gov (United States)

    Liao, Mei-Chen; Muratore, Christina R; Gierahn, Todd M; Sullivan, Sarah E; Srikanth, Priya; De Jager, Philip L; Love, J Christopher; Young-Pearse, Tracy L

    2016-02-03

    Secreted factors play a central role in normal and pathological processes in every tissue in the body. The brain is composed of a highly complex milieu of different cell types and few methods exist that can identify which individual cells in a complex mixture are secreting specific analytes. By identifying which cells are responsible, we can better understand neural physiology and pathophysiology, more readily identify the underlying pathways responsible for analyte production, and ultimately use this information to guide the development of novel therapeutic strategies that target the cell types of relevance. We present here a method for detecting analytes secreted from single human induced pluripotent stem cell (iPSC)-derived neural cells and have applied the method to measure amyloid β (Aβ) and soluble amyloid precursor protein-alpha (sAPPα), analytes central to Alzheimer's disease pathogenesis. Through these studies, we have uncovered the dynamic range of secretion profiles of these analytes from single iPSC-derived neuronal and glial cells and have molecularly characterized subpopulations of these cells through immunostaining and gene expression analyses. In examining Aβ and sAPPα secretion from single cells, we were able to identify previously unappreciated complexities in the biology of APP cleavage that could not otherwise have been found by studying averaged responses over pools of cells. This technique can be readily adapted to the detection of other analytes secreted by neural cells, which would have the potential to open new perspectives into human CNS development and dysfunction. We have established a technology that, for the first time, detects secreted analytes from single human neurons and astrocytes. We examine secretion of the Alzheimer's disease-relevant factors amyloid β (Aβ) and soluble amyloid precursor protein-alpha (sAPPα) and present novel findings that could not have been observed without a single-cell analytical platform. First, we

  11. Bidirectional coupling between astrocytes and neurons mediates learning and dynamic coordination in the brain: a multiple modeling approach.

    Directory of Open Access Journals (Sweden)

    John J Wade

    Full Text Available In recent years research suggests that astrocyte networks, in addition to nutrient and waste processing functions, regulate both structural and synaptic plasticity. To understand the biological mechanisms that underpin such plasticity requires the development of cell level models that capture the mutual interaction between astrocytes and neurons. This paper presents a detailed model of bidirectional signaling between astrocytes and neurons (the astrocyte-neuron model or AN model which yields new insights into the computational role of astrocyte-neuronal coupling. From a set of modeling studies we demonstrate two significant findings. Firstly, that spatial signaling via astrocytes can relay a "learning signal" to remote synaptic sites. Results show that slow inward currents cause synchronized postsynaptic activity in remote neurons and subsequently allow Spike-Timing-Dependent Plasticity based learning to occur at the associated synapses. Secondly, that bidirectional communication between neurons and astrocytes underpins dynamic coordination between neuron clusters. Although our composite AN model is presently applied to simplified neural structures and limited to coordination between localized neurons, the principle (which embodies structural, functional and dynamic complexity, and the modeling strategy may be extended to coordination among remote neuron clusters.

  12. Power laws from linear neuronal cable theory

    DEFF Research Database (Denmark)

    Pettersen, Klas H; Lindén, Henrik Anders; Tetzlaff, Tom

    2014-01-01

    suggested to be at the root of this phenomenon, we here demonstrate a possible origin of such power laws in the biophysical properties of single neurons described by the standard cable equation. Taking advantage of the analytical tractability of the so called ball and stick neuron model, we derive general...... are homogeneously distributed across the neural membranes and themselves exhibit pink ([Formula: see text]) noise distributions. While the PSD noise spectra at low frequencies may be dominated by synaptic noise, our findings suggest that the high-frequency power laws may originate in noise from intrinsic ion...

  13. Restoring neuronal progranulin reverses deficits in a mouse model of frontotemporal dementia.

    Science.gov (United States)

    Arrant, Andrew E; Filiano, Anthony J; Unger, Daniel E; Young, Allen H; Roberson, Erik D

    2017-05-01

    Loss-of-function mutations in progranulin (GRN), a secreted glycoprotein expressed by neurons and microglia, are a common autosomal dominant cause of frontotemporal dementia, a neurodegenerative disease commonly characterized by disrupted social and emotional behaviour. GRN mutations are thought to cause frontotemporal dementia through progranulin haploinsufficiency, therefore, boosting progranulin expression from the intact allele is a rational treatment strategy. However, this approach has not been tested in an animal model of frontotemporal dementia and it is unclear if boosting progranulin could correct pre-existing deficits. Here, we show that adeno-associated virus-driven expression of progranulin in the medial prefrontal cortex reverses social dominance deficits in Grn+/- mice, an animal model of frontotemporal dementia due to GRN mutations. Adeno-associated virus-progranulin also corrected lysosomal abnormalities in Grn+/- mice. The adeno-associated virus-progranulin vector only transduced neurons, suggesting that restoring neuronal progranulin is sufficient to correct deficits in Grn+/- mice. To further test the role of neuronal progranulin in the development of frontotemporal dementia-related deficits, we generated two neuronal progranulin-deficient mouse lines using CaMKII-Cre and Nestin-Cre. Measuring progranulin levels in these lines indicated that most brain progranulin is derived from neurons. Both neuronal progranulin-deficient lines developed social dominance deficits similar to those in global Grn+/- mice, showing that neuronal progranulin deficiency is sufficient to disrupt social behaviour. These data support the concept of progranulin-boosting therapies for frontotemporal dementia and highlight an important role for neuron-derived progranulin in maintaining normal social function. © The Author (2017). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  14. Establishment of mouse neuron and microglial cell co-cultured models and its action mechanism.

    Science.gov (United States)

    Zhang, Bo; Yang, Yunfeng; Tang, Jun; Tao, Yihao; Jiang, Bing; Chen, Zhi; Feng, Hua; Yang, Liming; Zhu, Gang

    2017-06-27

    The objective of this study is to establish a co-culture model of mouse neurons and microglial cells, and to analyze the mechanism of action of oxygen glucose deprivation (OGD) and transient oxygen glucose deprivation (tOGD) preconditioning cell models. Mouse primary neurons and BV2 microglial cells were successfully cultured, and the OGD and tOGD models were also established. In the co-culture of mouse primary neurons and microglial cells, the cell number of tOGD mouse neurons and microglial cells was larger than the OGD cell number, observed by a microscope. CCK-8 assay result showed that at 1h after treatment, the OD value in the control group is lower compared to all the other three groups (P control group compared to other three groups (P neurons cells were cultured. In the meantime mouse BV2 microglia cells were cultured. Two types of cells were co-cultured, and OGD and tOGD cell models were established. There were four groups in the experiment: control group (OGD), treatment group (tOGD+OGD), placebo group (tOGD+OGD+saline) and minocycline intervention group (tOGD+OGD+minocycline). CCK-8 kit was used to detect cell viability and flow cytometry was used to detect apoptosis. In this study, mouse primary neurons and microglial cells were co-cultured. The OGD and tOGD models were established successfully. tOGD was able to effectively protect neurons and microglial cells from damage, and inhibit the apoptosis caused by oxygen glucose deprivation.

  15. Single bumps in a 2-population homogenized neuronal network model

    Science.gov (United States)

    Kolodina, Karina; Oleynik, Anna; Wyller, John

    2018-05-01

    We investigate existence and stability of single bumps in a homogenized 2-population neural field model, when the firing rate functions are given by the Heaviside function. The model is derived by means of the two-scale convergence technique of Nguetseng in the case of periodic microvariation in the connectivity functions. The connectivity functions are periodically modulated in both the synaptic footprint and in the spatial scale. The bump solutions are constructed by using a pinning function technique for the case where the solutions are independent of the local variable. In the weakly modulated case the generic picture consists of two bumps (one narrow and one broad bump) for each admissible set of threshold values for firing. In addition, a new threshold value regime for existence of bumps is detected. Beyond the weakly modulated regime the number of bumps depends sensitively on the degree of heterogeneity. For the latter case we present a configuration consisting of three coexisting bumps. The linear stability of the bumps is studied by means of the spectral properties of a Fredholm integral operator, block diagonalization of this operator and the Fourier decomposition method. In the weakly modulated regime, one of the bumps is unstable for all relative inhibition times, while the other one is stable for small and moderate values of this parameter. The latter bump becomes unstable as the relative inhibition time exceeds a certain threshold. In the case of the three coexisting bumps detected in the regime of finite degree of heterogeneity, we have at least one stable bump (and maximum two stable bumps) for small and moderate values of the relative inhibition time.

  16. Role of neuronal activity in regulating the structure and function of auditory neurons

    International Nuclear Information System (INIS)

    Born, D.E.

    1986-01-01

    The role of afferent activity in maintaining neuronal structure and function was investigated in second order auditory neurons in nucleus magnocellularis (NM) of the chicken. The cochlea provides the major excitatory input to NM neurons via the eighth nerve. Removal of the cochlea causes dramatic changes in NM neurons. To determine if the elimination of neuronal activity is responsible for the changes in NM seen after cochlea removal, tetrodotoxin was used block action potentials in the cochlear ganglion cells. Tetrodotoxin injections into the perilymph reliably blocked neuronal activity in the cochlear nerve and NM. Far field recordings of sound-evoked potentials revealed that responses returned within 6 hours. Changes in amino acid incorporation in NM neurons were measured by giving intracardiac injections of 3 H-leucine and preparing tissue for autoradiographic demonstration of incorporated amino acid. Grain counts over individual neurons revealed that a single injection of tetrodotoxin produced a 40% decrease in grain density in ipsilateral NM neurons. It is concluded that neuronal activity plays an important contribution to the maintenance of the normal properties of NM neurons

  17. Modeling of Auditory Neuron Response Thresholds with Cochlear Implants

    Directory of Open Access Journals (Sweden)

    Frederic Venail

    2015-01-01

    Full Text Available The quality of the prosthetic-neural interface is a critical point for cochlear implant efficiency. It depends not only on technical and anatomical factors such as electrode position into the cochlea (depth and scalar placement, electrode impedance, and distance between the electrode and the stimulated auditory neurons, but also on the number of functional auditory neurons. The efficiency of electrical stimulation can be assessed by the measurement of e-CAP in cochlear implant users. In the present study, we modeled the activation of auditory neurons in cochlear implant recipients (nucleus device. The electrical response, measured using auto-NRT (neural responses telemetry algorithm, has been analyzed using multivariate regression with cubic splines in order to take into account the variations of insertion depth of electrodes amongst subjects as well as the other technical and anatomical factors listed above. NRT thresholds depend on the electrode squared impedance (β = −0.11 ± 0.02, P<0.01, the scalar placement of the electrodes (β = −8.50 ± 1.97, P<0.01, and the depth of insertion calculated as the characteristic frequency of auditory neurons (CNF. Distribution of NRT residues according to CNF could provide a proxy of auditory neurons functioning in implanted cochleas.

  18. Chimera-like states in a neuronal network model of the cat brain

    Science.gov (United States)

    Santos, M. S.; Szezech, J. D.; Borges, F. S.; Iarosz, K. C.; Caldas, I. L.; Batista, A. M.; Viana, R. L.; Kurths, J.

    2017-08-01

    Neuronal systems have been modeled by complex networks in different description levels. Recently, it has been verified that networks can simultaneously exhibit one coherent and other incoherent domain, known as chimera states. In this work, we study the existence of chimera states in a network considering the connectivity matrix based on the cat cerebral cortex. The cerebral cortex of the cat can be separated in 65 cortical areas organised into the four cognitive regions: visual, auditory, somatosensory-motor and frontolimbic. We consider a network where the local dynamics is given by the Hindmarsh-Rose model. The Hindmarsh-Rose equations are a well known model of neuronal activity that has been considered to simulate membrane potential in neuron. Here, we analyse under which conditions chimera states are present, as well as the affects induced by intensity of coupling on them. We observe the existence of chimera states in that incoherent structure can be composed of desynchronised spikes or desynchronised bursts. Moreover, we find that chimera states with desynchronised bursts are more robust to neuronal noise than with desynchronised spikes.

  19. Synaptic potentiation onto habenula neurons in learned helplessness model of depression

    Science.gov (United States)

    Li, Bo; Piriz, Joaquin; Mirrione, Martine; Chung, ChiHye; Proulx, Christophe D.; Schulz, Daniela; Henn, Fritz; Malinow, Roberto

    2010-01-01

    The cellular basis of depressive disorders is poorly understood1. Recent studies in monkeys indicate that neurons in the lateral habenula (LHb), a nucleus that mediates communication between forebrain and midbrain structures, can increase their activity when an animal fails to receive an expected positive reward or receives a stimulus that predicts aversive conditions (i.e. disappointment or anticipation of a negative outcome)2, 3, 4. LHb neurons project to and modulate dopamine-rich regions such as the ventral-tegmental area (VTA)2, 5 that control reward-seeking behavior6 and participate in depressive disorders7. Here we show in two learned helplessness models of depression that excitatory synapses onto LHb neurons projecting to the VTA are potentiated. Synaptic potentiation correlates with an animal’s helplessness behavior and is due to an enhanced presynaptic release probability. Depleting transmitter release by repeated electrical stimulation of LHb afferents, using a protocol that can be effective on depressed patients8, 9, dramatically suppresses synaptic drive onto VTA-projecting LHb neurons in brain slices and can significantly reduce learned helplessness behavior in rats. Our results indicate that increased presynaptic action onto LHb neurons contributes to the rodent learned helplessness model of depression. PMID:21350486

  20. Recapitulation of spinal motor neuron-specific disease phenotypes in a human cell model of spinal muscular atrophy

    Institute of Scientific and Technical Information of China (English)

    Zhi-Bo Wang; Xiaoqing Zhang; Xue-Jun Li

    2013-01-01

    Establishing human cell models of spinal muscular atrophy (SMA) to mimic motor neuron-specific phenotypes holds the key to understanding the pathogenesis of this devastating disease.Here,we developed a closely representative cell model of SMA by knocking down the disease-determining gene,survival motor neuron (SMN),in human embryonic stem cells (hESCs).Our study with this cell model demonstrated that knocking down of SMN does not interfere with neural induction or the initial specification of spinal motor neurons.Notably,the axonal outgrowth of spinal motor neurons was significantly impaired and these disease-mimicking neurons subsequently degenerated.Furthermore,these disease phenotypes were caused by SMN-full length (SMN-FL) but not SMN-A7 (lacking exon 7)knockdown,and were specific to spinal motor neurons.Restoring the expression of SMN-FL completely ameliorated all of the disease phenotypes,including specific axonal defects and motor neuron loss.Finally,knockdown of SMNFL led to excessive mitochondrial oxidative stress in human motor neuron progenitors.The involvement of oxidative stress in the degeneration of spinal motor neurons in the SMA cell model was further confirmed by the administration of N-acetylcysteine,a potent antioxidant,which prevented disease-related apoptosis and subsequent motor neuron death.Thus,we report here the successful establishment of an hESC-based SMA model,which exhibits disease gene isoform specificity,cell type specificity,and phenotype reversibility.Our model provides a unique paradigm for studying how motor neurons specifically degenerate and highlights the potential importance of antioxidants for the treatment of SMA.

  1. Serotonin Neuron Abnormalities in the BTBR Mouse Model of Autism

    Science.gov (United States)

    Guo, Yue-Ping; Commons, Kathryn G.

    2017-01-01

    The inbred mouse strain BTBR T+ Itpr3tf/J (BTBR) i studied as a model of idiopathic autism because they are less social and more resistant to change than other strains. Forebrain serotonin receptors and the response to serotonin drugs are altered in BTBR mice, yet it remains unknown if serotonin neurons themselves are abnormal. In this study, we found that serotonin tissue content and the density of serotonin axons is reduced in the hippocampus of BTBR mice in comparison to C57BL/6J (C57) mice. This was accompanied by possible compensatory changes in serotonin neurons that were most pronounced in regions known to provide innervation to the hippocampus: the caudal dorsal raphe (B6) and the median raphe. These changes included increased numbers of serotonin neurons and hyperactivation of Fos expression. Metrics of serotonin neurons in the rostral 2/3 of the dorsal raphe and serotonin content of the prefrontal cortex were less impacted. Thus, serotonin neurons exhibit region-dependent abnormalities in the BTBR mouse that may contribute to their altered behavioral profile. PMID:27478061

  2. Conceptual Network Model From Sensory Neurons to Astrocytes of the Human Nervous System.

    Science.gov (United States)

    Yang, Yiqun; Yeo, Chai Kiat

    2015-07-01

    From a single-cell animal like paramecium to vertebrates like ape, the nervous system plays an important role in responding to the variations of the environment. Compared to animals, the nervous system in the human body possesses more intricate organization and utility. The nervous system anatomy has been understood progressively, yet the explanation at the cell level regarding complete information transmission is still lacking. Along the signal pathway toward the brain, an external stimulus first activates action potentials in the sensing neuron and these electric pulses transmit along the spinal nerve or cranial nerve to the neurons in the brain. Second, calcium elevation is triggered in the branch of astrocyte at the tripartite synapse. Third, the local calcium wave expands to the entire territory of the astrocyte. Finally, the calcium wave propagates to the neighboring astrocyte via gap junction channel. In our study, we integrate the existing mathematical model and biological experiments in each step of the signal transduction to establish a conceptual network model for the human nervous system. The network is composed of four layers and the communication protocols of each layer could be adapted to entities with different characterizations. We verify our simulation results against the available biological experiments and mathematical models and provide a test case of the integrated network. As the production of conscious episode in the human nervous system is still under intense research, our model serves as a useful tool to facilitate, complement and verify current and future study in human cognition.

  3. Self-Organized Criticality in a Simple Neuron Model Based on Scale-Free Networks

    International Nuclear Information System (INIS)

    Lin Min; Wang Gang; Chen Tianlun

    2006-01-01

    A simple model for a set of interacting idealized neurons in scale-free networks is introduced. The basic elements of the model are endowed with the main features of a neuron function. We find that our model displays power-law behavior of avalanche sizes and generates long-range temporal correlation. More importantly, we find different dynamical behavior for nodes with different connectivity in the scale-free networks.

  4. Dynamical patterns of calcium signaling in a functional model of neuron-astrocyte networks

    DEFF Research Database (Denmark)

    Postnov, D.E.; Koreshkov, R.N.; Brazhe, N.A.

    2009-01-01

    We propose a functional mathematical model for neuron-astrocyte networks. The model incorporates elements of the tripartite synapse and the spatial branching structure of coupled astrocytes. We consider glutamate-induced calcium signaling as a specific mode of excitability and transmission...... in astrocytic-neuronal networks. We reproduce local and global dynamical patterns observed experimentally....

  5. A Communication Theoretical Modeling of Axonal Propagation in Hippocampal Pyramidal Neurons.

    Science.gov (United States)

    Ramezani, Hamideh; Akan, Ozgur B

    2017-06-01

    Understanding the fundamentals of communication among neurons, known as neuro-spike communication, leads to reach bio-inspired nanoscale communication paradigms. In this paper, we focus on a part of neuro-spike communication, known as axonal transmission, and propose a realistic model for it. The shape of the spike during axonal transmission varies according to previously applied stimulations to the neuron, and these variations affect the amount of information communicated between neurons. Hence, to reach an accurate model for neuro-spike communication, the memory of axon and its effect on the axonal transmission should be considered, which are not studied in the existing literature. In this paper, we extract the important factors on the memory of axon and define memory states based on these factors. We also describe the transition among these states and the properties of axonal transmission in each of them. Finally, we demonstrate that the proposed model can follow changes in the axonal functionality properly by simulating the proposed model and reporting the root mean square error between simulation results and experimental data.

  6. Predictive features of persistent activity emergence in regular spiking and intrinsic bursting model neurons.

    Directory of Open Access Journals (Sweden)

    Kyriaki Sidiropoulou

    Full Text Available Proper functioning of working memory involves the expression of stimulus-selective persistent activity in pyramidal neurons of the prefrontal cortex (PFC, which refers to neural activity that persists for seconds beyond the end of the stimulus. The mechanisms which PFC pyramidal neurons use to discriminate between preferred vs. neutral inputs at the cellular level are largely unknown. Moreover, the presence of pyramidal cell subtypes with different firing patterns, such as regular spiking and intrinsic bursting, raises the question as to what their distinct role might be in persistent firing in the PFC. Here, we use a compartmental modeling approach to search for discriminatory features in the properties of incoming stimuli to a PFC pyramidal neuron and/or its response that signal which of these stimuli will result in persistent activity emergence. Furthermore, we use our modeling approach to study cell-type specific differences in persistent activity properties, via implementing a regular spiking (RS and an intrinsic bursting (IB model neuron. We identify synaptic location within the basal dendrites as a feature of stimulus selectivity. Specifically, persistent activity-inducing stimuli consist of activated synapses that are located more distally from the soma compared to non-inducing stimuli, in both model cells. In addition, the action potential (AP latency and the first few inter-spike-intervals of the neuronal response can be used to reliably detect inducing vs. non-inducing inputs, suggesting a potential mechanism by which downstream neurons can rapidly decode the upcoming emergence of persistent activity. While the two model neurons did not differ in the coding features of persistent activity emergence, the properties of persistent activity, such as the firing pattern and the duration of temporally-restricted persistent activity were distinct. Collectively, our results pinpoint to specific features of the neuronal response to a given

  7. Connecting mirror neurons and forward models.

    Science.gov (United States)

    Miall, R C

    2003-12-02

    Two recent developments in motor neuroscience are promising the extension of theoretical concepts from motor control towards cognitive processes, including human social interactions and understanding the intentions of others. The first of these is the discovery of what are now called mirror neurons, which code for both observed and executed actions. The second is the concept of internal models, and in particular recent proposals that forward and inverse models operate in paired modules. These two ideas will be briefly introduced, and a recent suggestion linking between the two processes of mirroring and modelling will be described which may underlie our abilities for imitating actions, for cooperation between two actors, and possibly for communication via gesture and language.

  8. Finite element modeling of the neuron-electrode interface: stimulus transfer and geometry

    NARCIS (Netherlands)

    Buitenweg, Jan R.; Rutten, Wim; Marani, Enrico

    1999-01-01

    The relation between stimulus transfer and the geometry of the neuron-electrode interface can not be determined properly using electrical equivalent circuits, since current that flows from the sealing gap through the neuronal membrane is difficult to model in these circuits. Therefore, finite

  9. Single Canonical Model of Reflexive Memory and Spatial Attention

    Science.gov (United States)

    Patel, Saumil S.; Red, Stuart; Lin, Eric; Sereno, Anne B.

    2015-01-01

    Many neurons in the dorsal and ventral visual stream have the property that after a brief visual stimulus presentation in their receptive field, the spiking activity in these neurons persists above their baseline levels for several seconds. This maintained activity is not always correlated with the monkey’s task and its origin is unknown. We have previously proposed a simple neural network model, based on shape selective neurons in monkey lateral intraparietal cortex, which predicts the valence and time course of reflexive (bottom-up) spatial attention. In the same simple model, we demonstrate here that passive maintained activity or short-term memory of specific visual events can result without need for an external or top-down modulatory signal. Mutual inhibition and neuronal adaptation play distinct roles in reflexive attention and memory. This modest 4-cell model provides the first simple and unified physiologically plausible mechanism of reflexive spatial attention and passive short-term memory processes. PMID:26493949

  10. Single Canonical Model of Reflexive Memory and Spatial Attention.

    Science.gov (United States)

    Patel, Saumil S; Red, Stuart; Lin, Eric; Sereno, Anne B

    2015-10-23

    Many neurons in the dorsal and ventral visual stream have the property that after a brief visual stimulus presentation in their receptive field, the spiking activity in these neurons persists above their baseline levels for several seconds. This maintained activity is not always correlated with the monkey's task and its origin is unknown. We have previously proposed a simple neural network model, based on shape selective neurons in monkey lateral intraparietal cortex, which predicts the valence and time course of reflexive (bottom-up) spatial attention. In the same simple model, we demonstrate here that passive maintained activity or short-term memory of specific visual events can result without need for an external or top-down modulatory signal. Mutual inhibition and neuronal adaptation play distinct roles in reflexive attention and memory. This modest 4-cell model provides the first simple and unified physiologically plausible mechanism of reflexive spatial attention and passive short-term memory processes.

  11. Temperature manipulation of neuronal dynamics in a forebrain motor control nucleus.

    Directory of Open Access Journals (Sweden)

    Matías A Goldin

    2017-08-01

    Full Text Available Different neuronal types within brain motor areas contribute to the generation of complex motor behaviors. A widely studied songbird forebrain nucleus (HVC has been recognized as fundamental in shaping the precise timing characteristics of birdsong. This is based, among other evidence, on the stretching and the "breaking" of song structure when HVC is cooled. However, little is known about the temperature effects that take place in its neurons. To address this, we investigated the dynamics of HVC both experimentally and computationally. We developed a technique where simultaneous electrophysiological recordings were performed during temperature manipulation of HVC. We recorded spontaneous activity and found three effects: widening of the spike shape, decrease of the firing rate and change in the interspike interval distribution. All these effects could be explained with a detailed conductance based model of all the neurons present in HVC. Temperature dependence of the ionic channel time constants explained the first effect, while the second was based in the changes of the maximal conductance using single synaptic excitatory inputs. The last phenomenon, only emerged after introducing a more realistic synaptic input to the inhibitory interneurons. Two timescales were present in the interspike distributions. The behavior of one timescale was reproduced with different input balances received form the excitatory neurons, whereas the other, which disappears with cooling, could not be found assuming poissonian synaptic inputs. Furthermore, the computational model shows that the bursting of the excitatory neurons arises naturally at normal brain temperature and that they have an intrinsic delay at low temperatures. The same effect occurs at single synapses, which may explain song stretching. These findings shed light on the temperature dependence of neuronal dynamics and present a comprehensive framework to study neuronal connectivity. This study, which

  12. En masse in vitro functional profiling of the axonal mechanosensitivity of sensory neurons.

    Science.gov (United States)

    Usoskin, Dmitry; Zilberter, Misha; Linnarsson, Sten; Hjerling-Leffler, Jens; Uhlén, Per; Harkany, Tibor; Ernfors, Patrik

    2010-09-14

    Perception of the environment relies on somatosensory neurons. Mechanosensory, proprioceptor and many nociceptor subtypes of these neurons have specific mechanosensitivity profiles to adequately differentiate stimulus patterns. Nevertheless, the cellular basis of differential mechanosensation remains largely elusive. Successful transduction of sensory information relies on the recruitment of sensory neurons and mechanosensation occurring at their peripheral axonal endings in vivo. Conspicuously, existing in vitro models aimed to decipher molecular mechanisms of mechanosensation test single sensory neuron somata at any one time. Here, we introduce a compartmental in vitro chamber design to deliver precisely controlled mechanical stimulation of sensory axons with synchronous real-time imaging of Ca(2+) transients in neuronal somata that reliably reflect action potential firing patterns. We report of three previously not characterized types of mechanosensitive neuron subpopulations with distinct intrinsic axonal properties tuned specifically to static indentation or vibration stimuli, showing that different classes of sensory neurons are tuned to specific types of mechanical stimuli. Primary receptor currents of vibration neurons display rapidly adapting conductance reliably detected for every single stimulus during vibration and are consistently converted into action potentials. This result allows for the characterization of two critical steps of mechanosensation in vivo: primary signal detection and signal conversion into specific action potential firing patterns in axons.

  13. Developmental emergence of different forms of neuromodulation in Aplysia sensory neurons.

    Science.gov (United States)

    Marcus, E A; Carew, T J

    1998-04-14

    The capacity for neuromodulation and biophysical plasticity is a defining feature of most mature neuronal cell types. In several cases, modulation at the level of the individual neuron has been causally linked to changes in the functional output of a neuronal circuit and subsequent adaptive changes in the organism's behavioral responses. Understanding how such capacity for neuromodulation develops therefore may provide insights into the mechanisms both of neuronal development and learning and memory. We have examined the development of multiple forms of neuromodulation triggered by a common neurotransmitter, serotonin, in the pleural sensory neurons of Aplysia californica. We have found that multiple signaling cascades within a single neuron develop sequentially, with some being expressed only very late in development. In addition, our data suggest a model in which, within a single neuromodulatory pathway, the elements of the signaling cascade are developmentally expressed in a "retrograde" manner with the ionic channel that is modulated appearing early in development, functional elements in the second messenger cascade appearing later, and finally, coupling of the second messenger cascade to the serotonin receptor appearing quite late. These studies provide the characterization of the development of neuromodulation at the level of an identified cell type and offer insights into the potential roles of neuromodulatory processes in development and adult plasticity.

  14. Proton- and ammonium-sensing by histaminergic neurons controlling wakefulness.

    Science.gov (United States)

    Yanovsky, Yevgenij; Zigman, Jeffrey M; Kernder, Anna; Bein, Alisa; Sakata, Ichiro; Osborne-Lawrence, Sherri; Haas, Helmut L; Sergeeva, Olga A

    2012-01-01

    The histaminergic neurons in the tuberomamillary nucleus (TMN) of the posterior hypothalamus are involved in the control of arousal. These neurons are sensitive to hypercapnia as has been shown in experiments examining c-Fos expression, a marker for increased neuronal activity. We investigated the mechanisms through which TMN neurons respond to changes in extracellular levels of acid/CO(2). Recordings in rat brain slices revealed that acidification within the physiological range (pH from 7.4 to 7.0), as well as ammonium chloride (5 mM), excite histaminergic neurons. This excitation is significantly reduced by antagonists of type I metabotropic glutamate receptors and abolished by benzamil, an antagonist of acid-sensing ion channels (ASICs) and Na(+)/Ca(2+) exchanger, or by ouabain which blocks Na(+)/K(+) ATPase. We detected variable combinations of 4 known types of ASICs in single TMN neurons, and observed activation of ASICs in single dissociated TMN neurons only at pH lower than 7.0. Thus, glutamate, which is known to be released by glial cells and orexinergic neurons, amplifies the acid/CO(2)-induced activation of TMN neurons. This amplification demands the coordinated function of metabotropic glutamate receptors, Na(+)/Ca(2+) exchanger and Na(+)/K(+) ATPase. We also developed a novel HDC-Cre transgenic reporter mouse line in which histaminergic TMN neurons can be visualized. In contrast to the rat, the mouse histaminergic neurons lacked the pH 7.0-induced excitation and displayed only a minimal response to the mGluR I agonist DHPG (0.5 μM). On the other hand, ammonium-induced excitation was similar in mouse and rat. These results are relevant for the understanding of the neuronal mechanisms controlling acid/CO(2)-induced arousal in hepatic encephalopathy and obstructive sleep apnoea. Moreover, the new HDC-Cre mouse model will be a useful tool for studying the physiological and pathophysiological roles of the histaminergic system.

  15. The effect of correlated neuronal firing and neuronal heterogeneity on population coding accuracy in guinea pig inferior colliculus.

    Directory of Open Access Journals (Sweden)

    Oran Zohar

    Full Text Available It has been suggested that the considerable noise in single-cell responses to a stimulus can be overcome by pooling information from a large population. Theoretical studies indicated that correlations in trial-to-trial fluctuations in the responses of different neurons may limit the improvement due to pooling. Subsequent theoretical studies have suggested that inherent neuronal diversity, i.e., the heterogeneity of tuning curves and other response properties of neurons preferentially tuned to the same stimulus, can provide a means to overcome this limit. Here we study the effect of spike-count correlations and the inherent neuronal heterogeneity on the ability to extract information from large neural populations. We use electrophysiological data from the guinea pig Inferior-Colliculus to capture inherent neuronal heterogeneity and single cell statistics, and introduce response correlations artificially. To this end, we generate pseudo-population responses, based on single-cell recording of neurons responding to auditory stimuli with varying binaural correlations. Typically, when pseudo-populations are generated from single cell data, the responses within the population are statistically independent. As a result, the information content of the population will increase indefinitely with its size. In contrast, here we apply a simple algorithm that enables us to generate pseudo-population responses with variable spike-count correlations. This enables us to study the effect of neuronal correlations on the accuracy of conventional rate codes. We show that in a homogenous population, in the presence of even low-level correlations, information content is bounded. In contrast, utilizing a simple linear readout, that takes into account the natural heterogeneity, even of neurons preferentially tuned to the same stimulus, within the neural population, one can overcome the correlated noise and obtain a readout whose accuracy grows linearly with the size of

  16. One nuclear calcium transient induced by a single burst of action potentials represents the minimum signal strength in activity-dependent transcription in hippocampal neurons.

    Science.gov (United States)

    Yu, Yan; Oberlaender, Kristin; Bengtson, C Peter; Bading, Hilmar

    2017-07-01

    Neurons undergo dramatic changes in their gene expression profiles in response to synaptic stimulation. The coupling of neuronal excitation to gene transcription is well studied and is mediated by signaling pathways activated by cytoplasmic and nuclear calcium transients. Despite this, the minimum synaptic activity required to induce gene expression remains unknown. To address this, we used cultured hippocampal neurons and cellular compartment analysis of temporal activity by fluorescence in situ hybridization (catFISH) that allows detection of nascent transcripts in the cell nucleus. We found that a single burst of action potentials, consisting of 24.4±5.1 action potentials during a 6.7±1.9s depolarization of 19.5±2.0mV causing a 9.3±0.9s somatic calcium transient, is sufficient to activate transcription of the immediate early gene arc (also known as Arg3.1). The total arc mRNA yield produced after a single burst-induced nuclear calcium transient was very small and, compared to unstimulated control neurons, did not lead to a significant increase in arc mRNA levels measured using quantitative reverse transcriptase PCR (qRT-PCR) of cell lysates. Significantly increased arc mRNA levels became detectable in hippocampal neurons that had undergone 5-8 consecutive burst-induced nuclear calcium transients at 0.05-0.15Hz. These results indicate that a single burst-induced nuclear calcium transient can activate gene expression and that transcription is rapidly shut off after synaptic stimulation has ceased. Copyright © 2017 Elsevier Ltd. All rights reserved.

  17. BlastNeuron for Automated Comparison, Retrieval and Clustering of 3D Neuron Morphologies.

    Science.gov (United States)

    Wan, Yinan; Long, Fuhui; Qu, Lei; Xiao, Hang; Hawrylycz, Michael; Myers, Eugene W; Peng, Hanchuan

    2015-10-01

    Characterizing the identity and types of neurons in the brain, as well as their associated function, requires a means of quantifying and comparing 3D neuron morphology. Presently, neuron comparison methods are based on statistics from neuronal morphology such as size and number of branches, which are not fully suitable for detecting local similarities and differences in the detailed structure. We developed BlastNeuron to compare neurons in terms of their global appearance, detailed arborization patterns, and topological similarity. BlastNeuron first compares and clusters 3D neuron reconstructions based on global morphology features and moment invariants, independent of their orientations, sizes, level of reconstruction and other variations. Subsequently, BlastNeuron performs local alignment between any pair of retrieved neurons via a tree-topology driven dynamic programming method. A 3D correspondence map can thus be generated at the resolution of single reconstruction nodes. We applied BlastNeuron to three datasets: (1) 10,000+ neuron reconstructions from a public morphology database, (2) 681 newly and manually reconstructed neurons, and (3) neurons reconstructions produced using several independent reconstruction methods. Our approach was able to accurately and efficiently retrieve morphologically and functionally similar neuron structures from large morphology database, identify the local common structures, and find clusters of neurons that share similarities in both morphology and molecular profiles.

  18. Synaptic potentiation onto habenula neurons in the learned helplessness model of depression.

    Science.gov (United States)

    Li, Bo; Piriz, Joaquin; Mirrione, Martine; Chung, ChiHye; Proulx, Christophe D; Schulz, Daniela; Henn, Fritz; Malinow, Roberto

    2011-02-24

    The cellular basis of depressive disorders is poorly understood. Recent studies in monkeys indicate that neurons in the lateral habenula (LHb), a nucleus that mediates communication between forebrain and midbrain structures, can increase their activity when an animal fails to receive an expected positive reward or receives a stimulus that predicts aversive conditions (that is, disappointment or anticipation of a negative outcome). LHb neurons project to, and modulate, dopamine-rich regions, such as the ventral tegmental area (VTA), that control reward-seeking behaviour and participate in depressive disorders. Here we show that in two learned helplessness models of depression, excitatory synapses onto LHb neurons projecting to the VTA are potentiated. Synaptic potentiation correlates with an animal's helplessness behaviour and is due to an enhanced presynaptic release probability. Depleting transmitter release by repeated electrical stimulation of LHb afferents, using a protocol that can be effective for patients who are depressed, markedly suppresses synaptic drive onto VTA-projecting LHb neurons in brain slices and can significantly reduce learned helplessness behaviour in rats. Our results indicate that increased presynaptic action onto LHb neurons contributes to the rodent learned helplessness model of depression.

  19. Synaptic potentiation onto habenula neurons in the learned helplessness model of depression

    International Nuclear Information System (INIS)

    Li, B.; Schulz, D.; Piriz, J.; Mirrione, M.; Chung, C.H.; Proulx, C.D.; Schulz, D.; Henn, F.; Malinow, R.

    2011-01-01

    The cellular basis of depressive disorders is poorly understood. Recent studies in monkeys indicate that neurons in the lateral habenula (LHb), a nucleus that mediates communication between forebrain and midbrain structures, can increase their activity when an animal fails to receive an expected positive reward or receives a stimulus that predicts aversive conditions (that is, disappointment or anticipation of a negative outcome). LHb neurons project to, and modulate, dopamine-rich regions, such as the ventral tegmental area (VTA), that control reward-seeking behaviour and participate in depressive disorders. Here we show that in two learned helplessness models of depression, excitatory synapses onto LHb neurons projecting to the VTA are potentiated. Synaptic potentiation correlates with an animal's helplessness behaviour and is due to an enhanced presynaptic release probability. Depleting transmitter release by repeated electrical stimulation of LHb afferents, using a protocol that can be effective for patients who are depressed, markedly suppresses synaptic drive onto VTA-projecting LHb neurons in brain slices and can significantly reduce learned helplessness behaviour in rats. Our results indicate that increased presynaptic action onto LHb neurons contributes to the rodent learned helplessness model of depression.

  20. Induction of specific neuron types by overexpression of single transcription factors.

    Science.gov (United States)

    Teratani-Ota, Yusuke; Yamamizu, Kohei; Piao, Yulan; Sharova, Lioudmila; Amano, Misa; Yu, Hong; Schlessinger, David; Ko, Minoru S H; Sharov, Alexei A

    2016-10-01

    Specific neuronal types derived from embryonic stem cells (ESCs) can facilitate mechanistic studies and potentially aid in regenerative medicine. Existing induction methods, however, mostly rely on the effects of the combined action of multiple added growth factors, which generally tend to result in mixed populations of neurons. Here, we report that overexpression of specific transcription factors (TFs) in ESCs can rather guide the differentiation of ESCs towards specific neuron lineages. Analysis of data on gene expression changes 2 d after induction of each of 185 TFs implicated candidate TFs for further ESC differentiation studies. Induction of 23 TFs (out of 49 TFs tested) for 6 d facilitated neural differentiation of ESCs as inferred from increased proportion of cells with neural progenitor marker PSA-NCAM. We identified early activation of the Notch signaling pathway as a common feature of most potent inducers of neural differentiation. The majority of neuron-like cells generated by induction of Ascl1, Smad7, Nr2f1, Dlx2, Dlx4, Nr2f2, Barhl2, and Lhx1 were GABA-positive and expressed other markers of GABAergic neurons. In the same way, we identified Lmx1a and Nr4a2 as inducers for neurons bearing dopaminergic markers and Isl1, Fezf2, and St18 for cholinergic motor neurons. A time-course experiment with induction of Ascl1 showed early upregulation of most neural-specific messenger RNA (mRNA) and microRNAs (miRNAs). Sets of Ascl1-induced mRNAs and miRNAs were enriched in Ascl1 targets. In further studies, enrichment of cells obtained with the induction of Ascl1, Smad7, and Nr2f1 using microbeads resulted in essentially pure population of neuron-like cells with expression profiles similar to neural tissues and expressed markers of GABAergic neurons. In summary, this study indicates that induction of transcription factors is a promising approach to generate cultures that show the transcription profiles characteristic of specific neural cell types.

  1. Leaders of neuronal cultures in a quorum percolation model

    Directory of Open Access Journals (Sweden)

    Jean-Pierre Eckmann

    2010-09-01

    Full Text Available We present a theoretical framework using quorum-percolation for describing the initiation of activity in a neural culture. The cultures are modeled as random graphs, whose nodes are neurons with $kin$ inputs and $kout$ outputs, and whose input degrees $kin=k$ obey given distribution functions $p_k$. We examine the firing activity of the population of neurons according to their input degree ($k$ classes and calculate for each class its firing probability $Phi_k(t$ as a function of $t$. The probability of a node to fire is found to be determined by its in-degree $k$, and the first-to-fire neurons are those that have a high $k$. A small minority of high-$k$ classes may be called ``Leaders,'' as they form an inter-connected subnetwork that consistently fires much before the rest of the culture. Once initiated, the activity spreads from the Leaders to the less connected majority of the culture. We then use the distribution of in-degree of the Leaders to study the growth rate of the number of neurons active in a burst, which was experimentally measured to be initially exponential. We find that this kind of growth rate is best described by a population that has an in-degree distribution that is a Gaussian centered around $k=75$ with width $sigma=31$ for the majority of the neurons, but also has a power law tail with exponent $-2$ for ten percent of the population. Neurons in the tail may have as many as $k=4,700$ inputs. We explore and discuss the correspondence between the degree distribution and a dynamic neuronal threshold, showing that from the functional point of view, structure and elementary dynamics are interchangeable. We discuss possible geometric origins of this distribution, and comment on the importance of size, or of having a large number of neurons, in the culture.

  2. Neuronal Entropy-Rate Feature of Entopeduncular Nucleus in Rat Model of Parkinson's Disease.

    Science.gov (United States)

    Darbin, Olivier; Jin, Xingxing; Von Wrangel, Christof; Schwabe, Kerstin; Nambu, Atsushi; Naritoku, Dean K; Krauss, Joachim K; Alam, Mesbah

    2016-03-01

    The function of the nigro-striatal pathway on neuronal entropy in the basal ganglia (BG) output nucleus, i.e. the entopeduncular nucleus (EPN) was investigated in the unilaterally 6-hyroxydopamine (6-OHDA)-lesioned rat model of Parkinson's disease (PD). In both control subjects and subjects with 6-OHDA lesion of dopamine (DA) the nigro-striatal pathway, a histological hallmark for parkinsonism, neuronal entropy in EPN was maximal in neurons with firing rates ranging between 15 and 25 Hz. In 6-OHDA lesioned rats, neuronal entropy in the EPN was specifically higher in neurons with firing rates above 25 Hz. Our data establishes that the nigro-striatal pathway controls neuronal entropy in motor circuitry and that the parkinsonian condition is associated with abnormal relationship between firing rate and neuronal entropy in BG output nuclei. The neuronal firing rates and entropy relationship provide putative relevant electrophysiological information to investigate the sensory-motor processing in normal condition and conditions such as movement disorders.

  3. Neurofitter: a parameter tuning package for a wide range of electrophysiological neuron models

    Directory of Open Access Journals (Sweden)

    Werner Van Geit

    2007-11-01

    Full Text Available The increase in available computational power and the higher quality of experimental recordings have turned the tuning of neuron model parameters into a problem that can be solved by automatic global optimization algorithms. Neurofitter is a software tool that interfaces existing neural simulation software and sophisticated optimization algorithms with a new way to compute the error measure. This error measure represents how well a given parameter set is able to reproduce the experimental data. It is based on the phase-plane trajectory density method, which is insensitive to small phase differences between model and data. Neurofitter enables the effortless combination of many different time-dependent data traces into the error measure, allowing the neuroscientist to focus on what are the seminal properties of the model. We show results obtained by applying Neurofitter to a simple single compartmental model and a complex multi-compartmental Purkinje cell (PC model. These examples show that the method is able to solve a variety of tuning problems and demonstrate details of its practical application.

  4. The Effects of Guanfacine and Phenylephrine on a Spiking Neuron Model of Working Memory.

    Science.gov (United States)

    Duggins, Peter; Stewart, Terrence C; Choo, Xuan; Eliasmith, Chris

    2017-01-01

    We use a spiking neural network model of working memory (WM) capable of performing the spatial delayed response task (DRT) to investigate two drugs that affect WM: guanfacine (GFC) and phenylephrine (PHE). In this model, the loss of information over time results from changes in the spiking neural activity through recurrent connections. We reproduce the standard forgetting curve and then show that this curve changes in the presence of GFC and PHE, whose application is simulated by manipulating functional, neural, and biophysical properties of the model. In particular, applying GFC causes increased activity in neurons that are sensitive to the information currently being remembered, while applying PHE leads to decreased activity in these same neurons. Interestingly, these differential effects emerge from network-level interactions because GFC and PHE affect all neurons equally. We compare our model to both electrophysiological data from neurons in monkey dorsolateral prefrontal cortex and to behavioral evidence from monkeys performing the DRT. Copyright © 2016 Cognitive Science Society, Inc.

  5. Neuronal gain modulability is determined by dendritic morphology: A computational optogenetic study.

    Science.gov (United States)

    Jarvis, Sarah; Nikolic, Konstantin; Schultz, Simon R

    2018-03-01

    The mechanisms by which the gain of the neuronal input-output function may be modulated have been the subject of much investigation. However, little is known of the role of dendrites in neuronal gain control. New optogenetic experimental paradigms based on spatial profiles or patterns of light stimulation offer the prospect of elucidating many aspects of single cell function, including the role of dendrites in gain control. We thus developed a model to investigate how competing excitatory and inhibitory input within the dendritic arbor alters neuronal gain, incorporating kinetic models of opsins into our modeling to ensure it is experimentally testable. To investigate how different topologies of the neuronal dendritic tree affect the neuron's input-output characteristics we generate branching geometries which replicate morphological features of most common neurons, but keep the number of branches and overall area of dendrites approximately constant. We found a relationship between a neuron's gain modulability and its dendritic morphology, with neurons with bipolar dendrites with a moderate degree of branching being most receptive to control of the gain of their input-output relationship. The theory was then tested and confirmed on two examples of realistic neurons: 1) layer V pyramidal cells-confirming their role in neural circuits as a regulator of the gain in the circuit in addition to acting as the primary excitatory neurons, and 2) stellate cells. In addition to providing testable predictions and a novel application of dual-opsins, our model suggests that innervation of all dendritic subdomains is required for full gain modulation, revealing the importance of dendritic targeting in the generation of neuronal gain control and the functions that it subserves. Finally, our study also demonstrates that neurophysiological investigations which use direct current injection into the soma and bypass the dendrites may miss some important neuronal functions, such as gain

  6. Single-molecule folding mechanism of an EF-hand neuronal calcium sensor

    DEFF Research Database (Denmark)

    Heiðarsson, Pétur Orri; Otazo, Mariela R.; Bellucci, Luca

    2013-01-01

    EF-hand calcium sensors respond structurally to changes in intracellular Ca2+ concentration, triggering diverse cellular responses and resulting in broad interactomes. Despite impressive advances in decoding their structure-function relationships, the folding mechanism of neuronal calcium sensors...... of the N domain, showing striking interdomain dependence. Molecular dynamics results reveal the atomistic details of the unfolding process and rationalize the different domain stabilities during mechanical unfolding. Through constant-force experiments and hidden Markov model analysis, the free energy...

  7. Radiation Damage to Nervous System: Designing Optimal Models for Realistic Neuron Morphology in Hippocampus

    Science.gov (United States)

    Batmunkh, Munkhbaatar; Bugay, Alexander; Bayarchimeg, Lkhagvaa; Lkhagva, Oidov

    2018-02-01

    The present study is focused on the development of optimal models of neuron morphology for Monte Carlo microdosimetry simulations of initial radiation-induced events of heavy charged particles in the specific types of cells of the hippocampus, which is the most radiation-sensitive structure of the central nervous system. The neuron geometry and particles track structures were simulated by the Geant4/Geant4-DNA Monte Carlo toolkits. The calculations were made for beams of protons and heavy ions with different energies and doses corresponding to real fluxes of galactic cosmic rays. A simple compartmental model and a complex model with realistic morphology extracted from experimental data were constructed and compared. We estimated the distribution of the energy deposition events and the production of reactive chemical species within the developed models of CA3/CA1 pyramidal neurons and DG granule cells of the rat hippocampus under exposure to different particles with the same dose. Similar distributions of the energy deposition events and concentration of some oxidative radical species were obtained in both the simplified and realistic neuron models.

  8. Theoretical analysis of transcranial magneto-acoustical stimulation with Hodgkin–Huxley neuron model

    Directory of Open Access Journals (Sweden)

    Yi eYuan

    2016-04-01

    Full Text Available Transcranial magneto-acoustical stimulation (TMAS is a novel stimulation technology in which an ultrasonic wave within a magnetostatic field generates an electric current in an area of interest in the brain to modulate neuronal activities. As a key part of the neural network, neurons transmit information in the nervous system. However, the effect of TMAS on the neuronal firing rhythm remains unknown. To address this problem, we investigated the stimulatory mechanism of TMAS on neurons with a Hodgkin-Huxley neuron model. The simulation results indicate that the magnetostatic field intensity and ultrasonic power can affect the amplitude and interspike interval of neuronal action potential under continuous wave ultrasound. The simulation results also show that the ultrasonic power, duty cycle and repetition frequency can alter the firing rhythm of neural action potential under pulsed ultrasound. This study can help to reveal and explain the biological mechanism of TMAS and to provide a theoretical basis for TMAS in the treatment or rehabilitation of neuropsychiatric disorders.

  9. Hippocampal adaptive response following extensive neuronal loss in an inducible transgenic mouse model.

    Directory of Open Access Journals (Sweden)

    Kristoffer Myczek

    Full Text Available Neuronal loss is a common component of a variety of neurodegenerative disorders (including Alzheimer's, Parkinson's, and Huntington's disease and brain traumas (stroke, epilepsy, and traumatic brain injury. One brain region that commonly exhibits neuronal loss in several neurodegenerative disorders is the hippocampus, an area of the brain critical for the formation and retrieval of memories. Long-lasting and sometimes unrecoverable deficits caused by neuronal loss present a unique challenge for clinicians and for researchers who attempt to model these traumas in animals. Can these deficits be recovered, and if so, is the brain capable of regeneration following neuronal loss? To address this significant question, we utilized the innovative CaM/Tet-DT(A mouse model that selectively induces neuronal ablation. We found that we are able to inflict a consistent and significant lesion to the hippocampus, resulting in hippocampally-dependent behavioral deficits and a long-lasting upregulation in neurogenesis, suggesting that this process might be a critical part of hippocampal recovery. In addition, we provide novel evidence of angiogenic and vasculature changes following hippocampal neuronal loss in CaM/Tet-DTA mice. We posit that angiogenesis may be an important factor that promotes neurogenic upregulation following hippocampal neuronal loss, and both factors, angiogenesis and neurogenesis, can contribute to the adaptive response of the brain for behavioral recovery.

  10. The Role of Inhibition in a Computational Model of an Auditory Cortical Neuron during the Encoding of Temporal Information

    Science.gov (United States)

    Bendor, Daniel

    2015-01-01

    In auditory cortex, temporal information within a sound is represented by two complementary neural codes: a temporal representation based on stimulus-locked firing and a rate representation, where discharge rate co-varies with the timing between acoustic events but lacks a stimulus-synchronized response. Using a computational neuronal model, we find that stimulus-locked responses are generated when sound-evoked excitation is combined with strong, delayed inhibition. In contrast to this, a non-synchronized rate representation is generated when the net excitation evoked by the sound is weak, which occurs when excitation is coincident and balanced with inhibition. Using single-unit recordings from awake marmosets (Callithrix jacchus), we validate several model predictions, including differences in the temporal fidelity, discharge rates and temporal dynamics of stimulus-evoked responses between neurons with rate and temporal representations. Together these data suggest that feedforward inhibition provides a parsimonious explanation of the neural coding dichotomy observed in auditory cortex. PMID:25879843

  11. Olig2 and Hes regulatory dynamics during motor neuron differentiation revealed by single cell transcriptomics.

    Directory of Open Access Journals (Sweden)

    Andreas Sagner

    2018-02-01

    Full Text Available During tissue development, multipotent progenitors differentiate into specific cell types in characteristic spatial and temporal patterns. We addressed the mechanism linking progenitor identity and differentiation rate in the neural tube, where motor neuron (MN progenitors differentiate more rapidly than other progenitors. Using single cell transcriptomics, we defined the transcriptional changes associated with the transition of neural progenitors into MNs. Reconstruction of gene expression dynamics from these data indicate a pivotal role for the MN determinant Olig2 just prior to MN differentiation. Olig2 represses expression of the Notch signaling pathway effectors Hes1 and Hes5. Olig2 repression of Hes5 appears to be direct, via a conserved regulatory element within the Hes5 locus that restricts expression from MN progenitors. These findings reveal a tight coupling between the regulatory networks that control patterning and neuronal differentiation and demonstrate how Olig2 acts as the developmental pacemaker coordinating the spatial and temporal pattern of MN generation.

  12. A hierarchical model for structure learning based on the physiological characteristics of neurons

    Institute of Scientific and Technical Information of China (English)

    WEI Hui

    2007-01-01

    Almost all applications of Artificial Neural Networks (ANNs) depend mainly on their memory ability.The characteristics of typical ANN models are fixed connections,with evolved weights,globalized representations,and globalized optimizations,all based on a mathematical approach.This makes those models to be deficient in robustness,efficiency of learning,capacity,anti-jamming between training sets,and correlativity of samples,etc.In this paper,we attempt to address these problems by adopting the characteristics of biological neurons in morphology and signal processing.A hierarchical neural network was designed and realized to implement structure learning and representations based on connected structures.The basic characteristics of this model are localized and random connections,field limitations of neuron fan-in and fan-out,dynamic behavior of neurons,and samples represented through different sub-circuits of neurons specialized into different response patterns.At the end of this paper,some important aspects of error correction,capacity,learning efficiency,and soundness of structural representation are analyzed theoretically.This paper has demonstrated the feasibility and advantages of structure learning and representation.This model can serve as a fundamental element of cognitive systems such as perception and associative memory.Key-words structure learning,representation,associative memory,computational neuroscience

  13. Statistics of a neuron model driven by asymmetric colored noise.

    Science.gov (United States)

    Müller-Hansen, Finn; Droste, Felix; Lindner, Benjamin

    2015-02-01

    Irregular firing of neurons can be modeled as a stochastic process. Here we study the perfect integrate-and-fire neuron driven by dichotomous noise, a Markovian process that jumps between two states (i.e., possesses a non-Gaussian statistics) and exhibits nonvanishing temporal correlations (i.e., represents a colored noise). Specifically, we consider asymmetric dichotomous noise with two different transition rates. Using a first-passage-time formulation, we derive exact expressions for the probability density and the serial correlation coefficient of the interspike interval (time interval between two subsequent neural action potentials) and the power spectrum of the spike train. Furthermore, we extend the model by including additional Gaussian white noise, and we give approximations for the interspike interval (ISI) statistics in this case. Numerical simulations are used to validate the exact analytical results for pure dichotomous noise, and to test the approximations of the ISI statistics when Gaussian white noise is included. The results may help to understand how correlations and asymmetry of noise and signals in nerve cells shape neuronal firing statistics.

  14. Parvalbumin+ Neurons and Npas1+ Neurons Are Distinct Neuron Classes in the Mouse External Globus Pallidus.

    Science.gov (United States)

    Hernández, Vivian M; Hegeman, Daniel J; Cui, Qiaoling; Kelver, Daniel A; Fiske, Michael P; Glajch, Kelly E; Pitt, Jason E; Huang, Tina Y; Justice, Nicholas J; Chan, C Savio

    2015-08-26

    Compelling evidence suggests that pathological activity of the external globus pallidus (GPe), a nucleus in the basal ganglia, contributes to the motor symptoms of a variety of movement disorders such as Parkinson's disease. Recent studies have challenged the idea that the GPe comprises a single, homogenous population of neurons that serves as a simple relay in the indirect pathway. However, we still lack a full understanding of the diversity of the neurons that make up the GPe. Specifically, a more precise classification scheme is needed to better describe the fundamental biology and function of different GPe neuron classes. To this end, we generated a novel multicistronic BAC (bacterial artificial chromosome) transgenic mouse line under the regulatory elements of the Npas1 gene. Using a combinatorial transgenic and immunohistochemical approach, we discovered that parvalbumin-expressing neurons and Npas1-expressing neurons in the GPe represent two nonoverlapping cell classes, amounting to 55% and 27% of the total GPe neuron population, respectively. These two genetically identified cell classes projected primarily to the subthalamic nucleus and to the striatum, respectively. Additionally, parvalbumin-expressing neurons and Npas1-expressing neurons were distinct in their autonomous and driven firing characteristics, their expression of intrinsic ion conductances, and their responsiveness to chronic 6-hydroxydopamine lesion. In summary, our data argue that parvalbumin-expressing neurons and Npas1-expressing neurons are two distinct functional classes of GPe neurons. This work revises our understanding of the GPe, and provides the foundation for future studies of its function and dysfunction. Until recently, the heterogeneity of the constituent neurons within the external globus pallidus (GPe) was not fully appreciated. We addressed this knowledge gap by discovering two principal GPe neuron classes, which were identified by their nonoverlapping expression of the

  15. Parvalbumin+ Neurons and Npas1+ Neurons Are Distinct Neuron Classes in the Mouse External Globus Pallidus

    Science.gov (United States)

    Hernández, Vivian M.; Hegeman, Daniel J.; Cui, Qiaoling; Kelver, Daniel A.; Fiske, Michael P.; Glajch, Kelly E.; Pitt, Jason E.; Huang, Tina Y.; Justice, Nicholas J.

    2015-01-01

    Compelling evidence suggests that pathological activity of the external globus pallidus (GPe), a nucleus in the basal ganglia, contributes to the motor symptoms of a variety of movement disorders such as Parkinson's disease. Recent studies have challenged the idea that the GPe comprises a single, homogenous population of neurons that serves as a simple relay in the indirect pathway. However, we still lack a full understanding of the diversity of the neurons that make up the GPe. Specifically, a more precise classification scheme is needed to better describe the fundamental biology and function of different GPe neuron classes. To this end, we generated a novel multicistronic BAC (bacterial artificial chromosome) transgenic mouse line under the regulatory elements of the Npas1 gene. Using a combinatorial transgenic and immunohistochemical approach, we discovered that parvalbumin-expressing neurons and Npas1-expressing neurons in the GPe represent two nonoverlapping cell classes, amounting to 55% and 27% of the total GPe neuron population, respectively. These two genetically identified cell classes projected primarily to the subthalamic nucleus and to the striatum, respectively. Additionally, parvalbumin-expressing neurons and Npas1-expressing neurons were distinct in their autonomous and driven firing characteristics, their expression of intrinsic ion conductances, and their responsiveness to chronic 6-hydroxydopamine lesion. In summary, our data argue that parvalbumin-expressing neurons and Npas1-expressing neurons are two distinct functional classes of GPe neurons. This work revises our understanding of the GPe, and provides the foundation for future studies of its function and dysfunction. SIGNIFICANCE STATEMENT Until recently, the heterogeneity of the constituent neurons within the external globus pallidus (GPe) was not fully appreciated. We addressed this knowledge gap by discovering two principal GPe neuron classes, which were identified by their nonoverlapping

  16. Divisive normalization and neuronal oscillations in a single hierarchical framework of selective visual attention

    NARCIS (Netherlands)

    Montijn, J.S.; Klink, P.C.; van Wezel, R.J.A.

    2012-01-01

    Divisive normalization models of covert attention commonly use spike rate modulations as indicators of the effect of top-down attention. In addition, an increasing number of studies have shown that top-down attention increases the synchronization of neuronal oscillations as well, particularly in

  17. Spatio-Temporal Modeling of Neuron Fields

    DEFF Research Database (Denmark)

    Lund, Adam

    The starting point and focal point for this thesis was stochastic dynamical modelling of neuronal imaging data with the declared objective of drawing inference, within this model framework, in a large-scale (high-dimensional) data setting. Implicitly this objective entails carrying out three...... be achieved if the scale of the data is taken into consideration throughout i) - iii). The strategy in this project was, relying on a space and time continuous stochastic modelling approach, to obtain a stochastic functional differential equation on a Hilbert space. By decomposing the drift operator...... of this SFDE such that each component is essentially represented by a smooth function of time and space and expanding these component functions in a tensor product basis we implicitly reduce the number of model parameters. In addition, the component-wise tensor representation induce a corresponding component...

  18. Network dynamics in nociceptive pathways assessed by the neuronal avalanche model

    Directory of Open Access Journals (Sweden)

    Wu José

    2012-04-01

    Full Text Available Abstract Background Traditional electroencephalography provides a critical assessment of pain responses. The perception of pain, however, may involve a series of signal transmission pathways in higher cortical function. Recent studies have shown that a mathematical method, the neuronal avalanche model, may be applied to evaluate higher-order network dynamics. The neuronal avalanche is a cascade of neuronal activity, the size distribution of which can be approximated by a power law relationship manifested by the slope of a straight line (i.e., the α value. We investigated whether the neuronal avalanche could be a useful index for nociceptive assessment. Findings Neuronal activity was recorded with a 4 × 8 multichannel electrode array in the primary somatosensory cortex (S1 and anterior cingulate cortex (ACC. Under light anesthesia, peripheral pinch stimulation increased the slope of the α value in both the ACC and S1, whereas brush stimulation increased the α value only in the S1. The increase in α values was blocked in both regions under deep anesthesia. The increase in α values in the ACC induced by peripheral pinch stimulation was blocked by medial thalamic lesion, but the increase in α values in the S1 induced by brush and pinch stimulation was not affected. Conclusions The neuronal avalanche model shows a critical state in the cortical network for noxious-related signal processing. The α value may provide an index of brain network activity that distinguishes the responses to somatic stimuli from the control state. These network dynamics may be valuable for the evaluation of acute nociceptive processes and may be applied to chronic pathological pain conditions.

  19. Stochastic Wilson–Cowan models of neuronal network dynamics with memory and delay

    International Nuclear Information System (INIS)

    Goychuk, Igor; Goychuk, Andriy

    2015-01-01

    We consider a simple Markovian class of the stochastic Wilson–Cowan type models of neuronal network dynamics, which incorporates stochastic delay caused by the existence of a refractory period of neurons. From the point of view of the dynamics of the individual elements, we are dealing with a network of non-Markovian stochastic two-state oscillators with memory, which are coupled globally in a mean-field fashion. This interrelation of a higher-dimensional Markovian and lower-dimensional non-Markovian dynamics is discussed in its relevance to the general problem of the network dynamics of complex elements possessing memory. The simplest model of this class is provided by a three-state Markovian neuron with one refractory state, which causes firing delay with an exponentially decaying memory within the two-state reduced model. This basic model is used to study critical avalanche dynamics (the noise sustained criticality) in a balanced feedforward network consisting of the excitatory and inhibitory neurons. Such avalanches emerge due to the network size dependent noise (mesoscopic noise). Numerical simulations reveal an intermediate power law in the distribution of avalanche sizes with the critical exponent around −1.16. We show that this power law is robust upon a variation of the refractory time over several orders of magnitude. However, the avalanche time distribution is biexponential. It does not reflect any genuine power law dependence. (paper)

  20. Encoding of Spatial Attention by Primate Prefrontal Cortex Neuronal Ensembles

    Science.gov (United States)

    Treue, Stefan

    2018-01-01

    Abstract Single neurons in the primate lateral prefrontal cortex (LPFC) encode information about the allocation of visual attention and the features of visual stimuli. However, how this compares to the performance of neuronal ensembles at encoding the same information is poorly understood. Here, we recorded the responses of neuronal ensembles in the LPFC of two macaque monkeys while they performed a task that required attending to one of two moving random dot patterns positioned in different hemifields and ignoring the other pattern. We found single units selective for the location of the attended stimulus as well as for its motion direction. To determine the coding of both variables in the population of recorded units, we used a linear classifier and progressively built neuronal ensembles by iteratively adding units according to their individual performance (best single units), or by iteratively adding units based on their contribution to the ensemble performance (best ensemble). For both methods, ensembles of relatively small sizes (n decoding performance relative to individual single units. However, the decoder reached similar performance using fewer neurons with the best ensemble building method compared with the best single units method. Our results indicate that neuronal ensembles within the LPFC encode more information about the attended spatial and nonspatial features of visual stimuli than individual neurons. They further suggest that efficient coding of attention can be achieved by relatively small neuronal ensembles characterized by a certain relationship between signal and noise correlation structures. PMID:29568798

  1. Stability switches, Hopf bifurcation and chaos of a neuron model with delay-dependent parameters

    International Nuclear Information System (INIS)

    Xu, X.; Hu, H.Y.; Wang, H.L.

    2006-01-01

    It is very common that neural network systems usually involve time delays since the transmission of information between neurons is not instantaneous. Because memory intensity of the biological neuron usually depends on time history, some of the parameters may be delay dependent. Yet, little attention has been paid to the dynamics of such systems. In this Letter, a detailed analysis on the stability switches, Hopf bifurcation and chaos of a neuron model with delay-dependent parameters is given. Moreover, the direction and the stability of the bifurcating periodic solutions are obtained by the normal form theory and the center manifold theorem. It shows that the dynamics of the neuron model with delay-dependent parameters is quite different from that of systems with delay-independent parameters only

  2. Physiological Characterization of Vestibular Efferent Brainstem Neurons Using a Transgenic Mouse Model

    Science.gov (United States)

    Leijon, Sara; Magnusson, Anna K.

    2014-01-01

    The functional role of efferent innervation of the vestibular end-organs in the inner ear remains elusive. This study provides the first physiological characterization of the cholinergic vestibular efferent (VE) neurons in the brainstem by utilizing a transgenic mouse model, expressing eGFP under a choline-acetyltransferase (ChAT)-locus spanning promoter in combination with targeted patch clamp recordings. The intrinsic electrical properties of the eGFP-positive VE neurons were compared to the properties of the lateral olivocochlear (LOC) brainstem neurons, which gives rise to efferent innervation of the cochlea. Both VE and the LOC neurons were marked by their negative resting membrane potential neurons differed significantly in the depolarizing range. When injected with positive currents, VE neurons fired action potentials faithfully to the onset of depolarization followed by sparse firing with long inter-spike intervals. This response gave rise to a low response gain. The LOC neurons, conversely, responded with a characteristic delayed tonic firing upon depolarizing stimuli, giving rise to higher response gain than the VE neurons. Depolarization triggered large TEA insensitive outward currents with fast inactivation kinetics, indicating A-type potassium currents, in both the inner ear-projecting neuronal types. Immunohistochemistry confirmed expression of Kv4.3 and 4.2 ion channel subunits in both the VE and LOC neurons. The difference in spiking responses to depolarization is related to a two-fold impact of these transient outward currents on somatic integration in the LOC neurons compared to in VE neurons. It is speculated that the physiological properties of the VE neurons might be compatible with a wide-spread control over motion and gravity sensation in the inner ear, providing likewise feed-back amplification of abrupt and strong phasic signals from the semi-circular canals and of tonic signals from the gravito-sensitive macular organs. PMID:24867596

  3. Stereopsis and 3D surface perception by spiking neurons in laminar cortical circuits: a method for converting neural rate models into spiking models.

    Science.gov (United States)

    Cao, Yongqiang; Grossberg, Stephen

    2012-02-01

    A laminar cortical model of stereopsis and 3D surface perception is developed and simulated. The model shows how spiking neurons that interact in hierarchically organized laminar circuits of the visual cortex can generate analog properties of 3D visual percepts. The model describes how monocular and binocular oriented filtering interact with later stages of 3D boundary formation and surface filling-in in the LGN and cortical areas V1, V2, and V4. It proposes how interactions between layers 4, 3B, and 2/3 in V1 and V2 contribute to stereopsis, and how binocular and monocular information combine to form 3D boundary and surface representations. The model suggests how surface-to-boundary feedback from V2 thin stripes to pale stripes helps to explain how computationally complementary boundary and surface formation properties lead to a single consistent percept, eliminate redundant 3D boundaries, and trigger figure-ground perception. The model also shows how false binocular boundary matches may be eliminated by Gestalt grouping properties. In particular, the disparity filter, which helps to solve the correspondence problem by eliminating false matches, is realized using inhibitory interneurons as part of the perceptual grouping process by horizontal connections in layer 2/3 of cortical area V2. The 3D sLAMINART model simulates 3D surface percepts that are consciously seen in 18 psychophysical experiments. These percepts include contrast variations of dichoptic masking and the correspondence problem, the effect of interocular contrast differences on stereoacuity, Panum's limiting case, the Venetian blind illusion, stereopsis with polarity-reversed stereograms, da Vinci stereopsis, and perceptual closure. The model hereby illustrates a general method of unlumping rate-based models that use the membrane equations of neurophysiology into models that use spiking neurons, and which may be embodied in VLSI chips that use spiking neurons to minimize heat production. Copyright

  4. Competition model for aperiodic stochastic resonance in a Fitzhugh-Nagumo model of cardiac sensory neurons.

    Science.gov (United States)

    Kember, G C; Fenton, G A; Armour, J A; Kalyaniwalla, N

    2001-04-01

    Regional cardiac control depends upon feedback of the status of the heart from afferent neurons responding to chemical and mechanical stimuli as transduced by an array of sensory neurites. Emerging experimental evidence shows that neural control in the heart may be partially exerted using subthreshold inputs that are amplified by noisy mechanical fluctuations. This amplification is known as aperiodic stochastic resonance (ASR). Neural control in the noisy, subthreshold regime is difficult to see since there is a near absence of any correlation between input and the output, the latter being the average firing (spiking) rate of the neuron. This lack of correlation is unresolved by traditional energy models of ASR since these models are unsuitable for identifying "cause and effect" between such inputs and outputs. In this paper, the "competition between averages" model is used to determine what portion of a noisy, subthreshold input is responsible, on average, for the output of sensory neurons as represented by the Fitzhugh-Nagumo equations. A physiologically relevant conclusion of this analysis is that a nearly constant amount of input is responsible for a spike, on average, and this amount is approximately independent of the firing rate. Hence, correlation measures are generally reduced as the firing rate is lowered even though neural control under this model is actually unaffected.

  5. Iron-induced neuronal damage in a rat model of post-traumatic stress disorder.

    Science.gov (United States)

    Zhao, Ming; Yu, Zhibo; Zhang, Yang; Huang, Xueling; Hou, Jingming; Zhao, YanGang; Luo, Wei; Chen, Lin; Ou, Lan; Li, Haitao; Zhang, Jiqiang

    2016-08-25

    Previous studies have shown that iron redistribution and deposition in the brain occurs in some neurodegenerative diseases, and oxidative damage due to abnormal iron level is a primary cause of neuronal death. In the present study, we used the single prolonged stress (SPS) model to mimic post-traumatic stress disorder (PTSD), and examined whether iron was involved in the progression of PTSD. The anxiety-like behaviors of the SPS group were assessed by the elevated plus maze (EPM) and open field tests, and iron levels were measured by inductively coupled plasma optical emission spectrometer (ICP-OES). Expression of glucocorticoid receptors and transferrin receptor 1 (TfR1) and ferritin (Fn) was detected by Western blot and immunohistochemistry in selected brain areas; TfR1 and Fn mRNA expression were detected by quantitative-polymerase chain reaction (Q-PCR). Ultrastructures of the hippocampus were observed under a transmission electron microscope. Our results showed that SPS exposure induced anxiety-like symptoms and increased the level of serum cortisol and the concentration of iron in key brain areas such as the hippocampus, prefrontal cortex, and striatum. The stress induced region-specific changes in both protein and mRNA levels of TfR1 and Fn. Moreover, swelling mitochondria and cell apoptosis were observed in neurons in brain regions with iron accumulation. We concluded that SPS stress increased iron in some cognition-related brain regions and subsequently cause neuronal injury, indicating that the iron may function in the pathology of PTSD. Copyright © 2016 IBRO. Published by Elsevier Ltd. All rights reserved.

  6. Human iPSC-Derived Neuronal Model of Tau-A152T Frontotemporal Dementia Reveals Tau-Mediated Mechanisms of Neuronal Vulnerability

    Directory of Open Access Journals (Sweden)

    M. Catarina Silva

    2016-09-01

    Full Text Available Frontotemporal dementia (FTD and other tauopathies characterized by focal brain neurodegeneration and pathological accumulation of proteins are commonly associated with tau mutations. However, the mechanism of neuronal loss is not fully understood. To identify molecular events associated with tauopathy, we studied induced pluripotent stem cell (iPSC-derived neurons from individuals carrying the tau-A152T variant. We highlight the potential of in-depth phenotyping of human neuronal cell models for pre-clinical studies and identification of modulators of endogenous tau toxicity. Through a panel of biochemical and cellular assays, A152T neurons showed accumulation, redistribution, and decreased solubility of tau. Upregulation of tau was coupled to enhanced stress-inducible markers and cell vulnerability to proteotoxic, excitotoxic, and mitochondrial stressors, which was rescued upon CRISPR/Cas9-mediated targeting of tau or by pharmacological activation of autophagy. Our findings unmask tau-mediated perturbations of specific pathways associated with neuronal vulnerability, revealing potential early disease biomarkers and therapeutic targets for FTD and other tauopathies.

  7. Computational Models of Neuron-Astrocyte Interactions Lead to Improved Efficacy in the Performance of Neural Networks

    Science.gov (United States)

    Alvarellos-González, Alberto; Pazos, Alejandro; Porto-Pazos, Ana B.

    2012-01-01

    The importance of astrocytes, one part of the glial system, for information processing in the brain has recently been demonstrated. Regarding information processing in multilayer connectionist systems, it has been shown that systems which include artificial neurons and astrocytes (Artificial Neuron-Glia Networks) have well-known advantages over identical systems including only artificial neurons. Since the actual impact of astrocytes in neural network function is unknown, we have investigated, using computational models, different astrocyte-neuron interactions for information processing; different neuron-glia algorithms have been implemented for training and validation of multilayer Artificial Neuron-Glia Networks oriented toward classification problem resolution. The results of the tests performed suggest that all the algorithms modelling astrocyte-induced synaptic potentiation improved artificial neural network performance, but their efficacy depended on the complexity of the problem. PMID:22649480

  8. Neural dynamics as sampling: a model for stochastic computation in recurrent networks of spiking neurons.

    Science.gov (United States)

    Buesing, Lars; Bill, Johannes; Nessler, Bernhard; Maass, Wolfgang

    2011-11-01

    The organization of computations in networks of spiking neurons in the brain is still largely unknown, in particular in view of the inherently stochastic features of their firing activity and the experimentally observed trial-to-trial variability of neural systems in the brain. In principle there exists a powerful computational framework for stochastic computations, probabilistic inference by sampling, which can explain a large number of macroscopic experimental data in neuroscience and cognitive science. But it has turned out to be surprisingly difficult to create a link between these abstract models for stochastic computations and more detailed models of the dynamics of networks of spiking neurons. Here we create such a link and show that under some conditions the stochastic firing activity of networks of spiking neurons can be interpreted as probabilistic inference via Markov chain Monte Carlo (MCMC) sampling. Since common methods for MCMC sampling in distributed systems, such as Gibbs sampling, are inconsistent with the dynamics of spiking neurons, we introduce a different approach based on non-reversible Markov chains that is able to reflect inherent temporal processes of spiking neuronal activity through a suitable choice of random variables. We propose a neural network model and show by a rigorous theoretical analysis that its neural activity implements MCMC sampling of a given distribution, both for the case of discrete and continuous time. This provides a step towards closing the gap between abstract functional models of cortical computation and more detailed models of networks of spiking neurons.

  9. Probabilistic Inference in General Graphical Models through Sampling in Stochastic Networks of Spiking Neurons

    Science.gov (United States)

    Pecevski, Dejan; Buesing, Lars; Maass, Wolfgang

    2011-01-01

    An important open problem of computational neuroscience is the generic organization of computations in networks of neurons in the brain. We show here through rigorous theoretical analysis that inherent stochastic features of spiking neurons, in combination with simple nonlinear computational operations in specific network motifs and dendritic arbors, enable networks of spiking neurons to carry out probabilistic inference through sampling in general graphical models. In particular, it enables them to carry out probabilistic inference in Bayesian networks with converging arrows (“explaining away”) and with undirected loops, that occur in many real-world tasks. Ubiquitous stochastic features of networks of spiking neurons, such as trial-to-trial variability and spontaneous activity, are necessary ingredients of the underlying computational organization. We demonstrate through computer simulations that this approach can be scaled up to neural emulations of probabilistic inference in fairly large graphical models, yielding some of the most complex computations that have been carried out so far in networks of spiking neurons. PMID:22219717

  10. Probabilistic inference in general graphical models through sampling in stochastic networks of spiking neurons.

    Directory of Open Access Journals (Sweden)

    Dejan Pecevski

    2011-12-01

    Full Text Available An important open problem of computational neuroscience is the generic organization of computations in networks of neurons in the brain. We show here through rigorous theoretical analysis that inherent stochastic features of spiking neurons, in combination with simple nonlinear computational operations in specific network motifs and dendritic arbors, enable networks of spiking neurons to carry out probabilistic inference through sampling in general graphical models. In particular, it enables them to carry out probabilistic inference in Bayesian networks with converging arrows ("explaining away" and with undirected loops, that occur in many real-world tasks. Ubiquitous stochastic features of networks of spiking neurons, such as trial-to-trial variability and spontaneous activity, are necessary ingredients of the underlying computational organization. We demonstrate through computer simulations that this approach can be scaled up to neural emulations of probabilistic inference in fairly large graphical models, yielding some of the most complex computations that have been carried out so far in networks of spiking neurons.

  11. Probabilistic inference in general graphical models through sampling in stochastic networks of spiking neurons.

    Science.gov (United States)

    Pecevski, Dejan; Buesing, Lars; Maass, Wolfgang

    2011-12-01

    An important open problem of computational neuroscience is the generic organization of computations in networks of neurons in the brain. We show here through rigorous theoretical analysis that inherent stochastic features of spiking neurons, in combination with simple nonlinear computational operations in specific network motifs and dendritic arbors, enable networks of spiking neurons to carry out probabilistic inference through sampling in general graphical models. In particular, it enables them to carry out probabilistic inference in Bayesian networks with converging arrows ("explaining away") and with undirected loops, that occur in many real-world tasks. Ubiquitous stochastic features of networks of spiking neurons, such as trial-to-trial variability and spontaneous activity, are necessary ingredients of the underlying computational organization. We demonstrate through computer simulations that this approach can be scaled up to neural emulations of probabilistic inference in fairly large graphical models, yielding some of the most complex computations that have been carried out so far in networks of spiking neurons.

  12. Multi-timescale Modeling of Activity-Dependent Metabolic Coupling in the Neuron-Glia-Vasculature Ensemble

    KAUST Repository

    Jolivet, Renaud; Coggan, Jay S.; Allaman, Igor; Magistretti, Pierre J.

    2015-01-01

    time integrates the respective timescales at which energy metabolism and neuronal excitability occur. The model is constrained by relative neuronal and astrocytic oxygen and glucose utilization, by the concentration of metabolites at rest

  13. Differentiation-Dependent Energy Production and Metabolite Utilization: A Comparative Study on Neural Stem Cells, Neurons, and Astrocytes

    Science.gov (United States)

    Jády, Attila Gy.; Nagy, Ádám M.; Kőhidi, Tímea; Ferenczi, Szilamér; Tretter, László

    2016-01-01

    While it is evident that the metabolic machinery of stem cells should be fairly different from that of differentiated neurons, the basic energy production pathways in neural stem cells (NSCs) or in neurons are far from clear. Using the model of in vitro neuron production by NE-4C NSCs, this study focused on the metabolic changes taking place during the in vitro neuronal differentiation. O2 consumption, H+ production, and metabolic responses to single metabolites were measured in cultures of NSCs and in their neuronal derivatives, as well as in primary neuronal and astroglial cultures. In metabolite-free solutions, NSCs consumed little O2 and displayed a higher level of mitochondrial proton leak than neurons. In stem cells, glycolysis was the main source of energy for the survival of a 2.5-h period of metabolite deprivation. In contrast, stem cell-derived or primary neurons sustained a high-level oxidative phosphorylation during metabolite deprivation, indicating the consumption of own cellular material for energy production. The stem cells increased O2 consumption and mitochondrial ATP production in response to single metabolites (with the exception of glucose), showing rapid adaptation of the metabolic machinery to the available resources. In contrast, single metabolites did not increase the O2 consumption of neurons or astrocytes. In “starving” neurons, neither lactate nor pyruvate was utilized for mitochondrial ATP production. Gene expression studies also suggested that aerobic glycolysis and rapid metabolic adaptation characterize the NE-4C NSCs, while autophagy and alternative glucose utilization play important roles in the metabolism of stem cell-derived neurons. PMID:27116891

  14. Multiplicative multifractal modeling and discrimination of human neuronal activity

    International Nuclear Information System (INIS)

    Zheng Yi; Gao Jianbo; Sanchez, Justin C.; Principe, Jose C.; Okun, Michael S.

    2005-01-01

    Understanding neuronal firing patterns is one of the most important problems in theoretical neuroscience. It is also very important for clinical neurosurgery. In this Letter, we introduce a computational procedure to examine whether neuronal firing recordings could be characterized by cascade multiplicative multifractals. By analyzing raw recording data as well as generated spike train data from 3 patients collected in two brain areas, the globus pallidus externa (GPe) and the globus pallidus interna (GPi), we show that the neural firings are consistent with a multifractal process over certain time scale range (t 1 ,t 2 ), where t 1 is argued to be not smaller than the mean inter-spike-interval of neuronal firings, while t 2 may be related to the time that neuronal signals propagate in the major neural branching structures pertinent to GPi and GPe. The generalized dimension spectrum D q effectively differentiates the two brain areas, both intra- and inter-patients. For distinguishing between GPe and GPi, it is further shown that the cascade model is more effective than the methods recently examined by Schiff et al. as well as the Fano factor analysis. Therefore, the methodology may be useful in developing computer aided tools to help clinicians perform precision neurosurgery in the operating room

  15. The Morris-Lecar neuron model embeds a leaky integrate-and-fire model

    DEFF Research Database (Denmark)

    Ditlevsen, Susanne; Greenwood, Priscilla

    2013-01-01

    We showthat the stochastic Morris–Lecar neuron, in a neighborhood of its stable point, can be approximated by a two-dimensional Ornstein Uhlenbeck (OU) modulation of a constant circular motion. The associated radial OU process is an example of a leaky integrate-and-fire (LIF) model prior to firing...

  16. Response of Electrical Activity in an Improved Neuron Model under Electromagnetic Radiation and Noise.

    Science.gov (United States)

    Zhan, Feibiao; Liu, Shenquan

    2017-01-01

    Electrical activities are ubiquitous neuronal bioelectric phenomena, which have many different modes to encode the expression of biological information, and constitute the whole process of signal propagation between neurons. Therefore, we focus on the electrical activities of neurons, which is also causing widespread concern among neuroscientists. In this paper, we mainly investigate the electrical activities of the Morris-Lecar (M-L) model with electromagnetic radiation or Gaussian white noise, which can restore the authenticity of neurons in realistic neural network. First, we explore dynamical response of the whole system with electromagnetic induction (EMI) and Gaussian white noise. We find that there are slight differences in the discharge behaviors via comparing the response of original system with that of improved system, and electromagnetic induction can transform bursting or spiking state to quiescent state and vice versa. Furthermore, we research bursting transition mode and the corresponding periodic solution mechanism for the isolated neuron model with electromagnetic induction by using one-parameter and bi-parameters bifurcation analysis. Finally, we analyze the effects of Gaussian white noise on the original system and coupled system, which is conducive to understand the actual discharge properties of realistic neurons.

  17. Response of Electrical Activity in an Improved Neuron Model under Electromagnetic Radiation and Noise

    Directory of Open Access Journals (Sweden)

    Feibiao Zhan

    2017-11-01

    Full Text Available Electrical activities are ubiquitous neuronal bioelectric phenomena, which have many different modes to encode the expression of biological information, and constitute the whole process of signal propagation between neurons. Therefore, we focus on the electrical activities of neurons, which is also causing widespread concern among neuroscientists. In this paper, we mainly investigate the electrical activities of the Morris-Lecar (M-L model with electromagnetic radiation or Gaussian white noise, which can restore the authenticity of neurons in realistic neural network. First, we explore dynamical response of the whole system with electromagnetic induction (EMI and Gaussian white noise. We find that there are slight differences in the discharge behaviors via comparing the response of original system with that of improved system, and electromagnetic induction can transform bursting or spiking state to quiescent state and vice versa. Furthermore, we research bursting transition mode and the corresponding periodic solution mechanism for the isolated neuron model with electromagnetic induction by using one-parameter and bi-parameters bifurcation analysis. Finally, we analyze the effects of Gaussian white noise on the original system and coupled system, which is conducive to understand the actual discharge properties of realistic neurons.

  18. A Hebbian learning rule gives rise to mirror neurons and links them to control theoretic inverse models

    OpenAIRE

    Hanuschkin, A.; Ganguli, S.; Hahnloser, R. H. R.

    2013-01-01

    Mirror neurons are neurons whose responses to the observation of a motor act resemble responses measured during production of that act. Computationally, mirror neurons have been viewed as evidence for the existence of internal inverse models. Such models, rooted within control theory, map-desired sensory targets onto the motor commands required to generate those targets. To jointly explore both the formation of mirrored responses and their functional contribution to inverse models, we develop...

  19. Excitability of Aβ sensory neurons is altered in an animal model of peripheral neuropathy

    Directory of Open Access Journals (Sweden)

    Zhu Yong

    2012-01-01

    Full Text Available Abstract Background Causes of neuropathic pain following nerve injury remain unclear, limiting the development of mechanism-based therapeutic approaches. Animal models have provided some directions, but little is known about the specific sensory neurons that undergo changes in such a way as to induce and maintain activation of sensory pain pathways. Our previous studies implicated changes in the Aβ, normally non-nociceptive neurons in activating spinal nociceptive neurons in a cuff-induced animal model of neuropathic pain and the present study was directed specifically at determining any change in excitability of these neurons. Thus, the present study aimed at recording intracellularly from Aβ-fiber dorsal root ganglion (DRG neurons and determining excitability of the peripheral receptive field, of the cell body and of the dorsal roots. Methods A peripheral neuropathy was induced in Sprague Dawley rats by inserting two thin polyethylene cuffs around the right sciatic nerve. All animals were confirmed to exhibit tactile hypersensitivity to von Frey filaments three weeks later, before the acute electrophysiological experiments. Under stable intracellular recording conditions neurons were classified functionally on the basis of their response to natural activation of their peripheral receptive field. In addition, conduction velocity of the dorsal roots, configuration of the action potential and rate of adaptation to stimulation were also criteria for classification. Excitability was measured as the threshold to activation of the peripheral receptive field, the response to intracellular injection of depolarizing current into the soma and the response to electrical stimulation of the dorsal roots. Results In control animals mechanical thresholds of all neurons were within normal ranges. Aβ DRG neurons in neuropathic rats demonstrated a mean mechanical threshold to receptive field stimulation that were significantly lower than in control rats, a

  20. Timescales and Mechanisms of Sigh-Like Bursting and Spiking in Models of Rhythmic Respiratory Neurons.

    Science.gov (United States)

    Wang, Yangyang; Rubin, Jonathan E

    2017-12-01

    Neural networks generate a variety of rhythmic activity patterns, often involving different timescales. One example arises in the respiratory network in the pre-Bötzinger complex of the mammalian brainstem, which can generate the eupneic rhythm associated with normal respiration as well as recurrent low-frequency, large-amplitude bursts associated with sighing. Two competing hypotheses have been proposed to explain sigh generation: the recruitment of a neuronal population distinct from the eupneic rhythm-generating subpopulation or the reconfiguration of activity within a single population. Here, we consider two recent computational models, one of which represents each of the hypotheses. We use methods of dynamical systems theory, such as fast-slow decomposition, averaging, and bifurcation analysis, to understand the multiple-timescale mechanisms underlying sigh generation in each model. In the course of our analysis, we discover that a third timescale is required to generate sighs in both models. Furthermore, we identify the similarities of the underlying mechanisms in the two models and the aspects in which they differ.

  1. Contribution of synchronized GABAergic neurons to dopaminergic neuron firing and bursting.

    Science.gov (United States)

    Morozova, Ekaterina O; Myroshnychenko, Maxym; Zakharov, Denis; di Volo, Matteo; Gutkin, Boris; Lapish, Christopher C; Kuznetsov, Alexey

    2016-10-01

    In the ventral tegmental area (VTA), interactions between dopamine (DA) and γ-aminobutyric acid (GABA) neurons are critical for regulating DA neuron activity and thus DA efflux. To provide a mechanistic explanation of how GABA neurons influence DA neuron firing, we developed a circuit model of the VTA. The model is based on feed-forward inhibition and recreates canonical features of the VTA neurons. Simulations revealed that γ-aminobutyric acid (GABA) receptor (GABAR) stimulation can differentially influence the firing pattern of the DA neuron, depending on the level of synchronization among GABA neurons. Asynchronous activity of GABA neurons provides a constant level of inhibition to the DA neuron and, when removed, produces a classical disinhibition burst. In contrast, when GABA neurons are synchronized by common synaptic input, their influence evokes additional spikes in the DA neuron, resulting in increased measures of firing and bursting. Distinct from previous mechanisms, the increases were not based on lowered firing rate of the GABA neurons or weaker hyperpolarization by the GABAR synaptic current. This phenomenon was induced by GABA-mediated hyperpolarization of the DA neuron that leads to decreases in intracellular calcium (Ca 2+ ) concentration, thus reducing the Ca 2+ -dependent potassium (K + ) current. In this way, the GABA-mediated hyperpolarization replaces Ca 2+ -dependent K + current; however, this inhibition is pulsatile, which allows the DA neuron to fire during the rhythmic pauses in inhibition. Our results emphasize the importance of inhibition in the VTA, which has been discussed in many studies, and suggest a novel mechanism whereby computations can occur locally. Copyright © 2016 the American Physiological Society.

  2. NEURON and Python.

    Science.gov (United States)

    Hines, Michael L; Davison, Andrew P; Muller, Eilif

    2009-01-01

    The NEURON simulation program now allows Python to be used, alone or in combination with NEURON's traditional Hoc interpreter. Adding Python to NEURON has the immediate benefit of making available a very extensive suite of analysis tools written for engineering and science. It also catalyzes NEURON software development by offering users a modern programming tool that is recognized for its flexibility and power to create and maintain complex programs. At the same time, nothing is lost because all existing models written in Hoc, including graphical user interface tools, continue to work without change and are also available within the Python context. An example of the benefits of Python availability is the use of the xml module in implementing NEURON's Import3D and CellBuild tools to read MorphML and NeuroML model specifications.

  3. Multiple time scales of adaptation in auditory cortex neurons.

    Science.gov (United States)

    Ulanovsky, Nachum; Las, Liora; Farkas, Dina; Nelken, Israel

    2004-11-17

    Neurons in primary auditory cortex (A1) of cats show strong stimulus-specific adaptation (SSA). In probabilistic settings, in which one stimulus is common and another is rare, responses to common sounds adapt more strongly than responses to rare sounds. This SSA could be a correlate of auditory sensory memory at the level of single A1 neurons. Here we studied adaptation in A1 neurons, using three different probabilistic designs. We showed that SSA has several time scales concurrently, spanning many orders of magnitude, from hundreds of milliseconds to tens of seconds. Similar time scales are known for the auditory memory span of humans, as measured both psychophysically and using evoked potentials. A simple model, with linear dependence on both short-term and long-term stimulus history, provided a good fit to A1 responses. Auditory thalamus neurons did not show SSA, and their responses were poorly fitted by the same model. In addition, SSA increased the proportion of failures in the responses of A1 neurons to the adapting stimulus. Finally, SSA caused a bias in the neuronal responses to unbiased stimuli, enhancing the responses to eccentric stimuli. Therefore, we propose that a major function of SSA in A1 neurons is to encode auditory sensory memory on multiple time scales. This SSA might play a role in stream segregation and in binding of auditory objects over many time scales, a property that is crucial for processing of natural auditory scenes in cats and of speech and music in humans.

  4. Nogo-receptor 1 antagonization in combination with neurotrophin-4/5 is not superior to single factor treatment in promoting survival and morphological complexity of cultured dopaminergic neurons.

    Science.gov (United States)

    Seiler, Stefanie; Di Santo, Stefano; Sahli, Sebastian; Andereggen, Lukas; Widmer, Hans Rudolf

    2017-08-01

    Cell transplantation using ventral mesencephalic tissue is an experimental approach to treat Parkinson's disease. This approach is limited by poor survival of the transplants and the high number of dopaminergic neurons needed for grafting. Increasing the yield of dopaminergic neurons in donor tissue is of great importance. We have previously shown that antagonization of the Nogo-receptor 1 by NEP1-40 promoted survival of cultured dopaminergic neurons and exposure to neurotrophin-4/5 increased dopaminergic cell densities in organotypic midbrain cultures. We investigated whether a combination of both treatments offers a novel tool to further improve dopaminergic neuron survival. Rat embryonic ventral mesencephalic neurons grown as organotypic free-floating roller tube or primary dissociated cultures were exposed to neurotrophin-4/5 and NEP1-40. The combined and single factor treatment resulted in significantly higher numbers of tyrosine hydroxylase positive neurons compared to controls. Significantly stronger tyrosine hydroxylase signal intensity was detected by Western blotting in the combination-treated cultures compared to controls but not compared to single factor treatments. Neurotrophin-4/5 and the combined treatment showed significantly higher signals for the neuronal marker microtubule-associated protein 2 in Western blots compared to control while no effects were observed for the astroglial marker glial fibrillary acidic protein between groups, suggesting that neurotrophin-4/5 targets mainly neuronal cells. Finally, NEP1-40 and the combined treatment significantly augmented tyrosine hydroxylase positive neurite length. Summarizing, our findings substantiate that antagonization of the Nogo-receptor 1 promotes dopaminergic neurons but does not further increase the yield of dopaminergic neurons and their morphological complexity when combined with neurotrophin-4/5 hinting to the idea that these treatments might exert their effects by activating common

  5. Pathophysiological role of prostaglandin E2-induced up-regulation of the EP2 receptor in motor neuron-like NSC-34 cells and lumbar motor neurons in ALS model mice.

    Science.gov (United States)

    Kosuge, Yasuhiro; Miyagishi, Hiroko; Yoneoka, Yuki; Yoneda, Keiko; Nango, Hiroshi; Ishige, Kumiko; Ito, Yoshihisa

    2017-07-04

    Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease characterized by selective degeneration of motor neurons. The primary triggers for motor neuronal death are still unknown, but inflammation is considered to be an important factor contributing to the pathophysiology of ALS both clinically and in ALS models. Prostaglandin E2 (PGE2) and its corresponding four E-prostanoid receptors play a pivotal role in the degeneration of motor neurons in human and transgenic models of ALS. It has also been shown that PGE2-EP2 signaling in glial cells (astrocytes or microglia) promotes motor neuronal death in G93A mice. The present study was designed to investigate the levels of expression of EP receptors in the spinal motor neurons of ALS model mice and to examine whether PGE2 alters the expression of EP receptors in differentiated NSC-34 cells, a motor neuron-like cell line. Immunohistochemical staining demonstrated that EP2 and EP3 immunoreactivity was localized in NeuN-positive large cells showing the typical morphology of motor neurons in mice. Semi-quantitative analysis showed that the immunoreactivity of EP2 in motor neurons was significantly increased in the early symptomatic stage in ALS model mice. In contrast, the level of EP3 expression remained constant, irrespective of age. In differentiated NSC-34 cells, bath application of PGE2 resulted in a concentration-dependent decrease of MTT reduction. Although PGE2 had no effect on cell survival at concentrations of less than 10 μM, pretreatment with 10 μM PGE2 significantly up-regulated EP2 and concomitantly potentiated cell death induced by 30 μM PGE2. These results suggest that PGE2 is an important effector for induction of the EP2 subtype in differentiated NSC-34 cells, and that not only EP2 up-regulation in glial cells but also EP2 up-regulation in motor neurons plays a pivotal role in the vulnerability of motor neurons in ALS model mice. Copyright © 2017 Elsevier Ltd. All rights

  6. Substrates for Neuronal Cotransmission With Neuropeptides and Small Molecule Neurotransmitters in Drosophila

    Directory of Open Access Journals (Sweden)

    Dick R. Nässel

    2018-03-01

    Full Text Available It has been known for more than 40 years that individual neurons can produce more than one neurotransmitter and that neuropeptides often are colocalized with small molecule neurotransmitters (SMNs. Over the years much progress has been made in understanding the functional consequences of cotransmission in the nervous system of mammals. There are also some excellent invertebrate models that have revealed roles of coexpressed neuropeptides and SMNs in increasing complexity, flexibility, and dynamics in neuronal signaling. However, for the fly Drosophila there are surprisingly few functional studies on cotransmission, although there is ample evidence for colocalization of neuroactive compounds in neurons of the CNS, based both on traditional techniques and novel single cell transcriptome analysis. With the hope to trigger interest in initiating cotransmission studies, this review summarizes what is known about Drosophila neurons and neuronal circuits where different neuropeptides and SMNs are colocalized. Coexistence of neuroactive substances has been recorded in different neuron types such as neuroendocrine cells, interneurons, sensory cells and motor neurons. Some of the circuits highlighted here are well established in the analysis of learning and memory, circadian clock networks regulating rhythmic activity and sleep, as well as neurons and neuroendocrine cells regulating olfaction, nociception, feeding, metabolic homeostasis, diuretic functions, reproduction, and developmental processes. One emerging trait is the broad role of short neuropeptide F in cotransmission and presynaptic facilitation in a number of different neuronal circuits. This review also discusses the functional relevance of coexisting peptides in the intestine. Based on recent single cell transcriptomics data, it is likely that the neuronal systems discussed in this review are just a fraction of the total set of circuits where cotransmission occurs in Drosophila. Thus, a

  7. Substrates for Neuronal Cotransmission With Neuropeptides and Small Molecule Neurotransmitters in Drosophila

    Science.gov (United States)

    Nässel, Dick R.

    2018-01-01

    It has been known for more than 40 years that individual neurons can produce more than one neurotransmitter and that neuropeptides often are colocalized with small molecule neurotransmitters (SMNs). Over the years much progress has been made in understanding the functional consequences of cotransmission in the nervous system of mammals. There are also some excellent invertebrate models that have revealed roles of coexpressed neuropeptides and SMNs in increasing complexity, flexibility, and dynamics in neuronal signaling. However, for the fly Drosophila there are surprisingly few functional studies on cotransmission, although there is ample evidence for colocalization of neuroactive compounds in neurons of the CNS, based both on traditional techniques and novel single cell transcriptome analysis. With the hope to trigger interest in initiating cotransmission studies, this review summarizes what is known about Drosophila neurons and neuronal circuits where different neuropeptides and SMNs are colocalized. Coexistence of neuroactive substances has been recorded in different neuron types such as neuroendocrine cells, interneurons, sensory cells and motor neurons. Some of the circuits highlighted here are well established in the analysis of learning and memory, circadian clock networks regulating rhythmic activity and sleep, as well as neurons and neuroendocrine cells regulating olfaction, nociception, feeding, metabolic homeostasis, diuretic functions, reproduction, and developmental processes. One emerging trait is the broad role of short neuropeptide F in cotransmission and presynaptic facilitation in a number of different neuronal circuits. This review also discusses the functional relevance of coexisting peptides in the intestine. Based on recent single cell transcriptomics data, it is likely that the neuronal systems discussed in this review are just a fraction of the total set of circuits where cotransmission occurs in Drosophila. Thus, a systematic search for

  8. Binding by asynchrony: the neuronal phase code

    Directory of Open Access Journals (Sweden)

    Zoltan Nadasdy

    2010-09-01

    Full Text Available Neurons display continuous subthreshold oscillations and discrete action potentials. When action potentials are phase-locked to the subthreshold oscillation, we hypothesize they represent two types of information: the presence/absence of a sensory feature and the phase of subthreshold oscillation. If subthreshold oscillation phases are neuron-specific, then the sources of action potentials can be recovered based on the action potential times. If the spatial information about the stimulus is converted to action potential phases, then action potentials from multiple neurons can be combined into a single axon and the spatial configuration reconstructed elsewhere. For the reconstruction to be successful, we introduce two assumptions: that a subthreshold oscillation field has a constant phase gradient and that coincidences between action potentials and intracellular subthreshold oscillations are neuron-specific as defined by the "interference principle." Under these assumptions, a phase coding model enables information transfer between structures and reproduces experimental phenomenons such as phase precession, grid cell architecture, and phase modulation of cortical spikes. This article reviews a recently proposed neuronal algorithm for information encoding and decoding from the phase of action potentials (Nadasdy 2009. The focus is given to the principles common across different systems instead of emphasizing system specific differences.

  9. Plasticity-modulated seizure dynamics for seizure termination in realistic neuronal models

    NARCIS (Netherlands)

    Koppert, M.M.J.; Kalitzin, S.; Lopes da Silva, F.H.; Viergever, M.A.

    2011-01-01

    In previous studies we showed that autonomous absence seizure generation and termination can be explained by realistic neuronal models eliciting bi-stable dynamics. In these models epileptic seizures are triggered either by external stimuli (reflex epilepsies) or by internal fluctuations. This

  10. Enhanced Peptide Detection Toward Single-Neuron Proteomics by Reversed-Phase Fractionation Capillary Electrophoresis Mass Spectrometry

    Science.gov (United States)

    Choi, Sam B.; Lombard-Banek, Camille; Muñoz-LLancao, Pablo; Manzini, M. Chiara; Nemes, Peter

    2018-05-01

    The ability to detect peptides and proteins in single cells is vital for understanding cell heterogeneity in the nervous system. Capillary electrophoresis (CE) nanoelectrospray ionization (nanoESI) provides high-resolution mass spectrometry (HRMS) with trace-level sensitivity, but compressed separation during CE challenges protein identification by tandem HRMS with limited MS/MS duty cycle. Here, we supplemented ultrasensitive CE-nanoESI-HRMS with reversed-phase (RP) fractionation to enhance identifications from protein digest amounts that approximate to a few mammalian neurons. An 1 to 20 μg neuronal protein digest was fractionated on a RP column (ZipTip), and 1 ng to 500 pg of peptides were analyzed by a custom-built CE-HRMS system. Compared with the control (no fractionation), RP fractionation improved CE separation (theoretical plates 274,000 versus 412,000 maximum, resp.), which enhanced detection sensitivity (2.5-fold higher signal-to-noise ratio), minimized co-isolation spectral interferences during MS/MS, and increased the temporal rate of peptide identification by up to 57%. From 1 ng of protein digest (organization. [Figure not available: see fulltext.

  11. Routes to Chaos Induced by a Discontinuous Resetting Process in a Hybrid Spiking Neuron Model.

    Science.gov (United States)

    Nobukawa, Sou; Nishimura, Haruhiko; Yamanishi, Teruya

    2018-01-10

    Several hybrid spiking neuron models combining continuous spike generation mechanisms and discontinuous resetting processes following spiking have been proposed. The Izhikevich neuron model, for example, can reproduce many spiking patterns. This model clearly possesses various types of bifurcations and routes to chaos under the effect of a state-dependent jump in the resetting process. In this study, we focus further on the relation between chaotic behaviour and the state-dependent jump, approaching the subject by comparing spiking neuron model versions with and without the resetting process. We first adopt a continuous two-dimensional spiking neuron model in which the orbit in the spiking state does not exhibit divergent behaviour. We then insert the resetting process into the model. An evaluation using the Lyapunov exponent with a saltation matrix and a characteristic multiplier of the Poincar'e map reveals that two types of chaotic behaviour (i.e. bursting chaotic spikes and near-period-two chaotic spikes) are induced by the resetting process. In addition, we confirm that this chaotic bursting state is generated from the periodic spiking state because of the slow- and fast-scale dynamics that arise when jumping to the hyperpolarization and depolarization regions, respectively.

  12. A Note on Some Numerical Approaches to Solve a θ˙ Neuron Networks Model

    Directory of Open Access Journals (Sweden)

    Samir Kumar Bhowmik

    2014-01-01

    Full Text Available Space time integration plays an important role in analyzing scientific and engineering models. In this paper, we consider an integrodifferential equation that comes from modeling θ˙ neuron networks. Here, we investigate various schemes for time discretization of a theta-neuron model. We use collocation and midpoint quadrature formula for space integration and then apply various time integration schemes to get a full discrete system. We present some computational results to demonstrate the schemes.

  13. A Tractable Method for Describing Complex Couplings between Neurons and Population Rate.

    Science.gov (United States)

    Gardella, Christophe; Marre, Olivier; Mora, Thierry

    2016-01-01

    Neurons within a population are strongly correlated, but how to simply capture these correlations is still a matter of debate. Recent studies have shown that the activity of each cell is influenced by the population rate, defined as the summed activity of all neurons in the population. However, an explicit, tractable model for these interactions is still lacking. Here we build a probabilistic model of population activity that reproduces the firing rate of each cell, the distribution of the population rate, and the linear coupling between them. This model is tractable, meaning that its parameters can be learned in a few seconds on a standard computer even for large population recordings. We inferred our model for a population of 160 neurons in the salamander retina. In this population, single-cell firing rates depended in unexpected ways on the population rate. In particular, some cells had a preferred population rate at which they were most likely to fire. These complex dependencies could not be explained by a linear coupling between the cell and the population rate. We designed a more general, still tractable model that could fully account for these nonlinear dependencies. We thus provide a simple and computationally tractable way to learn models that reproduce the dependence of each neuron on the population rate.

  14. The synchronization of asymmetric-structured electric coupling neuronal system

    Science.gov (United States)

    Wang, Guanping; Jin, Wuyin; Liu, Hao; Sun, Wei

    2018-02-01

    Based on the Hindmarsh-Rose (HR) model, the synchronization dynamics of asymmetric-structured electric coupling two neuronal system is investigated in this paper. It is discovered that when the time-delay scope and coupling strength for the synchronization are correlated positively under unequal time delay, the time-delay difference does not make a clear distinction between the two individual inter-spike intervals (ISI) bifurcation diagrams of the two coupled neurons. Therefore, the superficial difference of the system synchronization dynamics is not obvious for the unequal time-delay feedback. In the asymmetrical current incentives under asymmetric electric coupled system, the two neurons can only be almost completely synchronized in specific area of the interval which end-pointed with two discharge modes for a single neuron under different stimuli currents before coupling, but the intervention of time-delay feedback, together with the change of the coupling strength, can make the coupled system not only almost completely synchronized within anywhere in the front area, but also outside of it.

  15. NeuronMetrics: software for semi-automated processing of cultured neuron images.

    Science.gov (United States)

    Narro, Martha L; Yang, Fan; Kraft, Robert; Wenk, Carola; Efrat, Alon; Restifo, Linda L

    2007-03-23

    Using primary cell culture to screen for changes in neuronal morphology requires specialized analysis software. We developed NeuronMetrics for semi-automated, quantitative analysis of two-dimensional (2D) images of fluorescently labeled cultured neurons. It skeletonizes the neuron image using two complementary image-processing techniques, capturing fine terminal neurites with high fidelity. An algorithm was devised to span wide gaps in the skeleton. NeuronMetrics uses a novel strategy based on geometric features called faces to extract a branch number estimate from complex arbors with numerous neurite-to-neurite contacts, without creating a precise, contact-free representation of the neurite arbor. It estimates total neurite length, branch number, primary neurite number, territory (the area of the convex polygon bounding the skeleton and cell body), and Polarity Index (a measure of neuronal polarity). These parameters provide fundamental information about the size and shape of neurite arbors, which are critical factors for neuronal function. NeuronMetrics streamlines optional manual tasks such as removing noise, isolating the largest primary neurite, and correcting length for self-fasciculating neurites. Numeric data are output in a single text file, readily imported into other applications for further analysis. Written as modules for ImageJ, NeuronMetrics provides practical analysis tools that are easy to use and support batch processing. Depending on the need for manual intervention, processing time for a batch of approximately 60 2D images is 1.0-2.5 h, from a folder of images to a table of numeric data. NeuronMetrics' output accelerates the quantitative detection of mutations and chemical compounds that alter neurite morphology in vitro, and will contribute to the use of cultured neurons for drug discovery.

  16. Overexpression of cypin alters dendrite morphology, single neuron activity, and network properties via distinct mechanisms

    Science.gov (United States)

    Rodríguez, Ana R.; O'Neill, Kate M.; Swiatkowski, Przemyslaw; Patel, Mihir V.; Firestein, Bonnie L.

    2018-02-01

    Objective. This study investigates the effect that overexpression of cytosolic PSD-95 interactor (cypin), a regulator of synaptic PSD-95 protein localization and a core regulator of dendrite branching, exerts on the electrical activity of rat hippocampal neurons and networks. Approach. We cultured rat hippocampal neurons and used lipid-mediated transfection and lentiviral gene transfer to achieve high levels of cypin or cypin mutant (cypinΔPDZ PSD-95 non-binding) expression cellularly and network-wide, respectively. Main results. Our analysis revealed that although overexpression of cypin and cypinΔPDZ increase dendrite numbers and decrease spine density, cypin and cypinΔPDZ distinctly regulate neuronal activity. At the single cell level, cypin promotes decreases in bursting activity while cypinΔPDZ reduces sEPSC frequency and further decreases bursting compared to cypin. At the network level, by using the Fano factor as a measure of spike count variability, cypin overexpression results in an increase in variability of spike count, and this effect is abolished when cypin cannot bind PSD-95. This variability is also dependent on baseline activity levels and on mean spike rate over time. Finally, our spike sorting data show that overexpression of cypin results in a more complex distribution of spike waveforms and that binding to PSD-95 is essential for this complexity. Significance. Our data suggest that dendrite morphology does not play a major role in cypin action on electrical activity.

  17. Computer Modelling of Functional Aspects of Noise in Endogenously Oscillating Neurons

    Science.gov (United States)

    Huber, M. T.; Dewald, M.; Voigt, K.; Braun, H. A.; Moss, F.

    1998-03-01

    Membrane potential oscillations are a widespread feature of neuronal activity. When such oscillations operate close to the spike-triggering threshold, noise can become an essential property of spike-generation. According to that, we developed a minimal Hodgkin-Huxley-type computer model which includes a noise term. This model accounts for experimental data from quite different cells ranging from mammalian cortical neurons to fish electroreceptors. With slight modifications of the parameters, the model's behavior can be tuned to bursting activity, which additionally allows it to mimick temperature encoding in peripheral cold receptors including transitions to apparently chaotic dynamics as indicated by methods for the detection of unstable periodic orbits. Under all conditions, cooperative effects between noise and nonlinear dynamics can be shown which, beyond stochastic resonance, might be of functional significance for stimulus encoding and neuromodulation.

  18. Cofilin Inhibition Restores Neuronal Cell Death in Oxygen-Glucose Deprivation Model of Ischemia.

    Science.gov (United States)

    Madineni, Anusha; Alhadidi, Qasim; Shah, Zahoor A

    2016-03-01

    Ischemia is a condition associated with decreased blood supply to the brain, eventually leading to death of neurons. It is associated with a diverse cascade of responses involving both degenerative and regenerative mechanisms. At the cellular level, the changes are initiated prominently in the neuronal cytoskeleton. Cofilin, a cytoskeletal actin severing protein, is known to be involved in the early stages of apoptotic cell death. Evidence supports its intervention in the progression of disease states like Alzheimer's and ischemic kidney disease. In the present study, we have hypothesized the possible involvement of cofilin in ischemia. Using PC12 cells and mouse primary cultures of cortical neurons, we investigated the potential role of cofilin in ischemia in two different in vitro ischemic models: chemical induced oxidative stress and oxygen-glucose deprivation/reperfusion (OGD/R). The expression profile studies demonstrated a decrease in phosphocofilin levels in all models of ischemia, implying stress-induced cofilin activation. Furthermore, calcineurin and slingshot 1L (SSH) phosphatases were found to be the signaling mediators of the cofilin activation. In primary cultures of cortical neurons, cofilin was found to be significantly activated after 1 h of OGD. To delineate the role of activated cofilin in ischemia, we knocked down cofilin by small interfering RNA (siRNA) technique and tested the impact of cofilin silencing on neuronal viability. Cofilin siRNA-treated neurons showed a significant reduction of cofilin levels in all treatment groups (control, OGD, and OGD/R). Additionally, cofilin siRNA-reduced cofilin mitochondrial translocation and caspase 3 cleavage, with a concomitant increase in neuronal viability. These results strongly support the active role of cofilin in ischemia-induced neuronal degeneration and apoptosis. We believe that targeting this protein mediator has a potential for therapeutic intervention in ischemic brain injury and stroke.

  19. Internally generated preactivation of single neurons in human medial frontal cortex predicts volition

    OpenAIRE

    Fried, Itzhak; Mukamel, Roy; Kreiman, Gabriel

    2011-01-01

    Understanding how self-initiated behavior is encoded by neuronal circuits in the human brain remains elusive. We recorded the activity of 1019 neurons while twelve subjects performed self-initiated finger movement. We report progressive neuronal recruitment over ∼1500 ms before subjects report making the decision to move. We observed progressive increase or decrease in neuronal firing rate, particularly in the supplementary motor area (SMA), as the reported time of decision was approached. A ...

  20. Single-neuron correlates of subjective vision in the human medial temporal lobe

    OpenAIRE

    Kreiman, Gabriel; Fried, Itzhak; Koch, Christof

    2002-01-01

    Visual information from the environment is transformed into perceptual sensations through several stages of neuronal processing. Flash suppression constitutes a striking example in which the same retinal input can give rise to two different conscious visual percepts. We directly recorded the responses of individual neurons during flash suppression in the human amygdala, entorhinal cortex, hippocampus, and parahippocampal gyrus, allowing us to explore the neuronal responses in untrained subjec...

  1. Molecular and Cellular Organization of Taste Neurons in Adult Drosophila Pharynx

    Directory of Open Access Journals (Sweden)

    Yu-Chieh David Chen

    2017-12-01

    Full Text Available Summary: The Drosophila pharyngeal taste organs are poorly characterized despite their location at important sites for monitoring food quality. Functional analysis of pharyngeal neurons has been hindered by the paucity of molecular tools to manipulate them, as well as their relative inaccessibility for neurophysiological investigations. Here, we generate receptor-to-neuron maps of all three pharyngeal taste organs by performing a comprehensive chemoreceptor-GAL4/LexA expression analysis. The organization of pharyngeal neurons reveals similarities and distinctions in receptor repertoires and neuronal groupings compared to external taste neurons. We validate the mapping results by pinpointing a single pharyngeal neuron required for feeding avoidance of L-canavanine. Inducible activation of pharyngeal taste neurons reveals functional differences between external and internal taste neurons and functional subdivision within pharyngeal sweet neurons. Our results provide roadmaps of pharyngeal taste organs in an insect model system for probing the role of these understudied neurons in controlling feeding behaviors. : Chen and Dahanukar carry out a large-scale, systematic analysis to understand the molecular organization of pharyngeal taste neurons. Taking advantage of the molecular genetic toolkit that arises from this map, they use genetic dissection strategies to probe the functional roles of selected pharyngeal neurons in food choice. Keywords: Drosophila, taste, pharynx, chemosensory receptors, gustatory receptors, ionotropic receptors, feeding

  2. Properties of Neurons in External Globus Pallidus Can Support Optimal Action Selection

    Science.gov (United States)

    Bogacz, Rafal; Martin Moraud, Eduardo; Abdi, Azzedine; Magill, Peter J.; Baufreton, Jérôme

    2016-01-01

    The external globus pallidus (GPe) is a key nucleus within basal ganglia circuits that are thought to be involved in action selection. A class of computational models assumes that, during action selection, the basal ganglia compute for all actions available in a given context the probabilities that they should be selected. These models suggest that a network of GPe and subthalamic nucleus (STN) neurons computes the normalization term in Bayes’ equation. In order to perform such computation, the GPe needs to send feedback to the STN equal to a particular function of the activity of STN neurons. However, the complex form of this function makes it unlikely that individual GPe neurons, or even a single GPe cell type, could compute it. Here, we demonstrate how this function could be computed within a network containing two types of GABAergic GPe projection neuron, so-called ‘prototypic’ and ‘arkypallidal’ neurons, that have different response properties in vivo and distinct connections. We compare our model predictions with the experimentally-reported connectivity and input-output functions (f-I curves) of the two populations of GPe neurons. We show that, together, these dichotomous cell types fulfil the requirements necessary to compute the function needed for optimal action selection. We conclude that, by virtue of their distinct response properties and connectivities, a network of arkypallidal and prototypic GPe neurons comprises a neural substrate capable of supporting the computation of the posterior probabilities of actions. PMID:27389780

  3. Effects of neuronal loss in the dynamic model of neural networks

    International Nuclear Information System (INIS)

    Yoon, B-G; Choi, J; Choi, M Y

    2008-01-01

    We study the phase transitions and dynamic behavior of the dynamic model of neural networks, with an emphasis on the effects of neuronal loss due to external stress. In the absence of loss the overall results obtained numerically are found to agree excellently with the theoretical ones. When the external stress is turned on, some neurons may deteriorate and die; such loss of neurons, in general, weakens the memory in the system. As the loss increases beyond a critical value, the order parameter measuring the strength of memory decreases to zero either continuously or discontinuously, namely, the system loses its memory via a second- or a first-order transition, depending on the ratio of the refractory period to the duration of action potential

  4. Heterogeneity and convergence of olfactory first-order neurons account for the high speed and sensitivity of second-order neurons.

    Directory of Open Access Journals (Sweden)

    Jean-Pierre Rospars

    2014-12-01

    Full Text Available In the olfactory system of male moths, a specialized subset of neurons detects and processes the main component of the sex pheromone emitted by females. It is composed of several thousand first-order olfactory receptor neurons (ORNs, all expressing the same pheromone receptor, that contact synaptically a few tens of second-order projection neurons (PNs within a single restricted brain area. The functional simplicity of this system makes it a favorable model for studying the factors that contribute to its exquisite sensitivity and speed. Sensory information--primarily the identity and intensity of the stimulus--is encoded as the firing rate of the action potentials, and possibly as the latency of the neuron response. We found that over all their dynamic range, PNs respond with a shorter latency and a higher firing rate than most ORNs. Modelling showed that the increased sensitivity of PNs can be explained by the ORN-to-PN convergent architecture alone, whereas their faster response also requires cell-to-cell heterogeneity of the ORN population. So, far from being detrimental to signal detection, the ORN heterogeneity is exploited by PNs, and results in two different schemes of population coding based either on the response of a few extreme neurons (latency or on the average response of many (firing rate. Moreover, ORN-to-PN transformations are linear for latency and nonlinear for firing rate, suggesting that latency could be involved in concentration-invariant coding of the pheromone blend and that sensitivity at low concentrations is achieved at the expense of precise encoding at high concentrations.

  5. An electronic implementation for Liao's chaotic delayed neuron model with non-monotonous activation function

    International Nuclear Information System (INIS)

    Duan Shukai; Liao Xiaofeng

    2007-01-01

    A new chaotic delayed neuron model with non-monotonously increasing transfer function, called as chaotic Liao's delayed neuron model, was recently reported and analyzed. An electronic implementation of this model is described in detail. At the same time, some methods in circuit design, especially for circuit with time delayed unit and non-monotonously increasing activation unit, are also considered carefully. We find that the dynamical behaviors of the designed circuits are closely similar to the results predicted by numerical experiments

  6. An in silico agent-based model demonstrates Reelin function in directing lamination of neurons during cortical development.

    Science.gov (United States)

    Caffrey, James R; Hughes, Barry D; Britto, Joanne M; Landman, Kerry A

    2014-01-01

    The characteristic six-layered appearance of the neocortex arises from the correct positioning of pyramidal neurons during development and alterations in this process can cause intellectual disabilities and developmental delay. Malformations in cortical development arise when neurons either fail to migrate properly from the germinal zones or fail to cease migration in the correct laminar position within the cortical plate. The Reelin signalling pathway is vital for correct neuronal positioning as loss of Reelin leads to a partially inverted cortex. The precise biological function of Reelin remains controversial and debate surrounds its role as a chemoattractant or stop signal for migrating neurons. To investigate this further we developed an in silico agent-based model of cortical layer formation. Using this model we tested four biologically plausible hypotheses for neuron motility and four biologically plausible hypotheses for the loss of neuron motility (conversion from migration). A matrix of 16 combinations of motility and conversion rules was applied against the known structure of mouse cortical layers in the wild-type cortex, the Reelin-null mutant, the Dab1-null mutant and a conditional Dab1 mutant. Using this approach, many combinations of motility and conversion mechanisms can be rejected. For example, the model does not support Reelin acting as a repelling or as a stopping signal. In contrast, the study lends very strong support to the notion that the glycoprotein Reelin acts as a chemoattractant for neurons. Furthermore, the most viable proposition for the conversion mechanism is one in which conversion is affected by a motile neuron sensing in the near vicinity neurons that have already converted. Therefore, this model helps elucidate the function of Reelin during neuronal migration and cortical development.

  7. An in silico agent-based model demonstrates Reelin function in directing lamination of neurons during cortical development.

    Directory of Open Access Journals (Sweden)

    James R Caffrey

    Full Text Available The characteristic six-layered appearance of the neocortex arises from the correct positioning of pyramidal neurons during development and alterations in this process can cause intellectual disabilities and developmental delay. Malformations in cortical development arise when neurons either fail to migrate properly from the germinal zones or fail to cease migration in the correct laminar position within the cortical plate. The Reelin signalling pathway is vital for correct neuronal positioning as loss of Reelin leads to a partially inverted cortex. The precise biological function of Reelin remains controversial and debate surrounds its role as a chemoattractant or stop signal for migrating neurons. To investigate this further we developed an in silico agent-based model of cortical layer formation. Using this model we tested four biologically plausible hypotheses for neuron motility and four biologically plausible hypotheses for the loss of neuron motility (conversion from migration. A matrix of 16 combinations of motility and conversion rules was applied against the known structure of mouse cortical layers in the wild-type cortex, the Reelin-null mutant, the Dab1-null mutant and a conditional Dab1 mutant. Using this approach, many combinations of motility and conversion mechanisms can be rejected. For example, the model does not support Reelin acting as a repelling or as a stopping signal. In contrast, the study lends very strong support to the notion that the glycoprotein Reelin acts as a chemoattractant for neurons. Furthermore, the most viable proposition for the conversion mechanism is one in which conversion is affected by a motile neuron sensing in the near vicinity neurons that have already converted. Therefore, this model helps elucidate the function of Reelin during neuronal migration and cortical development.

  8. Analytical Calculation of Mutual Information between Weakly Coupled Poisson-Spiking Neurons in Models of Dynamically Gated Communication.

    Science.gov (United States)

    Cannon, Jonathan

    2017-01-01

    Mutual information is a commonly used measure of communication between neurons, but little theory exists describing the relationship between mutual information and the parameters of the underlying neuronal interaction. Such a theory could help us understand how specific physiological changes affect the capacity of neurons to synaptically communicate, and, in particular, they could help us characterize the mechanisms by which neuronal dynamics gate the flow of information in the brain. Here we study a pair of linear-nonlinear-Poisson neurons coupled by a weak synapse. We derive an analytical expression describing the mutual information between their spike trains in terms of synapse strength, neuronal activation function, the time course of postsynaptic currents, and the time course of the background input received by the two neurons. This expression allows mutual information calculations that would otherwise be computationally intractable. We use this expression to analytically explore the interaction of excitation, information transmission, and the convexity of the activation function. Then, using this expression to quantify mutual information in simulations, we illustrate the information-gating effects of neural oscillations and oscillatory coherence, which may either increase or decrease the mutual information across the synapse depending on parameters. Finally, we show analytically that our results can quantitatively describe the selection of one information pathway over another when multiple sending neurons project weakly to a single receiving neuron.

  9. CFTR mediates noradrenaline-induced ATP efflux from DRG neurons.

    Science.gov (United States)

    Kanno, Takeshi; Nishizaki, Tomoyuki

    2011-09-24

    In our earlier study, noradrenaline (NA) stimulated ATP release from dorsal root ganglion (DRG) neurons as mediated via β(3) adrenoceptors linked to G(s) protein involving protein kinase A (PKA) activation, to cause allodynia. The present study was conducted to understand how ATP is released from DRG neurons. In an outside-out patch-clamp configuration from acutely dissociated rat DRG neurons, single-channel currents, sensitive to the P2X receptor inhibitor PPADS, were evoked by approaching the patch-electrode tip close to a neuron, indicating that ATP is released from DRG neurons, to activate P2X receptor. NA increased the frequency of the single-channel events, but such NA effect was not found for DRG neurons transfected with the siRNA to silence the cystic fibrosis transmembrane conductance regulator (CFTR) gene. In the immunocytochemical study using acutely dissociated rat DRG cells, CFTR was expressed in neurons alone, but not satellite cells, fibroblasts, or Schwann cells. It is concluded from these results that CFTR mediates NA-induced ATP efflux from DRG neurons as an ATP channel.

  10. Short and Long-Term Attentional Firing Rates Can Be Explained by ST-Neuron Dynamics

    Directory of Open Access Journals (Sweden)

    Oscar J. Avella Gonzalez

    2018-03-01

    Full Text Available Attention modulates neural selectivity and optimizes the allocation of cortical resources during visual tasks. A large number of experimental studies in primates and humans provide ample evidence. As an underlying principle of visual attention, some theoretical models suggested the existence of a gain element that enhances contrast of the attended stimuli. In contrast, the Selective Tuning model of attention (ST proposes an attentional mechanism based on suppression of irrelevant signals. In this paper, we present an updated characterization of the ST-neuron proposed by the Selective Tuning model, and suggest that the inclusion of adaptation currents (Ih to ST-neurons may explain the temporal profiles of the firing rates recorded in single V4 cells during attentional tasks. Furthermore, using the model we show that the interaction between stimulus-selectivity of a neuron and attention shapes the profile of the firing rate, and is enough to explain its fast modulation and other discontinuities observed, when the neuron responds to a sudden switch of stimulus, or when one stimulus is added to another during a visual task.

  11. Control of a dendritic neuron driven by a phase-independent stimulation

    International Nuclear Information System (INIS)

    Fedaravičius, Augustinas Povilas; Cao, Maosen; Ragulskis, Minvydas

    2016-01-01

    A dendritic neuron model exhibits bistability under continuous weak stimulation – the oscillatory synchronized regime and the quiet regime coexist. Complex nonlinear dynamics is observed when the neuron undergoes not only phase-dependent continuous weak stimulation, but also when it is driven by an external phase-independent stimulation. In the latter case basin boundaries between the synchronized and the quiet regime become complex and fractal. Simple strategies based on control pulses are not sufficient in these circumstances, because it becomes difficult to predict the dynamics of the neuron after the application of the control pulse. Therefore, a new neural control method is proposed. Initially, a weak phase control strategy is applied until fractal basin boundaries evolve into a deterministic manifold. Consequently, a single control pulse is immediately applied and the neuron evolves into the calm state.

  12. A mouse model of DEPDC5-related epilepsy: Neuronal loss of Depdc5 causes dysplastic and ectopic neurons, increased mTOR signaling, and seizure susceptibility.

    Science.gov (United States)

    Yuskaitis, Christopher J; Jones, Brandon M; Wolfson, Rachel L; Super, Chloe E; Dhamne, Sameer C; Rotenberg, Alexander; Sabatini, David M; Sahin, Mustafa; Poduri, Annapurna

    2018-03-01

    DEPDC5 is a newly identified epilepsy-related gene implicated in focal epilepsy, brain malformations, and Sudden Unexplained Death in Epilepsy (SUDEP). In vitro, DEPDC5 negatively regulates amino acid sensing by the mTOR complex 1 (mTORC1) pathway, but the role of DEPDC5 in neurodevelopment and epilepsy has not been described. No animal model of DEPDC5-related epilepsy has recapitulated the neurological phenotypes seen in patients, and germline knockout rodent models are embryonic lethal. Here, we establish a neuron-specific Depdc5 conditional knockout mouse by cre-recombination under the Synapsin1 promotor. Depdc5 flox/flox -Syn1 Cre (Depdc5cc+) mice survive to adulthood with a progressive neurologic phenotype that includes motor abnormalities (i.e., hind limb clasping) and reduced survival compared to littermate control mice. Depdc5cc+ mice have larger brains with increased cortical neuron size and dysplastic neurons throughout the cortex, comparable to the abnormal neurons seen in human focal cortical dysplasia specimens. Depdc5 results in constitutive mTORC1 hyperactivation exclusively in neurons as measured by the increased phosphorylation of the downstream ribosomal protein S6. Despite a lack of increased mTORC1 signaling within astrocytes, Depdc5cc+ brains show reactive astrogliosis. We observed two Depdc5cc+ mice to have spontaneous seizures, including a terminal seizure. We demonstrate that as a group Depdc5cc+ mice have lowered seizure thresholds, as evidenced by decreased latency to seizures after chemoconvulsant injection and increased mortality from pentylenetetrazole-induced seizures. In summary, our neuron-specific Depdc5 knockout mouse model recapitulates clinical, pathological, and biochemical features of human DEPDC5-related epilepsy and brain malformations. We thereby present an important model in which to study targeted therapeutic strategies for DEPDC5-related conditions. Copyright © 2017 Elsevier Inc. All rights reserved.

  13. Neuronal medium that supports basic synaptic functions and activity of human neurons in vitro.

    Science.gov (United States)

    Bardy, Cedric; van den Hurk, Mark; Eames, Tameji; Marchand, Cynthia; Hernandez, Ruben V; Kellogg, Mariko; Gorris, Mark; Galet, Ben; Palomares, Vanessa; Brown, Joshua; Bang, Anne G; Mertens, Jerome; Böhnke, Lena; Boyer, Leah; Simon, Suzanne; Gage, Fred H

    2015-05-19

    Human cell reprogramming technologies offer access to live human neurons from patients and provide a new alternative for modeling neurological disorders in vitro. Neural electrical activity is the essence of nervous system function in vivo. Therefore, we examined neuronal activity in media widely used to culture neurons. We found that classic basal media, as well as serum, impair action potential generation and synaptic communication. To overcome this problem, we designed a new neuronal medium (BrainPhys basal + serum-free supplements) in which we adjusted the concentrations of inorganic salts, neuroactive amino acids, and energetic substrates. We then tested that this medium adequately supports neuronal activity and survival of human neurons in culture. Long-term exposure to this physiological medium also improved the proportion of neurons that were synaptically active. The medium was designed to culture human neurons but also proved adequate for rodent neurons. The improvement in BrainPhys basal medium to support neurophysiological activity is an important step toward reducing the gap between brain physiological conditions in vivo and neuronal models in vitro.

  14. Signals and Circuits in the Purkinje Neuron

    Directory of Open Access Journals (Sweden)

    Ze'ev R Abrams

    2011-09-01

    Full Text Available Purkinje neurons in the cerebellum have over 100,000 inputs organized in an orthogonal geometry, and a single output channel. As the sole output of the cerebellar cortex layer, their complex firing pattern has been associated with motor control and learning. As such they have been extensively modeled and measured using tools ranging from electrophysiology and neuroanatomy, to dynamic systems and artificial intelligence methods. However, there is an alternative approach to analyze and describe the neuronal output of these cells using concepts from Electrical Engineering, particularly signal processing and digital/analog circuits. By viewing the Purkinje neuron as an unknown circuit to be reverse-engineered, we can use the tools that provide the foundations of today’s integrated circuits and communication systems to analyze the Purkinje system at the circuit level. We use Fourier transforms to analyze and isolate the inherent frequency modes in the Purkinje neuron and define 3 unique frequency ranges associated with the cells’ output. Comparing the Purkinje neuron to a signal generator that can be externally modulated adds an entire level of complexity to the functional role of these neurons both in terms of data analysis and information processing, relying on Fourier analysis methods in place of statistical ones. We also re-describe some of the recent literature in the field, using the nomenclature of signal processing. Furthermore, by comparing the experimental data of the past decade with basic electronic circuitry, we can resolve the outstanding controversy in the field, by recognizing that the Purkinje neuron can act as a multivibrator circuit.

  15. Neuromorphic Silicon Neuron Circuits

    Science.gov (United States)

    Indiveri, Giacomo; Linares-Barranco, Bernabé; Hamilton, Tara Julia; van Schaik, André; Etienne-Cummings, Ralph; Delbruck, Tobi; Liu, Shih-Chii; Dudek, Piotr; Häfliger, Philipp; Renaud, Sylvie; Schemmel, Johannes; Cauwenberghs, Gert; Arthur, John; Hynna, Kai; Folowosele, Fopefolu; Saighi, Sylvain; Serrano-Gotarredona, Teresa; Wijekoon, Jayawan; Wang, Yingxue; Boahen, Kwabena

    2011-01-01

    Hardware implementations of spiking neurons can be extremely useful for a large variety of applications, ranging from high-speed modeling of large-scale neural systems to real-time behaving systems, to bidirectional brain–machine interfaces. The specific circuit solutions used to implement silicon neurons depend on the application requirements. In this paper we describe the most common building blocks and techniques used to implement these circuits, and present an overview of a wide range of neuromorphic silicon neurons, which implement different computational models, ranging from biophysically realistic and conductance-based Hodgkin–Huxley models to bi-dimensional generalized adaptive integrate and fire models. We compare the different design methodologies used for each silicon neuron design described, and demonstrate their features with experimental results, measured from a wide range of fabricated VLSI chips. PMID:21747754

  16. Neuromorphic silicon neuron circuits

    Directory of Open Access Journals (Sweden)

    Giacomo eIndiveri

    2011-05-01

    Full Text Available Hardware implementations of spiking neurons can be extremely useful for a large variety of applications, ranging from high-speed modeling of large-scale neural systems to real-time behaving systems, to bidirectional brain-machine interfaces. The specific circuit solutions used to implement silicon neurons depend on the application requirements. In this paper we describe the most common building blocks and techniques used to implement these circuits, and present an overview of a wide range of neuromorphic silicon neurons, which implement different computational models, ranging from biophysically realistic and conductance based Hodgkin-Huxley models to bi-dimensional generalized adaptive Integrate and Fire models. We compare the different design methodologies used for each silicon neuron design described, and demonstrate their features with experimental results, measured from a wide range of fabricated VLSI chips.

  17. High-frequency stimulation-induced peptide release synchronizes arcuate kisspeptin neurons and excites GnRH neurons

    Science.gov (United States)

    Qiu, Jian; Nestor, Casey C; Zhang, Chunguang; Padilla, Stephanie L; Palmiter, Richard D

    2016-01-01

    Kisspeptin (Kiss1) and neurokinin B (NKB) neurocircuits are essential for pubertal development and fertility. Kisspeptin neurons in the hypothalamic arcuate nucleus (Kiss1ARH) co-express Kiss1, NKB, dynorphin and glutamate and are postulated to provide an episodic, excitatory drive to gonadotropin-releasing hormone 1 (GnRH) neurons, the synaptic mechanisms of which are unknown. We characterized the cellular basis for synchronized Kiss1ARH neuronal activity using optogenetics, whole-cell electrophysiology, molecular pharmacology and single cell RT-PCR in mice. High-frequency photostimulation of Kiss1ARH neurons evoked local release of excitatory (NKB) and inhibitory (dynorphin) neuropeptides, which were found to synchronize the Kiss1ARH neuronal firing. The light-evoked synchronous activity caused robust excitation of GnRH neurons by a synaptic mechanism that also involved glutamatergic input to preoptic Kiss1 neurons from Kiss1ARH neurons. We propose that Kiss1ARH neurons play a dual role of driving episodic secretion of GnRH through the differential release of peptide and amino acid neurotransmitters to coordinate reproductive function. DOI: http://dx.doi.org/10.7554/eLife.16246.001 PMID:27549338

  18. Complete functional characterization of sensory neurons by system identification.

    Science.gov (United States)

    Wu, Michael C-K; David, Stephen V; Gallant, Jack L

    2006-01-01

    System identification is a growing approach to sensory neurophysiology that facilitates the development of quantitative functional models of sensory processing. This approach provides a clear set of guidelines for combining experimental data with other knowledge about sensory function to obtain a description that optimally predicts the way that neurons process sensory information. This prediction paradigm provides an objective method for evaluating and comparing computational models. In this chapter we review many of the system identification algorithms that have been used in sensory neurophysiology, and we show how they can be viewed as variants of a single statistical inference problem. We then review many of the practical issues that arise when applying these methods to neurophysiological experiments: stimulus selection, behavioral control, model visualization, and validation. Finally we discuss several problems to which system identification has been applied recently, including one important long-term goal of sensory neuroscience: developing models of sensory systems that accurately predict neuronal responses under completely natural conditions.

  19. A mathematical model of communication between groups of circadian neurons in Drosophila melanogaster.

    Science.gov (United States)

    Risau-Gusman, Sebastián; Gleiser, Pablo M

    2014-12-01

    In the fruit fly, circadian behavior is controlled by a small number of specialized neurons, whose molecular clocks are relatively well known. However, much less is known about how these neurons communicate among themselves. In particular, only 1 circadian neuropeptide, pigment-dispersing factor (PDF), has been identified, and most aspects of its interaction with the molecular clock remain to be elucidated. Furthermore, it is speculated that many other peptides should contribute to circadian communication. We have developed a relatively detailed model of the 2 main groups of circadian pacemaker neurons (sLNvs and LNds) to investigate these issues. We have proposed many possible mechanisms for the interaction between the synchronization factors and the molecular clock, and we have compared the outputs with the experimental results reported in the literature both for the wild-type and PDF-null mutant. We have studied how different the properties of each neuron should be to account for the observations reported for the sLNvs in the mutant. We have found that only a few mechanisms, mostly related to the slowing down of nuclear entry of a circadian protein, can synchronize neurons that present these differences. Detailed immunofluorescent recordings have suggested that, whereas in the mutant, LNd neurons are synchronized, in the wild-type, a subset of the LNds oscillate faster than the rest. With our model, we find that a more likely explanation for the same observations is that this subset is being driven outside its synchronization range and displays therefore a complex pattern of oscillation.

  20. Sensory neurons do not induce motor neuron loss in a human stem cell model of spinal muscular atrophy.

    Science.gov (United States)

    Schwab, Andrew J; Ebert, Allison D

    2014-01-01

    Spinal muscular atrophy (SMA) is an autosomal recessive disorder leading to paralysis and early death due to reduced SMN protein. It is unclear why there is such a profound motor neuron loss, but recent evidence from fly and mouse studies indicate that cells comprising the whole sensory-motor circuit may contribute to motor neuron dysfunction and loss. Here, we used induced pluripotent stem cells derived from SMA patients to test whether sensory neurons directly contribute to motor neuron loss. We generated sensory neurons from SMA induced pluripotent stem cells and found no difference in neuron generation or survival, although there was a reduced calcium response to depolarizing stimuli. Using co-culture of SMA induced pluripotent stem cell derived sensory neurons with control induced pluripotent stem cell derived motor neurons, we found no significant reduction in motor neuron number or glutamate transporter boutons on motor neuron cell bodies or neurites. We conclude that SMA sensory neurons do not overtly contribute to motor neuron loss in this human stem cell system.

  1. Identifying cochlear implant channels with poor electrode-neuron interface: partial tripolar, single-channel thresholds and psychophysical tuning curves.

    Science.gov (United States)

    Bierer, Julie Arenberg; Faulkner, Kathleen F

    2010-04-01

    The goal of this study was to evaluate the ability of a threshold measure, made with a restricted electrode configuration, to identify channels exhibiting relatively poor spatial selectivity. With a restricted electrode configuration, channel-to-channel variability in threshold may reflect variations in the interface between the electrodes and auditory neurons (i.e., nerve survival, electrode placement, and tissue impedance). These variations in the electrode-neuron interface should also be reflected in psychophysical tuning curve (PTC) measurements. Specifically, it is hypothesized that high single-channel thresholds obtained with the spatially focused partial tripolar (pTP) electrode configuration are predictive of wide or tip-shifted PTCs. Data were collected from five cochlear implant listeners implanted with the HiRes90k cochlear implant (Advanced Bionics Corp., Sylmar, CA). Single-channel thresholds and most comfortable listening levels were obtained for stimuli that varied in presumed electrical field size by using the pTP configuration for which a fraction of current (sigma) from a center-active electrode returns through two neighboring electrodes and the remainder through a distant indifferent electrode. Forward-masked PTCs were obtained for channels with the highest, lowest, and median tripolar (sigma = 1 or 0.9) thresholds. The probe channel and level were fixed and presented with either the monopolar (sigma = 0) or a more focused pTP (sigma > or = 0.55) configuration. The masker channel and level were varied, whereas the configuration was fixed to sigma = 0.5. A standard, three-interval, two-alternative forced choice procedure was used for thresholds and masked levels. Single-channel threshold and variability in threshold across channels systematically increased as the compensating current, sigma, increased and the presumed electrical field became more focused. Across subjects, channels with the highest single-channel thresholds, when measured with a

  2. Linking macroscopic with microscopic neuroanatomy using synthetic neuronal populations.

    Science.gov (United States)

    Schneider, Calvin J; Cuntz, Hermann; Soltesz, Ivan

    2014-10-01

    Dendritic morphology has been shown to have a dramatic impact on neuronal function. However, population features such as the inherent variability in dendritic morphology between cells belonging to the same neuronal type are often overlooked when studying computation in neural networks. While detailed models for morphology and electrophysiology exist for many types of single neurons, the role of detailed single cell morphology in the population has not been studied quantitatively or computationally. Here we use the structural context of the neural tissue in which dendritic trees exist to drive their generation in silico. We synthesize the entire population of dentate gyrus granule cells, the most numerous cell type in the hippocampus, by growing their dendritic trees within their characteristic dendritic fields bounded by the realistic structural context of (1) the granule cell layer that contains all somata and (2) the molecular layer that contains the dendritic forest. This process enables branching statistics to be linked to larger scale neuroanatomical features. We find large differences in dendritic total length and individual path length measures as a function of location in the dentate gyrus and of somatic depth in the granule cell layer. We also predict the number of unique granule cell dendrites invading a given volume in the molecular layer. This work enables the complete population-level study of morphological properties and provides a framework to develop complex and realistic neural network models.

  3. Induced Pluripotent Stem Cell Models of Progranulin-Deficient Frontotemporal Dementia Uncover Specific Reversible Neuronal Defects

    Science.gov (United States)

    Almeida, Sandra; Zhang, Zhijun; Coppola, Giovanni; Mao, Wenjie; Futai, Kensuke; Karydas, Anna; Geschwind, Michael D.; Tartaglia, M. Carmela; Gao, Fuying; Gianni, Davide; Sena-Esteves, Miguel; Geschwind, Daniel H.; Miller, Bruce L.; Farese, Robert V.; Gao, Fen-Biao

    2012-01-01

    SUMMARY The pathogenic mechanisms of frontotemporal dementia (FTD) remain poorly understood. Here we generated multiple induced pluripotent stem cell (iPSC) lines from a control subject, a patient with sporadic FTD, and an FTD patient with a novel GRN mutation (PGRN S116X). In neurons and microglia differentiated from PGRN S116X iPSCs, the levels of intracellular and secreted progranulin were reduced, establishing patient-specific cellular models of progranulin haploinsufficiency. Through a systematic screen of inducers of cellular stress, we found that PGRN S116X neurons, but not sporadic FTD neurons, exhibited increased sensitivity to staurosporine and other kinase inhibitors. Moreover, the serine/threonine kinase S6K2, a component of the PI3K and MAPK pathways, was specifically downregulated in PGRN S116X neurons. Both increased sensitivity to kinase inhibitors and reduced S6K2 were rescued by progranulin expression. Our findings identify cell-autonomous, reversible defects in patient neurons with progranulin deficiency and provide a new model for studying progranulin-dependent pathogenic mechanisms and testing potential therapies. PMID:23063362

  4. Single-Cell Imaging of Bioenergetic Responses to Neuronal Excitotoxicity and Oxygen and Glucose Deprivation

    OpenAIRE

    Connolly, Niamh M; Düssmann, Heiko; Anilkumar, Ujval; Huber, Heinrich J; Prehn, Jochen HM

    2014-01-01

    Excitotoxicity is a condition occurring during cerebral ischemia, seizures, and chronic neurodegeneration. It is characterized by overactivation of glutamate receptors, leading to excessive Ca2+/Na+ influx into neurons, energetic stress, and subsequent neuronal injury.We and others have previously investigated neuronal populations to study how bioenergetic parameters determine neuronal injury; however, such experiments are often confounded by population-based heterogeneity and the contributio...

  5. A neuronal network model with simplified tonotopicity for tinnitus generation and its relief by sound therapy.

    Science.gov (United States)

    Nagashino, Hirofumi; Kinouchi, Yohsuke; Danesh, Ali A; Pandya, Abhijit S

    2013-01-01

    Tinnitus is the perception of sound in the ears or in the head where no external source is present. Sound therapy is one of the most effective techniques for tinnitus treatment that have been proposed. In order to investigate mechanisms of tinnitus generation and the clinical effects of sound therapy, we have proposed conceptual and computational models with plasticity using a neural oscillator or a neuronal network model. In the present paper, we propose a neuronal network model with simplified tonotopicity of the auditory system as more detailed structure. In this model an integrate-and-fire neuron model is employed and homeostatic plasticity is incorporated. The computer simulation results show that the present model can show the generation of oscillation and its cessation by external input. It suggests that the present framework is promising as a modeling for the tinnitus generation and the effects of sound therapy.

  6. Membrane potential dye imaging of ventromedial hypothalamus neurons from adult mice to study glucose sensing.

    Science.gov (United States)

    Vazirani, Reema P; Fioramonti, Xavier; Routh, Vanessa H

    2013-11-27

    Studies of neuronal activity are often performed using neurons from rodents less than 2 months of age due to the technical difficulties associated with increasing connective tissue and decreased neuronal viability that occur with age. Here, we describe a methodology for the dissociation of healthy hypothalamic neurons from adult-aged mice. The ability to study neurons from adult-aged mice allows the use of disease models that manifest at a later age and might be more developmentally accurate for certain studies. Fluorescence imaging of dissociated neurons can be used to study the activity of a population of neurons, as opposed to using electrophysiology to study a single neuron. This is particularly useful when studying a heterogeneous neuronal population in which the desired neuronal type is rare such as for hypothalamic glucose sensing neurons. We utilized membrane potential dye imaging of adult ventromedial hypothalamic neurons to study their responses to changes in extracellular glucose. Glucose sensing neurons are believed to play a role in central regulation of energy balance. The ability to study glucose sensing in adult rodents is particularly useful since the predominance of diseases related to dysfunctional energy balance (e.g. obesity) increase with age.

  7. Auto- and Crosscorrelograms for the Spike Response of Leaky Integrate-and-Fire Neurons with Slow Synapses

    International Nuclear Information System (INIS)

    Moreno-Bote, Ruben; Parga, Nestor

    2006-01-01

    An analytical description of the response properties of simple but realistic neuron models in the presence of noise is still lacking. We determine completely up to the second order the firing statistics of a single and a pair of leaky integrate-and-fire neurons receiving some common slowly filtered white noise. In particular, the auto- and cross-correlation functions of the output spike trains of pairs of cells are obtained from an improvement of the adiabatic approximation introduced previously by Moreno-Bote and Parga [Phys. Rev. Lett. 92, 028102 (2004)]. These two functions define the firing variability and firing synchronization between neurons, and are of much importance for understanding neuron communication

  8. Intervention of Peiyuan Huayu Decoction on the neuron damage in model rats with acute subdural hematoma

    Directory of Open Access Journals (Sweden)

    Xiao-Xuan Fan

    2017-07-01

    Full Text Available Objective: To study the intervention effect of Peiyuan Huayu Decoction on the neuron damage in model rats with acute subdural hematoma (ASDH. Methods: 160 SD rats were randomly divided into four groups, and the ASDH model rats were made by stereotactic autoblood injection, and sham operation group received craniotomy without blood injection. Sham operation group and model group were normally bred after model establishment, and 6 h after model establishment, the treatment group received intragastric administration of Peiyuan Huayu Decoction, and control group received intragastric administration of Piracetam Tablets, 1 time a day. On the 1d, 3d, 5d and 7d after model establishment, the general conditions of rats (activity, food intake and mental state were observed, blood was collected via auricula dextra, ELISA method was used to determine peripheral plasma NSE and S100毬 protein contents, routine HE staining was conducted after perfusion fixation, the neurons in blood injection side of brain tissue were counted, and the neuron damage was observed. Results: 26 rats were dead in the experiment. The general conditions of sham operation group were significantly better than those of other groups, treatment group was significantly better than model group and control group on the 5d group (P0.05; neuron count of sham operation group was basically stable, treatment group was not different from model group and control group on the 1d (P>0.05, treatment group was better than model group (P0.05 on the 3d, and treatment group was better than model group and control group on the 5d and 7d (P0.05, S100毬 protein and NSE contents decreased significantly on the 3d, and treatment group was significantly different from model group and control group (P<0.05, S100毬 protein and NSE contents increased on the 5d and 7d, the increase in treatment group was slower than that in model group and control group, and there was significant difference (P<0.05. Conclusion

  9. Temporal Information Processing and Stability Analysis of the MHSN Neuron Model in DDF

    Directory of Open Access Journals (Sweden)

    Saket Kumar Choudhary

    2016-12-01

    Full Text Available Implementation of a neuron like information processing structure at hardware level is a burning research problem. In this article, we analyze the modified hybrid spiking neuron model (the MHSN model in distributed delay framework (DDF for hardware level implementation point of view. We investigate its temporal information processing capability in term of inter-spike-interval (ISI distribution. We also perform the stability analysis of the MHSN model, in which, we compute nullclines, steady state solution, eigenvalues corresponding the MHSN model. During phase plane analysis, we notice that the MHSN model generates limit cycle oscillations which is an important phenomenon in many biological processes. Qualitative behavior of these limit cycle does not changes due to the variation in applied input stimulus, however, delay effect the spiking activity and duration of cycle get altered.

  10. Inhibitory neurons modulate spontaneous signaling in cultured cortical neurons: density-dependent regulation of excitatory neuronal signaling

    International Nuclear Information System (INIS)

    Serra, Michael; Guaraldi, Mary; Shea, Thomas B

    2010-01-01

    Cortical neuronal activity depends on a balance between excitatory and inhibitory influences. Culturing of neurons on multi-electrode arrays (MEAs) has provided insight into the development and maintenance of neuronal networks. Herein, we seeded MEAs with murine embryonic cortical/hippocampal neurons at different densities ( 1000 cells mm −2 ) and monitored resultant spontaneous signaling. Sparsely seeded cultures displayed a large number of bipolar, rapid, high-amplitude individual signals with no apparent temporal regularity. By contrast, densely seeded cultures instead displayed clusters of signals at regular intervals. These patterns were observed even within thinner and thicker areas of the same culture. GABAergic neurons (25% of total neurons in our cultures) mediated the differential signal patterns observed above, since addition of the inhibitory antagonist bicuculline to dense cultures and hippocampal slice cultures induced the signal pattern characteristic of sparse cultures. Sparsely seeded cultures likely lacked sufficient inhibitory neurons to modulate excitatory activity. Differential seeding of MEAs can provide a unique model for analyses of pertubation in the interaction between excitatory and inhibitory function during aging and neuropathological conditions where dysregulation of GABAergic neurons is a significant component

  11. Modeling non-syndromic autism and the impact of TRPC6 disruption in human neurons

    Science.gov (United States)

    Griesi-Oliveira, Karina; Acab, Allan; Gupta, Abha R.; Sunaga, Daniele Yumi; Chailangkarn, Thanathom; Nicol, Xavier; Nunez, Yanelli; Walker, Michael F.; Murdoch, John D.; Sanders, Stephan J.; Fernandez, Thomas V.; Ji, Weizhen; Lifton, Richard P.; Vadasz, Estevão; Dietrich, Alexander; Pradhan, Dennis; Song, Hongjun; Ming, Guo-li; Guoe, Xiang; Haddad, Gabriel; Marchetto, Maria C. N.; Spitzer, Nicholas; Passos-Bueno, Maria Rita; State, Matthew W.; Muotri, Alysson R.

    2014-01-01

    An increasing number of genetic variants have been implicated in autism spectrum disorders (ASD), and the functional study of such variants will be critical for the elucidation of autism pathophysiology. Here, we report a de novo balanced translocation disruption of TRPC6, a cation channel, in a non-syndromic autistic individual. Using multiple models, such as dental pulp cells, iPSC-derived neuronal cells and mouse models, we demonstrate that TRPC6 reduction or haploinsufficiency leads to altered neuronal development, morphology, and function. The observed neuronal phenotypes could then be rescued by TRPC6 complementation and by treatment with IGF1 or hyperforin, a TRPC6-specific agonist, suggesting that ASD individuals with alterations in this pathway might benefit from these drugs. We also demonstrate that MeCP2 levels affect TRPC6 expression. Mutations in MeCP2 cause Rett syndrome, revealing common pathways among ASDs. Genetic sequencing of TRPC6 in 1041 ASD individuals and 2872 controls revealed significantly more nonsynonymous mutations in the ASD population, and identified loss-of-function mutations with incomplete penetrance in two patients. Taken together, these findings suggest that TRPC6 is a novel predisposing gene for ASD that may act in a multiple-hit model. This is the first study to use iPSC-derived human neurons to model non-syndromic ASD and illustrate the potential of modeling genetically complex sporadic diseases using such cells. PMID:25385366

  12. Dynamics and Synchronization of Noise Perturbed Ensembles of Periodically Activated neuron Cells

    DEFF Research Database (Denmark)

    Belykh, V. N.; Pankratova, Evgeniya; Mosekilde, Erik

    2008-01-01

    The role of noise for a single neuron and for an ensemble of mutually coupled neurons is investigated. For a single element we show that an increase in noise intensity in the regime of irregular. ring enhances the coherence of the neuronal response. For this regime of spiking a study...... based on the connection graph stability method and through numerical simulation....

  13. Opioid and noradrenergic contributions of tapentadol to the inhibition of locus coeruleus neurons in the streptozotocin rat model of polyneuropathic pain.

    Science.gov (United States)

    Torres-Sanchez, Sonia; Borges, Gisela Da Silva; Mico, Juan A; Berrocoso, Esther

    2018-06-01

    Tapentadol is an analgesic that acts as an agonist of µ opioid receptors (MOR) and that inhibits noradrenaline reuptake. Data from healthy rats show that tapentadol inhibits neuronal activity in the locus coeruleus (LC), a nucleus regulated by both the noradrenergic and opioid systems. Thus, we set out to investigate the effect of tapentadol on LC activity in streptozotocin (STZ)-induced diabetic rats, a model of diabetic polyneuropathy, by analyzing single-unit extracellular recordings of LC neurons. Four weeks after inducing diabetes, tapentadol dose-response curves were obtained from animals pre-treated with RX821002 or naloxone (alpha2-adrenoceptors and opioid receptors antagonists, respectively). In STZ rats, the spontaneous activity of LC neurons (0.9 ± 0.1 Hz) was lower than in naïve animals (1.5 ± 0.1 Hz), and tapentadol's inhibitory effect was also weaker. Alpha2-adrenoceptors blockade by RX821002 (100 μg/kg i.v.) in STZ animals significantly increased the spontaneous activity (from 0.8 ± 0.1 to 1.4 ± 0.2 Hz) and it dampened the inhibition of LC neurons produced by tapentadol. However, opioid receptors blockade following naloxone pre-treatment (5 mg/kg i.v.) did not alter the spontaneous firing rate (0.9 ± 0.2 vs 0.9 ± 0.2 Hz) or the inhibitory effect of tapentadol on LC neurons in STZ animals. Thus, diabetic polyneuropathy appears to exert neuroplastic changes in LC neurotransmission, enhancing the sensitivity of alpha2-adrenoceptors and dampening opioid receptors expression. Tapentadol's activity seems to be predominantly mediated through its noradrenergic effects rather than its influence on opioid receptors in the STZ model of diabetic polyneuropathy. Copyright © 2018 Elsevier Ltd. All rights reserved.

  14. Firing dynamics of an autaptic neuron

    International Nuclear Information System (INIS)

    Wang Heng-Tong; Chen Yong

    2015-01-01

    Autapses are synapses that connect a neuron to itself in the nervous system. Previously, both experimental and theoretical studies have demonstrated that autaptic connections in the nervous system have a significant physiological function. Autapses in nature provide self-delayed feedback, thus introducing an additional timescale to neuronal activities and causing many dynamic behaviors in neurons. Recently, theoretical studies have revealed that an autapse provides a control option for adjusting the response of a neuron: e.g., an autaptic connection can cause the electrical activities of the Hindmarsh–Rose neuron to switch between quiescent, periodic, and chaotic firing patterns; an autapse can enhance or suppress the mode-locking status of a neuron injected with sinusoidal current; and the firing frequency and interspike interval distributions of the response spike train can also be modified by the autapse. In this paper, we review recent studies that showed how an autapse affects the response of a single neuron. (topical review)

  15. Mechanical Dissociation of Retinal Neurons with Vibration

    Science.gov (United States)

    Motomura, Tamami; Hayashida, Yuki; Murayama, Nobuki

    The neuromorphic device, which implements the functions of biological neural circuits by means of VLSI technology, has been collecting much attention in the engineering fields in the last decade. Concurrently, progress in neuroscience research has revealed the nonlinear computation in single neuron levels, suggesting that individual neurons are not merely the circuit elements but computational units. Thus, elucidating the properties of neuronal signal processing is thought to be an essential step for developing the next generation of neuromorphic devices. In the present study, we developed a method for dissociating single neurons from specific sublayers of mammalian retinas with using no proteolytic enzymes but rather combining tissue incubation in a low-Ca2+ medium and the vibro-dissociation technique developed for the slices of brains and spinal cords previously. Our method took shorter time of the procedure, and required less elaborated skill, than the conventional enzymatic method did; nevertheless it yielded enough number of the cells available for acute electrophysiological experiments. The isolated retinal neurons were useful for measuring the nonlinear membrane conductances as well as the spike firing properties under the perforated-patch whole-cell configuration. These neurons also enabled us to examine the effects of proteolytic enzymes on the membrane excitability in those cells.

  16. Neuronal growth on L- and D-cysteine self-assembled monolayers reveals neuronal chiral sensitivity.

    Science.gov (United States)

    Baranes, Koby; Moshe, Hagay; Alon, Noa; Schwartz, Shmulik; Shefi, Orit

    2014-05-21

    Studying the interaction between neuronal cells and chiral molecules is fundamental for the design of novel biomaterials and drugs. Chirality influences all biological processes that involve intermolecular interaction. One common method used to study cellular interactions with different enantiomeric targets is the use of chiral surfaces. Based on previous studies that demonstrated the importance of cysteine in the nervous system, we studied the effect of L- and D-cysteine on single neuronal growth. L-Cysteine, which normally functions as a neuromodulator or a neuroprotective antioxidant, causes damage at elevated levels, which may occur post trauma. In this study, we grew adult neurons in culture enriched with L- and D-cysteine as free compounds or as self-assembled monolayers of chiral surfaces and examined the effect on the neuronal morphology and adhesion. Notably, we have found that exposure to the L-cysteine enantiomer inhibited, and even prevented, neuronal attachment more severely than exposure to the D-cysteine enantiomer. Atop the L-cysteine surfaces, neuronal growth was reduced and degenerated. Since the cysteine molecules were attached to the surface via the thiol groups, the neuronal membrane was exposed to the molecular chiral site. Thus, our results have demonstrated high neuronal chiral sensitivity, revealing chiral surfaces as indirect regulators of neuronal cells and providing a reference for studying chiral drugs.

  17. Tp53 gene mediates distinct dopaminergic neuronal damage in different dopaminergic neurotoxicant models

    Directory of Open Access Journals (Sweden)

    Tao Lu

    2017-01-01

    Full Text Available Tp53, a stress response gene, is involved in diverse cell death pathways and its activation is implicated in the pathogenesis of Parkinson's disease. However, whether the neuronal Tp53 protein plays a direct role in regulating dopaminergic (DA neuronal cell death or neuronal terminal damage in different neurotoxicant models is unknown. In our recent studies, in contrast to the global inhibition of Tp53 function by pharmacological inhibitors and in traditional Tp53 knock-out mice, we examined the effects of DA-specific Tp53 gene deletion after 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine and methamphetamine exposure. Our data suggests that the Tp53 gene might be involved in both neuronal apoptosis and neuronal terminal damage caused by different neurotoxicants. Additional results from other studies also suggest that as a master regulator of many pathways that regulate apoptosis and synaptic terminal damage, it is possible that Tp53 may function as a signaling hub to integrate different signaling pathways to mediate distinctive target pathways. Tp53 protein as a signaling hub might be able to evaluate the microenvironment of neurons, assess the forms and severities of injury incurred, and determine whether apoptotic cell death or neuronal terminal degeneration occurs. Identification of the precise mechanisms activated in distinct neuronal damage caused by different forms and severities of injuries might allow for development of specific Tp53 inhibitors or ways to modulate distinct downstream target pathways involved.

  18. Dynamics of a neuron model in different two-dimensional parameter-spaces

    International Nuclear Information System (INIS)

    Rech, Paulo C.

    2011-01-01

    We report some two-dimensional parameter-space diagrams numerically obtained for the multi-parameter Hindmarsh-Rose neuron model. Several different parameter planes are considered, and we show that regardless of the combination of parameters, a typical scenario is preserved: for all choice of two parameters, the parameter-space presents a comb-shaped chaotic region immersed in a large periodic region. We also show that exist regions close these chaotic region, separated by the comb teeth, organized themselves in period-adding bifurcation cascades. - Research highlights: → We report parameter-spaces obtained for the Hindmarsh-Rose neuron model. → Regardless of the combination of parameters, a typical scenario is preserved. → The scenario presents a comb-shaped chaotic region immersed in a periodic region. → Periodic regions near the chaotic region are in period-adding bifurcation cascades.

  19. Spiking Neurons for Analysis of Patterns

    Science.gov (United States)

    Huntsberger, Terrance

    2008-01-01

    Artificial neural networks comprising spiking neurons of a novel type have been conceived as improved pattern-analysis and pattern-recognition computational systems. These neurons are represented by a mathematical model denoted the state-variable model (SVM), which among other things, exploits a computational parallelism inherent in spiking-neuron geometry. Networks of SVM neurons offer advantages of speed and computational efficiency, relative to traditional artificial neural networks. The SVM also overcomes some of the limitations of prior spiking-neuron models. There are numerous potential pattern-recognition, tracking, and data-reduction (data preprocessing) applications for these SVM neural networks on Earth and in exploration of remote planets. Spiking neurons imitate biological neurons more closely than do the neurons of traditional artificial neural networks. A spiking neuron includes a central cell body (soma) surrounded by a tree-like interconnection network (dendrites). Spiking neurons are so named because they generate trains of output pulses (spikes) in response to inputs received from sensors or from other neurons. They gain their speed advantage over traditional neural networks by using the timing of individual spikes for computation, whereas traditional artificial neurons use averages of activity levels over time. Moreover, spiking neurons use the delays inherent in dendritic processing in order to efficiently encode the information content of incoming signals. Because traditional artificial neurons fail to capture this encoding, they have less processing capability, and so it is necessary to use more gates when implementing traditional artificial neurons in electronic circuitry. Such higher-order functions as dynamic tasking are effected by use of pools (collections) of spiking neurons interconnected by spike-transmitting fibers. The SVM includes adaptive thresholds and submodels of transport of ions (in imitation of such transport in biological

  20. Learning of time series through neuron-to-neuron instruction

    Energy Technology Data Exchange (ETDEWEB)

    Miyazaki, Y [Department of Physics, Kyoto University, Kyoto 606-8502, (Japan); Kinzel, W [Institut fuer Theoretische Physik, Universitaet Wurzburg, 97074 Wurzburg (Germany); Shinomoto, S [Department of Physics, Kyoto University, Kyoto (Japan)

    2003-02-07

    A model neuron with delayline feedback connections can learn a time series generated by another model neuron. It has been known that some student neurons that have completed such learning under the instruction of a teacher's quasi-periodic sequence mimic the teacher's time series over a long interval, even after instruction has ceased. We found that in addition to such faithful students, there are unfaithful students whose time series eventually diverge exponentially from that of the teacher. In order to understand the circumstances that allow for such a variety of students, the orbit dimension was estimated numerically. The quasi-periodic orbits in question were found to be confined in spaces with dimensions significantly smaller than that of the full phase space.

  1. Learning of time series through neuron-to-neuron instruction

    International Nuclear Information System (INIS)

    Miyazaki, Y; Kinzel, W; Shinomoto, S

    2003-01-01

    A model neuron with delayline feedback connections can learn a time series generated by another model neuron. It has been known that some student neurons that have completed such learning under the instruction of a teacher's quasi-periodic sequence mimic the teacher's time series over a long interval, even after instruction has ceased. We found that in addition to such faithful students, there are unfaithful students whose time series eventually diverge exponentially from that of the teacher. In order to understand the circumstances that allow for such a variety of students, the orbit dimension was estimated numerically. The quasi-periodic orbits in question were found to be confined in spaces with dimensions significantly smaller than that of the full phase space

  2. Unaltered Neuronal and Glial Counts in Animal Models of Magnetic Seizure Therapy and Electroconvulsive Therapy

    DEFF Research Database (Denmark)

    Dwork, A.J.; Christensen, J.R.; Larsen, K.B.

    2009-01-01

    report on its anatomical effects. We discerned no histological lesions in the brains of higher mammals subjected to electroconvulsive shock (ECS) or MST, under conditions that model closely those used in humans. We sought to extend these findings by determining whether these interventions affected...... the number of neurons or glia in the frontal cortex or hippocampus. Twenty-four animals received 6 weeks of ECS, MST, or anesthesia alone, 4 days per week. After perfusion fixation, numbers of neurons and glia in frontal cortex and hippocampus were determined by unbiased stereological methods. We found...... no effect of either intervention on volumes or total number or numerical density of neurons or glia in hippocampus, frontal cortex, or subregions of these structures. Induction of seizures in a rigorous model of human ECT and MST therapy does not cause a change in the number of neurons or glia...

  3. A Hebbian learning rule gives rise to mirror neurons and links them to control theoretic inverse models

    Directory of Open Access Journals (Sweden)

    Alexander eHanuschkin

    2013-06-01

    Full Text Available Mirror neurons are neurons whose responses to the observation of a motor act resemble responses measured during production of that act. Computationally, mirror neurons have been viewed as evidence for the existence of internal inverse models. Such models, rooted within control theory, map desired sensory targets onto the motor commands required to generate those targets. To jointly explore both the formation of mirrored responses and their functional contribution to inverse models, we develop a correlation-based theory of interactions between a sensory and a motor area. We show that a simple eligibility-weighted Hebbian learning rule, operating within a sensorimotor loop during motor explorations and stabilized by heterosynaptic competition, naturally gives rise to mirror neurons as well as control theoretic inverse models encoded in the synaptic weights from sensory to motor neurons. Crucially, we find that the correlational structure or stereotypy of the neural code underlying motor explorations determines the nature of the learned inverse model: Random motor codes lead to causal inverses that map sensory activity patterns to their motor causes; such inverses are maximally useful, they allow for imitating arbitrary sensory target sequences. By contrast, stereotyped motor codes lead to less useful predictive inverses that map sensory activity to future motor actions.Our theory generalizes previous work on inverse models by showing that such models can be learned in a simple Hebbian framework without the need for error signals or backpropagation, and it makes new conceptual connections between the causal nature of inverse models, the statistical structure of motor variability, and the time-lag between sensory and motor responses of mirror neurons. Applied to bird song learning, our theory can account for puzzling aspects of the song system, including necessity of sensorimotor gating and selectivity of auditory responses to bird’s own song

  4. A Hebbian learning rule gives rise to mirror neurons and links them to control theoretic inverse models.

    Science.gov (United States)

    Hanuschkin, A; Ganguli, S; Hahnloser, R H R

    2013-01-01

    Mirror neurons are neurons whose responses to the observation of a motor act resemble responses measured during production of that act. Computationally, mirror neurons have been viewed as evidence for the existence of internal inverse models. Such models, rooted within control theory, map-desired sensory targets onto the motor commands required to generate those targets. To jointly explore both the formation of mirrored responses and their functional contribution to inverse models, we develop a correlation-based theory of interactions between a sensory and a motor area. We show that a simple eligibility-weighted Hebbian learning rule, operating within a sensorimotor loop during motor explorations and stabilized by heterosynaptic competition, naturally gives rise to mirror neurons as well as control theoretic inverse models encoded in the synaptic weights from sensory to motor neurons. Crucially, we find that the correlational structure or stereotypy of the neural code underlying motor explorations determines the nature of the learned inverse model: random motor codes lead to causal inverses that map sensory activity patterns to their motor causes; such inverses are maximally useful, by allowing the imitation of arbitrary sensory target sequences. By contrast, stereotyped motor codes lead to less useful predictive inverses that map sensory activity to future motor actions. Our theory generalizes previous work on inverse models by showing that such models can be learned in a simple Hebbian framework without the need for error signals or backpropagation, and it makes new conceptual connections between the causal nature of inverse models, the statistical structure of motor variability, and the time-lag between sensory and motor responses of mirror neurons. Applied to bird song learning, our theory can account for puzzling aspects of the song system, including necessity of sensorimotor gating and selectivity of auditory responses to bird's own song (BOS) stimuli.

  5. Restoring the encoding properties of a stochastic neuron model by an exogenous noise

    Science.gov (United States)

    Paffi, Alessandra; Camera, Francesca; Apollonio, Francesca; d'Inzeo, Guglielmo; Liberti, Micaela

    2015-01-01

    Here we evaluate the possibility of improving the encoding properties of an impaired neuronal system by superimposing an exogenous noise to an external electric stimulation signal. The approach is based on the use of mathematical neuron models consisting of stochastic HH-like circuit, where the impairment of the endogenous presynaptic inputs is described as a subthreshold injected current and the exogenous stimulation signal is a sinusoidal voltage perturbation across the membrane. Our results indicate that a correlated Gaussian noise, added to the sinusoidal signal can significantly increase the encoding properties of the impaired system, through the Stochastic Resonance (SR) phenomenon. These results suggest that an exogenous noise, suitably tailored, could improve the efficacy of those stimulation techniques used in neuronal systems, where the presynaptic sensory neurons are impaired and have to be artificially bypassed. PMID:25999845

  6. Restoring the encoding properties of a stochastic neuron model by an exogenous noise

    Directory of Open Access Journals (Sweden)

    Alessandra ePaffi

    2015-05-01

    Full Text Available Here we evaluate the possibility of improving the encoding properties of an impaired neuronal system by superimposing an exogenous noise to an external electric stimulation signal. The approach is based on the use of mathematical neuron models consisting of stochastic HH-like circuit, where the impairment of the endogenous presynaptic inputs is described as a subthreshold injected current and the exogenous stimulation signal is a sinusoidal voltage perturbation across the membrane. Our results indicate that a correlated Gaussian noise, added to the sinusoidal signal can significantly increase the encoding properties of the impaired system, through the Stochastic Resonance (SR phenomenon. These results suggest that an exogenous noise, suitably tailored, could improve the efficacy of those stimulation techniques used in neuronal systems, where the presynaptic sensory neurons are impaired and have to be artificially bypassed.

  7. Temporal-pattern recognition by single neurons in a sensory pathway devoted to social communication behavior.

    Science.gov (United States)

    Carlson, Bruce A

    2009-07-29

    Sensory systems often encode stimulus information into the temporal pattern of action potential activity. However, little is known about how the information contained within these patterns is extracted by postsynaptic neurons. Similar to temporal coding by sensory neurons, social information in mormyrid fish is encoded into the temporal patterning of an electric organ discharge. In the current study, sensitivity to temporal patterns of electrosensory stimuli was found to arise within the midbrain posterior exterolateral nucleus (ELp). Whole-cell patch recordings from ELp neurons in vivo revealed three patterns of interpulse interval (IPI) tuning: low-pass neurons tuned to long intervals, high-pass neurons tuned to short intervals, and bandpass neurons tuned to intermediate intervals. Many neurons within each class also responded preferentially to either increasing or decreasing IPIs. Playback of electric signaling patterns recorded from freely behaving fish revealed that the IPI and direction tuning of ELp neurons resulted in selective responses to particular social communication displays characterized by distinct IPI patterns. The postsynaptic potential responses of many neurons indicated a combination of excitatory and inhibitory synaptic input, and the IPI tuning of ELp neurons was directly related to rate-dependent changes in the direction and amplitude of postsynaptic potentials. These results suggest that differences in the dynamics of short-term synaptic plasticity in excitatory and inhibitory pathways may tune central sensory neurons to particular temporal patterns of presynaptic activity. This may represent a general mechanism for the processing of behaviorally relevant stimulus information encoded into temporal patterns of activity by sensory neurons.

  8. Neuronal spike-train responses in the presence of threshold noise.

    Science.gov (United States)

    Coombes, S; Thul, R; Laudanski, J; Palmer, A R; Sumner, C J

    2011-03-01

    The variability of neuronal firing has been an intense topic of study for many years. From a modelling perspective it has often been studied in conductance based spiking models with the use of additive or multiplicative noise terms to represent channel fluctuations or the stochastic nature of neurotransmitter release. Here we propose an alternative approach using a simple leaky integrate-and-fire model with a noisy threshold. Initially, we develop a mathematical treatment of the neuronal response to periodic forcing using tools from linear response theory and use this to highlight how a noisy threshold can enhance downstream signal reconstruction. We further develop a more general framework for understanding the responses to large amplitude forcing based on a calculation of first passage times. This is ideally suited to understanding stochastic mode-locking, for which we numerically determine the Arnol'd tongue structure. An examination of data from regularly firing stellate neurons within the ventral cochlear nucleus, responding to sinusoidally amplitude modulated pure tones, shows tongue structures consistent with these predictions and highlights that stochastic, as opposed to deterministic, mode-locking is utilised at the level of the single stellate cell to faithfully encode periodic stimuli.

  9. Memory effects on a resonate-and-fire neuron model subjected to Ornstein-Uhlenbeck noise

    Science.gov (United States)

    Paekivi, S.; Mankin, R.; Rekker, A.

    2017-10-01

    We consider a generalized Langevin equation with an exponentially decaying memory kernel as a model for the firing process of a resonate-and-fire neuron. The effect of temporally correlated random neuronal input is modeled as Ornstein-Uhlenbeck noise. In the noise-induced spiking regime of the neuron, we derive exact analytical formulas for the dependence of some statistical characteristics of the output spike train, such as the probability distribution of the interspike intervals (ISIs) and the survival probability, on the parameters of the input stimulus. Particularly, on the basis of these exact expressions, we have established sufficient conditions for the occurrence of memory-time-induced transitions between unimodal and multimodal structures of the ISI density and a critical damping coefficient which marks a dynamical transition in the behavior of the system.

  10. Capturing spike variability in noisy Izhikevich neurons using point process generalized linear models

    DEFF Research Database (Denmark)

    Østergaard, Jacob; Kramer, Mark A.; Eden, Uri T.

    2018-01-01

    current. We then fit these spike train datawith a statistical model (a generalized linear model, GLM, with multiplicative influences of past spiking). For different levels of noise, we show how the GLM captures both the deterministic features of the Izhikevich neuron and the variability driven...... by the noise. We conclude that the GLM captures essential features of the simulated spike trains, but for near-deterministic spike trains, goodness-of-fit analyses reveal that the model does not fit very well in a statistical sense; the essential random part of the GLM is not captured....... are separately applied; understanding the relationships between these modeling approaches remains an area of active research. In this letter, we examine this relationship using simulation. To do so, we first generate spike train data from a well-known dynamical model, the Izhikevich neuron, with a noisy input...

  11. Protective effect of zinc against ischemic neuronal injury in a middle cerebral artery occlusion model.

    Science.gov (United States)

    Kitamura, Youji; Iida, Yasuhiko; Abe, Jun; Ueda, Masashi; Mifune, Masaki; Kasuya, Fumiyo; Ohta, Masayuki; Igarashi, Kazuo; Saito, Yutaka; Saji, Hideo

    2006-02-01

    In this study, we investigated the effect of vesicular zinc on ischemic neuronal injury. In cultured neurons, addition of a low concentration (under 100 microM) of zinc inhibited both glutamate-induced calcium influx and neuronal death. In contrast, a higher concentration (over 150 microM) of zinc decreased neuronal viability, although calcium influx was inhibited. These results indicate that zinc exhibits biphasic effects depending on its concentration. Furthermore, in cultured neurons, co-addition of glutamate and CaEDTA, which binds extra-cellular zinc, increased glutamate-induced calcium influx and aggravated the neurotoxicity of glutamate. In a rat transient middle cerebral artery occlusion (MCAO) model, the infarction volume, which is related to the neurotoxicity of glutamate, increased rapidly on the intracerebral ventricular injection of CaEDTA 30 min prior to occlusion. These results suggest that zinc released from synaptic vesicles may provide a protective effect against ischemic neuronal injury.

  12. Nanotopography induced contact guidance of the F11 cell line during neuronal differentiation: a neuronal model cell line for tissue scaffold development

    International Nuclear Information System (INIS)

    Wieringa, Paul; Micera, Silvestro; Tonazzini, Ilaria; Cecchini, Marco

    2012-01-01

    The F11 hybridoma, a dorsal root ganglion-derived cell line, was used to investigate the response of nociceptive sensory neurons to nanotopographical guidance cues. This established this cell line as a model of peripheral sensory neuron growth for tissue scaffold design. Cells were seeded on substrates of cyclic olefin copolymer (COC) films imprinted via nanoimprint lithography (NIL) with a grating pattern of nano-scale grooves and ridges. Different ridge widths were employed to alter the focal adhesion formation, thereby changing the cell/substrate interaction. Differentiation was stimulated with forskolin in culture medium consisting of either 1 or 10% fetal bovine serum (FBS). Per medium condition, similar neurite alignment was achieved over the four day period, with the 1% serum condition exhibiting longer, more aligned neurites. Immunostaining for focal adhesions found the 1% FBS condition to also have fewer, less developed focal adhesions. The robust response of the F11 to guidance cues further builds on the utility of this cell line as a sensory neuron model, representing a useful tool to explore the design of regenerative guidance tissue scaffolds. (paper)

  13. One rule to grow them all: a general theory of neuronal branching and its practical application.

    Directory of Open Access Journals (Sweden)

    Hermann Cuntz

    2010-08-01

    Full Text Available Understanding the principles governing axonal and dendritic branching is essential for unravelling the functionality of single neurons and the way in which they connect. Nevertheless, no formalism has yet been described which can capture the general features of neuronal branching. Here we propose such a formalism, which is derived from the expression of dendritic arborizations as locally optimized graphs. Inspired by Ramón y Cajal's laws of conservation of cytoplasm and conduction time in neural circuitry, we show that this graphical representation can be used to optimize these variables. This approach allows us to generate synthetic branching geometries which replicate morphological features of any tested neuron. The essential structure of a neuronal tree is thereby captured by the density profile of its spanning field and by a single parameter, a balancing factor weighing the costs for material and conduction time. This balancing factor determines a neuron's electrotonic compartmentalization. Additions to this rule, when required in the construction process, can be directly attributed to developmental processes or a neuron's computational role within its neural circuit. The simulations presented here are implemented in an open-source software package, the "TREES toolbox," which provides a general set of tools for analyzing, manipulating, and generating dendritic structure, including a tool to create synthetic members of any particular cell group and an approach for a model-based supervised automatic morphological reconstruction from fluorescent image stacks. These approaches provide new insights into the constraints governing dendritic architectures. They also provide a novel framework for modelling and analyzing neuronal branching structures and for constructing realistic synthetic neural networks.

  14. Induced Pluripotent Stem Cell Models of Progranulin-Deficient Frontotemporal Dementia Uncover Specific Reversible Neuronal Defects

    Directory of Open Access Journals (Sweden)

    Sandra Almeida

    2012-10-01

    Full Text Available The pathogenic mechanisms of frontotemporal dementia (FTD remain poorly understood. Here we generated multiple induced pluripotent stem cell lines from a control subject, a patient with sporadic FTD, and an FTD patient with a novel heterozygous GRN mutation (progranulin [PGRN] S116X. In neurons and microglia differentiated from PGRN S116X induced pluripotent stem cells, the levels of intracellular and secreted PGRN were reduced, establishing patient-specific cellular models of PGRN haploinsufficiency. Through a systematic screen of inducers of cellular stress, we found that PGRN S116X neurons, but not sporadic FTD neurons, exhibited increased sensitivity to staurosporine and other kinase inhibitors. Moreover, the serine/threonine kinase S6K2, a component of the phosphatidylinositol 3-kinase and mitogen-activated protein kinase pathways, was specifically downregulated in PGRN S116X neurons. Both increased sensitivity to kinase inhibitors and reduced S6K2 were rescued by PGRN expression. Our findings identify cell-autonomous, reversible defects in patient neurons with PGRN deficiency, and provide a compelling model for studying PGRN-dependent pathogenic mechanisms and testing potential therapies.

  15. Mechanisms for multiple activity modes of VTA dopamine neurons

    Directory of Open Access Journals (Sweden)

    Andrew eOster

    2015-07-01

    Full Text Available Midbrain ventral segmental area (VTA dopaminergic neurons send numerous projections to cortical and sub-cortical areas, and diffusely release dopamine (DA to their targets. DA neurons display a range of activity modes that vary in frequency and degree of burst firing. Importantly, DA neuronal bursting is associated with a significantly greater degree of DA release than an equivalent tonic activity pattern. Here, we introduce a single compartmental, conductance-based computational model for DA cell activity that captures the behavior of DA neuronal dynamics and examine the multiple factors that underlie DA firing modes: the strength of the SK conductance, the amount of drive, and GABA inhibition. Our results suggest that neurons with low SK conductance fire in a fast firing mode, are correlated with burst firing, and require higher levels of applied current before undergoing depolarization block. We go on to consider the role of GABAergic inhibition on an ensemble of dynamical classes of DA neurons and find that strong GABA inhibition suppresses burst firing. Our studies suggest differences in the distribution of the SK conductance and GABA inhibition levels may indicate subclasses of DA neurons within the VTA. We further identify, that by considering alternate potassium dynamics, the dynamics display burst patterns that terminate via depolarization block, akin to those observed in vivo in VTA DA neurons and in substantia nigra pars compacta DA cell preparations under apamin application. In addition, we consider the generation of transient burst firing events that are NMDA-initiated or elicited by a sudden decrease of GABA inhibition, that is, disinhibition.

  16. Neuroprotective effects of a novel single compound 1-methoxyoctadecan-1-ol isolated from Uncaria sinensis in primary cortical neurons and a photothrombotic ischemia model.

    Directory of Open Access Journals (Sweden)

    Ji Yeon Jang

    Full Text Available We identified a novel neuroprotective compound, 1-methoxyoctadecan-1-ol, from Uncaria sinensis (Oliv. Havil and investigated its effects and mechanisms in primary cortical neurons and in a photothrombotic ischemic model. In primary rat cortical neurons against glutamate-induced neurotoxicity, pretreatment with 1-methoxyoctadecan-1-ol resulted in significantly reduced neuronal death in a dose-dependent manner. In addition, treatment with 1-methoxyoctadecan-1-ol resulted in decreased neuronal apoptotic death, as assessed by nuclear morphological approaches. To clarify the neuroprotective mechanism of 1-methoxyoctadecan-1-ol, we explored the downstream signaling pathways of N-methyl-D-aspartate receptor (NMDAR with calpain activation. Treatment with glutamate leads to early activation of NMDAR, which in turn leads to calpain-mediated cleavage of striatal-enriched protein tyrosine phosphatase (STEP and subsequent activation of p38 mitogen activated protein kinase (MAPK. However, pretreatment with 1-methoxyoctadecan-1-ol resulted in significantly attenuated activation of GluN2B-NMDAR and a decrease in calpain-mediated STEP cleavage, leading to subsequent attenuation of p38 MAPK activation. We confirmed the critical role of p38 MAPK in neuroprotective effects of 1-methoxyoctadecan-1-ol using specific inhibitor SB203580. In the photothrombotic ischemic injury in mice, treatment with 1-methoxyoctadecan-1-ol resulted in significantly reduced infarct volume, edema size, and improved neurological function. 1-methoxyoctadecan-1-ol effectively prevents cerebral ischemic damage through down-regulation of calpain-mediated STEP cleavage and activation of p38 MAPK. These results suggest that 1-methoxyoctadecan-1-ol showed neuroprotective effects through down-regulation of calpain-mediated STEP cleavage with activation of GluN2B-NMDAR, and subsequent alleviation of p38 MAPK activation. In addition, 1-methoxyoctadecan-1-ol might be a useful therapeutic agent for

  17. Predictive models of glucose control: roles for glucose-sensing neurones

    Science.gov (United States)

    Kosse, C.; Gonzalez, A.; Burdakov, D.

    2018-01-01

    The brain can be viewed as a sophisticated control module for stabilizing blood glucose. A review of classical behavioural evidence indicates that central circuits add predictive (feedforward/anticipatory) control to the reactive (feedback/compensatory) control by peripheral organs. The brain/cephalic control is constructed and engaged, via associative learning, by sensory cues predicting energy intake or expenditure (e.g. sight, smell, taste, sound). This allows rapidly measurable sensory information (rather than slowly generated internal feedback signals, e.g. digested nutrients) to control food selection, glucose supply for fight-or-flight responses or preparedness for digestion/absorption. Predictive control is therefore useful for preventing large glucose fluctuations. We review emerging roles in predictive control of two classes of widely projecting hypothalamic neurones, orexin/hypocretin (ORX) and melanin-concentrating hormone (MCH) cells. Evidence is cited that ORX neurones (i) are activated by sensory cues (e.g. taste, sound), (ii) drive hepatic production, and muscle uptake, of glucose, via sympathetic nerves, (iii) stimulate wakefulness and exploration via global brain projections and (iv) are glucose-inhibited. MCH neurones are (i) glucose-excited, (ii) innervate learning and reward centres to promote synaptic plasticity, learning and memory and (iii) are critical for learning associations useful for predictive control (e.g. using taste to predict nutrient value of food). This evidence is unified into a model for predictive glucose control. During associative learning, inputs from some glucose-excited neurones may promote connections between the ‘fast’ senses and reward circuits, constructing neural shortcuts for efficient action selection. In turn, glucose-inhibited neurones may engage locomotion/exploration and coordinate the required fuel supply. Feedback inhibition of the latter neurones by glucose would ensure that glucose fluxes they

  18. Predictive models of glucose control: roles for glucose-sensing neurones.

    Science.gov (United States)

    Kosse, C; Gonzalez, A; Burdakov, D

    2015-01-01

    The brain can be viewed as a sophisticated control module for stabilizing blood glucose. A review of classical behavioural evidence indicates that central circuits add predictive (feedforward/anticipatory) control to the reactive (feedback/compensatory) control by peripheral organs. The brain/cephalic control is constructed and engaged, via associative learning, by sensory cues predicting energy intake or expenditure (e.g. sight, smell, taste, sound). This allows rapidly measurable sensory information (rather than slowly generated internal feedback signals, e.g. digested nutrients) to control food selection, glucose supply for fight-or-flight responses or preparedness for digestion/absorption. Predictive control is therefore useful for preventing large glucose fluctuations. We review emerging roles in predictive control of two classes of widely projecting hypothalamic neurones, orexin/hypocretin (ORX) and melanin-concentrating hormone (MCH) cells. Evidence is cited that ORX neurones (i) are activated by sensory cues (e.g. taste, sound), (ii) drive hepatic production, and muscle uptake, of glucose, via sympathetic nerves, (iii) stimulate wakefulness and exploration via global brain projections and (iv) are glucose-inhibited. MCH neurones are (i) glucose-excited, (ii) innervate learning and reward centres to promote synaptic plasticity, learning and memory and (iii) are critical for learning associations useful for predictive control (e.g. using taste to predict nutrient value of food). This evidence is unified into a model for predictive glucose control. During associative learning, inputs from some glucose-excited neurones may promote connections between the 'fast' senses and reward circuits, constructing neural shortcuts for efficient action selection. In turn, glucose-inhibited neurones may engage locomotion/exploration and coordinate the required fuel supply. Feedback inhibition of the latter neurones by glucose would ensure that glucose fluxes they stimulate

  19. The transfer function of neuron spike.

    Science.gov (United States)

    Palmieri, Igor; Monteiro, Luiz H A; Miranda, Maria D

    2015-08-01

    The mathematical modeling of neuronal signals is a relevant problem in neuroscience. The complexity of the neuron behavior, however, makes this problem a particularly difficult task. Here, we propose a discrete-time linear time-invariant (LTI) model with a rational function in order to represent the neuronal spike detected by an electrode located in the surroundings of the nerve cell. The model is presented as a cascade association of two subsystems: one that generates an action potential from an input stimulus, and one that represents the medium between the cell and the electrode. The suggested approach employs system identification and signal processing concepts, and is dissociated from any considerations about the biophysical processes of the neuronal cell, providing a low-complexity alternative to model the neuronal spike. The model is validated by using in vivo experimental readings of intracellular and extracellular signals. A computational simulation of the model is presented in order to assess its proximity to the neuronal signal and to observe the variability of the estimated parameters. The implications of the results are discussed in the context of spike sorting. Copyright © 2015 Elsevier Ltd. All rights reserved.

  20. Neurons the decision makers, Part I: The firing function of a single neuron.

    Science.gov (United States)

    Saaty, Thomas

    2017-02-01

    This paper is concerned with understanding synthesis of electric signals in the neural system based on making pairwise comparisons. Fundamentally, every person and every animal are born with the talent to compare stimuli from things that share properties in space or over time. Comparisons always need experience to distinguish among things. Pairwise comparisons are numerically reciprocal. If a value is assigned to the larger of two elements that have a given property when compared with the smaller one, then the smaller has the reciprocal of that value when compared with the larger. Because making comparisons requires the reciprocal property, we need mathematics that can cope with division. There are four division algebras that would allow us to use our reciprocals arising from comparisons: The real numbers, the complex numbers, the non-commutative quaternions and the non-associative octonions. Rather than inferring function as from electric flow in a network, in this paper we infer the flow from function. Neurons fire in response to stimuli and their firings vary relative to the intensities of the stimuli. We believe neurons use some kind of pairwise comparison mechanism to determine when to fire based on the stimuli they receive. The ideas we develop here about flows are used to deduce how a system based on this kind of firing determination works and can be described. Furthermore the firing of neurons requires continuous comparisons. To develop a formula describing the output of these pairwise comparisons requires solving Fredholm's equation of the second kind which is satisfied if and only if a simple functional equation has solutions. The Fourier transform of the real solution of this equation leads to inverse square laws like those that are common in physics. The Fourier transform applied to a complex valued solution leads to Dirac type of firings. Such firings are dense in the very general fields of functions known as Sobolev spaces and thus can be used to

  1. Novel human neuronal tau model exhibiting neurofibrillary tangles and transcellular propagation.

    Science.gov (United States)

    Reilly, Patrick; Winston, Charisse N; Baron, Kelsey R; Trejo, Margarita; Rockenstein, Edward M; Akers, Johnny C; Kfoury, Najla; Diamond, Marc; Masliah, Eliezer; Rissman, Robert A; Yuan, Shauna H

    2017-10-01

    Tauopathies are a class of neurodegenerative diseases, including Alzheimer's disease, frontotemporal dementia and progressive supranuclear palsy, which are associated with the pathological aggregation of tau protein into neurofibrillary tangles (NFT). Studies have characterized tau as a "prion-like" protein given its ability to form distinct, stable amyloid conformations capable of transcellular and multigenerational propagation in clonal fashion. It has been proposed that progression of tauopathy could be due to the prion-like propagation of tau, suggesting the possibility that end-stage pathologies, like NFT formation, may require an instigating event such as tau seeding. To investigate this, we applied a novel human induced pluripotent stem cell (hiPSC) system we have developed to serve as a human neuronal model. We introduced the tau repeat domain (tau-RD) with P301L and V337M (tau-RD-LM) mutations into hiPSC-derived neurons and observed expression of tau-RD at levels similar to total tau in postmortem AD brains. Tau aggregation occurred without the addition of recombinant tau fibrils. The conditioned media from tau-RD cultures contained tau-RD seeds, which were capable of inducing aggregate formation in homotypic mode in non-transduced recipient neuronal cultures. The resultant NFTs were thioflavin-positive, silver stain-positive, and assumed fibrillary appearance on transmission electron microscopy (TEM) with immunogold, which revealed paired helical filament 1 (PHF1)-positive NFTs, representing possible recruitment of endogenous tau in the aggregates. Functionally, expression of tau-RD caused neurotoxicity that manifested as axon retraction, synaptic density reduction, and enlargement of lysosomes. The results of our hiPSC study were reinforced by the observation that Tau-RD-LM is excreted in exosomes, which mediated the transfer of human tau to wild-type mouse neurons in vivo. Our hiPSC human neuronal system provides a model for further studies of tau

  2. A new glucocerebrosidase-deficient neuronal cell model provides a tool to probe pathophysiology and therapeutics for Gaucher disease.

    Science.gov (United States)

    Westbroek, Wendy; Nguyen, Matthew; Siebert, Marina; Lindstrom, Taylor; Burnett, Robert A; Aflaki, Elma; Jung, Olive; Tamargo, Rafael; Rodriguez-Gil, Jorge L; Acosta, Walter; Hendrix, An; Behre, Bahafta; Tayebi, Nahid; Fujiwara, Hideji; Sidhu, Rohini; Renvoise, Benoit; Ginns, Edward I; Dutra, Amalia; Pak, Evgenia; Cramer, Carole; Ory, Daniel S; Pavan, William J; Sidransky, Ellen

    2016-07-01

    Glucocerebrosidase is a lysosomal hydrolase involved in the breakdown of glucosylceramide. Gaucher disease, a recessive lysosomal storage disorder, is caused by mutations in the gene GBA1 Dysfunctional glucocerebrosidase leads to accumulation of glucosylceramide and glycosylsphingosine in various cell types and organs. Mutations in GBA1 are also a common genetic risk factor for Parkinson disease and related synucleinopathies. In recent years, research on the pathophysiology of Gaucher disease, the molecular link between Gaucher and Parkinson disease, and novel therapeutics, have accelerated the need for relevant cell models with GBA1 mutations. Although induced pluripotent stem cells, primary rodent neurons, and transfected neuroblastoma cell lines have been used to study the effect of glucocerebrosidase deficiency on neuronal function, these models have limitations because of challenges in culturing and propagating the cells, low yield, and the introduction of exogenous mutant GBA1 To address some of these difficulties, we established a high yield, easy-to-culture mouse neuronal cell model with nearly complete glucocerebrosidase deficiency representative of Gaucher disease. We successfully immortalized cortical neurons from embryonic null allele gba(-/-) mice and the control littermate (gba(+/+)) by infecting differentiated primary cortical neurons in culture with an EF1α-SV40T lentivirus. Immortalized gba(-/-) neurons lack glucocerebrosidase protein and enzyme activity, and exhibit a dramatic increase in glucosylceramide and glucosylsphingosine accumulation, enlarged lysosomes, and an impaired ATP-dependent calcium-influx response; these phenotypical characteristics were absent in gba(+/+) neurons. This null allele gba(-/-) mouse neuronal model provides a much-needed tool to study the pathophysiology of Gaucher disease and to evaluate new therapies. © 2016. Published by The Company of Biologists Ltd.

  3. A new glucocerebrosidase-deficient neuronal cell model provides a tool to probe pathophysiology and therapeutics for Gaucher disease

    Directory of Open Access Journals (Sweden)

    Wendy Westbroek

    2016-07-01

    Full Text Available Glucocerebrosidase is a lysosomal hydrolase involved in the breakdown of glucosylceramide. Gaucher disease, a recessive lysosomal storage disorder, is caused by mutations in the gene GBA1. Dysfunctional glucocerebrosidase leads to accumulation of glucosylceramide and glycosylsphingosine in various cell types and organs. Mutations in GBA1 are also a common genetic risk factor for Parkinson disease and related synucleinopathies. In recent years, research on the pathophysiology of Gaucher disease, the molecular link between Gaucher and Parkinson disease, and novel therapeutics, have accelerated the need for relevant cell models with GBA1 mutations. Although induced pluripotent stem cells, primary rodent neurons, and transfected neuroblastoma cell lines have been used to study the effect of glucocerebrosidase deficiency on neuronal function, these models have limitations because of challenges in culturing and propagating the cells, low yield, and the introduction of exogenous mutant GBA1. To address some of these difficulties, we established a high yield, easy-to-culture mouse neuronal cell model with nearly complete glucocerebrosidase deficiency representative of Gaucher disease. We successfully immortalized cortical neurons from embryonic null allele gba−/− mice and the control littermate (gba+/+ by infecting differentiated primary cortical neurons in culture with an EF1α-SV40T lentivirus. Immortalized gba−/− neurons lack glucocerebrosidase protein and enzyme activity, and exhibit a dramatic increase in glucosylceramide and glucosylsphingosine accumulation, enlarged lysosomes, and an impaired ATP-dependent calcium-influx response; these phenotypical characteristics were absent in gba+/+ neurons. This null allele gba−/− mouse neuronal model provides a much-needed tool to study the pathophysiology of Gaucher disease and to evaluate new therapies.

  4. A latent low-dimensional common input drives a pool of motor neurons: a probabilistic latent state-space model.

    Science.gov (United States)

    Feeney, Daniel F; Meyer, François G; Noone, Nicholas; Enoka, Roger M

    2017-10-01

    Motor neurons appear to be activated with a common input signal that modulates the discharge activity of all neurons in the motor nucleus. It has proven difficult for neurophysiologists to quantify the variability in a common input signal, but characterization of such a signal may improve our understanding of how the activation signal varies across motor tasks. Contemporary methods of quantifying the common input to motor neurons rely on compiling discrete action potentials into continuous time series, assuming the motor pool acts as a linear filter, and requiring signals to be of sufficient duration for frequency analysis. We introduce a space-state model in which the discharge activity of motor neurons is modeled as inhomogeneous Poisson processes and propose a method to quantify an abstract latent trajectory that represents the common input received by motor neurons. The approach also approximates the variation in synaptic noise in the common input signal. The model is validated with four data sets: a simulation of 120 motor units, a pair of integrate-and-fire neurons with a Renshaw cell providing inhibitory feedback, the discharge activity of 10 integrate-and-fire neurons, and the discharge times of concurrently active motor units during an isometric voluntary contraction. The simulations revealed that a latent state-space model is able to quantify the trajectory and variability of the common input signal across all four conditions. When compared with the cumulative spike train method of characterizing common input, the state-space approach was more sensitive to the details of the common input current and was less influenced by the duration of the signal. The state-space approach appears to be capable of detecting rather modest changes in common input signals across conditions. NEW & NOTEWORTHY We propose a state-space model that explicitly delineates a common input signal sent to motor neurons and the physiological noise inherent in synaptic signal

  5. A Computational Model of the SC Multisensory Neurons: Integrative Capabilities, Maturation, and Plasticity

    Directory of Open Access Journals (Sweden)

    Cristiano Cuppini

    2011-10-01

    Full Text Available Different cortical and subcortical structures present neurons able to integrate stimuli of different sensory modalities. Among the others, one of the most investigated integrative regions is the Superior Colliculus (SC, a midbrain structure whose aim is to guide attentive behaviour and motor responses toward external events. Despite the large amount of experimental data in the literature, the neural mechanisms underlying the SC response are not completely understood. Moreover, recent data indicate that multisensory integration ability is the result of maturation after birth, depending on sensory experience. Mathematical models and computer simulations can be of value to investigate and clarify these phenomena. In the last few years, several models have been implemented to shed light on these mechanisms and to gain a deeper comprehension of the SC capabilities. Here, a neural network model (Cuppini et al., 2010 is extensively discussed. The model considers visual-auditory interaction, and is able to reproduce and explain the main physiological features of multisensory integration in SC neurons, and their acquisition during postnatal life. To reproduce a neonatal condition, the model assumes that during early life: 1 cortical-SC synapses are present but not active; 2 in this phase, responses are driven by non-cortical inputs with very large receptive fields (RFs and little spatial tuning; 3 a slight spatial preference for the visual inputs is present. Sensory experience is modeled by a “training phase” in which the network is repeatedly exposed to modality-specific and cross-modal stimuli at different locations. As results, Cortical-SC synapses are crafted during this period thanks to the Hebbian rules of potentiation and depression, RFs are reduced in size, and neurons exhibit integrative capabilities to cross-modal stimuli, such as multisensory enhancement, inverse effectiveness, and multisensory depression. The utility of the modelling

  6. Reward inference by primate prefrontal and striatal neurons.

    Science.gov (United States)

    Pan, Xiaochuan; Fan, Hongwei; Sawa, Kosuke; Tsuda, Ichiro; Tsukada, Minoru; Sakagami, Masamichi

    2014-01-22

    The brain contains multiple yet distinct systems involved in reward prediction. To understand the nature of these processes, we recorded single-unit activity from the lateral prefrontal cortex (LPFC) and the striatum in monkeys performing a reward inference task using an asymmetric reward schedule. We found that neurons both in the LPFC and in the striatum predicted reward values for stimuli that had been previously well experienced with set reward quantities in the asymmetric reward task. Importantly, these LPFC neurons could predict the reward value of a stimulus using transitive inference even when the monkeys had not yet learned the stimulus-reward association directly; whereas these striatal neurons did not show such an ability. Nevertheless, because there were two set amounts of reward (large and small), the selected striatal neurons were able to exclusively infer the reward value (e.g., large) of one novel stimulus from a pair after directly experiencing the alternative stimulus with the other reward value (e.g., small). Our results suggest that although neurons that predict reward value for old stimuli in the LPFC could also do so for new stimuli via transitive inference, those in the striatum could only predict reward for new stimuli via exclusive inference. Moreover, the striatum showed more complex functions than was surmised previously for model-free learning.

  7. Fast calcium transients translate the distribution and conduction of neural activity in different regions of a single sensory neuron.

    Science.gov (United States)

    Purali, Nuhan

    2017-09-01

    In the present study, cytosolic calcium concentration changes were recorded in response to various forms of excitations, using the fluorescent calcium indicator dye OG-BAPTA1 together with the current or voltage clamp methods in stretch receptor neurons of crayfish. A single action potential evoked a rise in the resting calcium level in the axon and axonal hillock, whereas an impulse train or a large saturating current injection would be required to evoke an equivalent response in the dendrite region. Under voltage clamp conditions, amplitude differences between axon and dendrite responses vanished completely. The fast activation time and the modulation of the response by extracellular calcium concentration changes indicated that the evoked calcium transients might be mediated by calcium entry into the cytosol through a voltage-gated calcium channel. The decay of the responses was slow and sensitive to extracellular sodium and calcium concentrations as well as exposure to 1-10 mM NiCl 2 and 10-500 µM lanthanum. Thus, a sodium calcium exchanger and a calcium ATPase might be responsible for calcium extrusion from the cytosol. Present results indicate that the calcium indicator OG-BAPTA1 might be an efficient but indirect way of monitoring regional membrane potential differences in a single neuron.

  8. Interfacing 3D Engineered Neuronal Cultures to Micro-Electrode Arrays: An Innovative In Vitro Experimental Model

    NARCIS (Netherlands)

    Tedesco, M.; Frega, M.; Martinoia, S.; Pesce, M.; Massobrio, P.

    2015-01-01

    Currently, large-scale networks derived from dissociated neurons growing and developing in vitro on extracellular micro-transducer devices are the gold-standard experimental model to study basic neurophysiological mechanisms involved in the formation and maintenance of neuronal cell assemblies.

  9. Spike propagation through the dorsal root ganglia in an unmyelinated sensory neuron: a modeling study.

    Science.gov (United States)

    Sundt, Danielle; Gamper, Nikita; Jaffe, David B

    2015-12-01

    Unmyelinated C-fibers are a major type of sensory neurons conveying pain information. Action potential conduction is regulated by the bifurcation (T-junction) of sensory neuron axons within the dorsal root ganglia (DRG). Understanding how C-fiber signaling is influenced by the morphology of the T-junction and the local expression of ion channels is important for understanding pain signaling. In this study we used biophysical computer modeling to investigate the influence of axon morphology within the DRG and various membrane conductances on the reliability of spike propagation. As expected, calculated input impedance and the amplitude of propagating action potentials were both lowest at the T-junction. Propagation reliability for single spikes was highly sensitive to the diameter of the stem axon and the density of voltage-gated Na(+) channels. A model containing only fast voltage-gated Na(+) and delayed-rectifier K(+) channels conducted trains of spikes up to frequencies of 110 Hz. The addition of slowly activating KCNQ channels (i.e., KV7 or M-channels) to the model reduced the following frequency to 30 Hz. Hyperpolarization produced by addition of a much slower conductance, such as a Ca(2+)-dependent K(+) current, was needed to reduce the following frequency to 6 Hz. Attenuation of driving force due to ion accumulation or hyperpolarization produced by a Na(+)-K(+) pump had no effect on following frequency but could influence the reliability of spike propagation mutually with the voltage shift generated by a Ca(2+)-dependent K(+) current. These simulations suggest how specific ion channels within the DRG may contribute toward therapeutic treatments for chronic pain. Copyright © 2015 the American Physiological Society.

  10. The dynamic brain: from spiking neurons to neural masses and cortical fields.

    Directory of Open Access Journals (Sweden)

    Gustavo Deco

    2008-08-01

    Full Text Available The cortex is a complex system, characterized by its dynamics and architecture, which underlie many functions such as action, perception, learning, language, and cognition. Its structural architecture has been studied for more than a hundred years; however, its dynamics have been addressed much less thoroughly. In this paper, we review and integrate, in a unifying framework, a variety of computational approaches that have been used to characterize the dynamics of the cortex, as evidenced at different levels of measurement. Computational models at different space-time scales help us understand the fundamental mechanisms that underpin neural processes and relate these processes to neuroscience data. Modeling at the single neuron level is necessary because this is the level at which information is exchanged between the computing elements of the brain; the neurons. Mesoscopic models tell us how neural elements interact to yield emergent behavior at the level of microcolumns and cortical columns. Macroscopic models can inform us about whole brain dynamics and interactions between large-scale neural systems such as cortical regions, the thalamus, and brain stem. Each level of description relates uniquely to neuroscience data, from single-unit recordings, through local field potentials to functional magnetic resonance imaging (fMRI, electroencephalogram (EEG, and magnetoencephalogram (MEG. Models of the cortex can establish which types of large-scale neuronal networks can perform computations and characterize their emergent properties. Mean-field and related formulations of dynamics also play an essential and complementary role as forward models that can be inverted given empirical data. This makes dynamic models critical in integrating theory and experiments. We argue that elaborating principled and informed models is a prerequisite for grounding empirical neuroscience in a cogent theoretical framework, commensurate with the achievements in the

  11. Integrated workflows for spiking neuronal network simulations

    Directory of Open Access Journals (Sweden)

    Ján eAntolík

    2013-12-01

    Full Text Available The increasing availability of computational resources is enabling more detailed, realistic modelling in computational neuroscience, resulting in a shift towards more heterogeneous models of neuronal circuits, and employment of complex experimental protocols. This poses a challenge for existing tool chains, as the set of tools involved in a typical modeller's workflow is expanding concomitantly, with growing complexity in the metadata flowing between them. For many parts of the workflow, a range of tools is available; however, numerous areas lack dedicated tools, while integration of existing tools is limited. This forces modellers to either handle the workflow manually, leading to errors, or to write substantial amounts of code to automate parts of the workflow, in both cases reducing their productivity.To address these issues, we have developed Mozaik: a workflow system for spiking neuronal network simulations written in Python. Mozaik integrates model, experiment and stimulation specification, simulation execution, data storage, data analysis and visualisation into a single automated workflow, ensuring that all relevant metadata are available to all workflow components. It is based on several existing tools, including PyNN, Neo and Matplotlib. It offers a declarative way to specify models and recording configurations using hierarchically organised configuration files. Mozaik automatically records all data together with all relevant metadata about the experimental context, allowing automation of the analysis and visualisation stages. Mozaik has a modular architecture, and the existing modules are designed to be extensible with minimal programming effort. Mozaik increases the productivity of running virtual experiments on highly structured neuronal networks by automating the entire experimental cycle, while increasing the reliability of modelling studies by relieving the user from manual handling of the flow of metadata between the individual

  12. A model of biological neuron with terminal chaos and quantum-like features

    International Nuclear Information System (INIS)

    Conte, Elio; Pierri, GianPaolo; Federici, Antonio; Mendolicchio, Leonardo; Zbilut, Joseph P.

    2006-01-01

    A model of biological neuron is proposed combining terminal dynamics with quantum-like mechanical features, assuming the spin to be an important entity in neurodynamics, and, in particular, in synaptic transmission

  13. Automatically tracking neurons in a moving and deforming brain.

    Directory of Open Access Journals (Sweden)

    Jeffrey P Nguyen

    2017-05-01

    Full Text Available Advances in optical neuroimaging techniques now allow neural activity to be recorded with cellular resolution in awake and behaving animals. Brain motion in these recordings pose a unique challenge. The location of individual neurons must be tracked in 3D over time to accurately extract single neuron activity traces. Recordings from small invertebrates like C. elegans are especially challenging because they undergo very large brain motion and deformation during animal movement. Here we present an automated computer vision pipeline to reliably track populations of neurons with single neuron resolution in the brain of a freely moving C. elegans undergoing large motion and deformation. 3D volumetric fluorescent images of the animal's brain are straightened, aligned and registered, and the locations of neurons in the images are found via segmentation. Each neuron is then assigned an identity using a new time-independent machine-learning approach we call Neuron Registration Vector Encoding. In this approach, non-rigid point-set registration is used to match each segmented neuron in each volume with a set of reference volumes taken from throughout the recording. The way each neuron matches with the references defines a feature vector which is clustered to assign an identity to each neuron in each volume. Finally, thin-plate spline interpolation is used to correct errors in segmentation and check consistency of assigned identities. The Neuron Registration Vector Encoding approach proposed here is uniquely well suited for tracking neurons in brains undergoing large deformations. When applied to whole-brain calcium imaging recordings in freely moving C. elegans, this analysis pipeline located 156 neurons for the duration of an 8 minute recording and consistently found more neurons more quickly than manual or semi-automated approaches.

  14. Functional imaging of stimulus convergence in amygdalar neurons during Pavlovian fear conditioning.

    Directory of Open Access Journals (Sweden)

    Sabiha K Barot

    2009-07-01

    Full Text Available Associative conditioning is a ubiquitous form of learning throughout the animal kingdom and fear conditioning is one of the most widely researched models for studying its neurobiological basis. Fear conditioning is also considered a model system for understanding phobias and anxiety disorders. A fundamental issue in fear conditioning regards the existence and location of neurons in the brain that receive convergent information about the conditioned stimulus (CS and unconditioned stimulus (US during the acquisition of conditioned fear memory. Convergent activation of neurons is generally viewed as a key event for fear learning, yet there has been almost no direct evidence of this critical event in the mammalian brain.Here, we used Arc cellular compartmental analysis of temporal gene transcription by fluorescence in situ hybridization (catFISH to identify neurons activated during single trial contextual fear conditioning in rats. To conform to temporal requirements of catFISH analysis we used a novel delayed contextual fear conditioning protocol which yields significant single- trial fear conditioning with temporal parameters amenable to catFISH analysis. Analysis yielded clear evidence that a population of BLA neurons receives convergent CS and US information at the time of the learning, that this only occurs when the CS-US arrangement is supportive of the learning, and that this process requires N-methyl-D-aspartate receptor activation. In contrast, CS-US convergence was not observed in dorsal hippocampus.Based on the pattern of Arc activation seen in conditioning and control groups, we propose that a key requirement for CS-US convergence onto BLA neurons is the potentiation of US responding by prior exposure to a novel CS. Our results also support the view that contextual fear memories are encoded in the amygdala and that the role of dorsal hippocampus is to process and transmit contextual CS information.

  15. Organization of left–right coordination of neuronal activity in the mammalian spinal cord: Insights from computational modelling

    Science.gov (United States)

    Shevtsova, Natalia A; Talpalar, Adolfo E; Markin, Sergey N; Harris-Warrick, Ronald M; Kiehn, Ole; Rybak, Ilya A

    2015-01-01

    Different locomotor gaits in mammals, such as walking or galloping, are produced by coordinated activity in neuronal circuits in the spinal cord. Coordination of neuronal activity between left and right sides of the cord is provided by commissural interneurons (CINs), whose axons cross the midline. In this study, we construct and analyse two computational models of spinal locomotor circuits consisting of left and right rhythm generators interacting bilaterally via several neuronal pathways mediated by different CINs. The CIN populations incorporated in the models include the genetically identified inhibitory (V0D) and excitatory (V0V) subtypes of V0 CINs and excitatory V3 CINs. The model also includes the ipsilaterally projecting excitatory V2a interneurons mediating excitatory drive to the V0V CINs. The proposed network architectures and CIN connectivity allow the models to closely reproduce and suggest mechanistic explanations for several experimental observations. These phenomena include: different speed-dependent contributions of V0D and V0V CINs and V2a interneurons to left–right alternation of neural activity, switching gaits between the left–right alternating walking-like activity and the left–right synchronous hopping-like pattern in mutants lacking specific neuron classes, and speed-dependent asymmetric changes of flexor and extensor phase durations. The models provide insights into the architecture of spinal network and the organization of parallel inhibitory and excitatory CIN pathways and suggest explanations for how these pathways maintain alternating and synchronous gaits at different locomotor speeds. The models propose testable predictions about the neural organization and operation of mammalian locomotor circuits. Key points Coordination of neuronal activity between left and right sides of the mammalian spinal cord is provided by several sets of commissural interneurons (CINs) whose axons cross the midline. Genetically identified inhibitory V

  16. Mirror neurons and imitation: a computationally guided review.

    Science.gov (United States)

    Oztop, Erhan; Kawato, Mitsuo; Arbib, Michael

    2006-04-01

    Neurophysiology reveals the properties of individual mirror neurons in the macaque while brain imaging reveals the presence of 'mirror systems' (not individual neurons) in the human. Current conceptual models attribute high level functions such as action understanding, imitation, and language to mirror neurons. However, only the first of these three functions is well-developed in monkeys. We thus distinguish current opinions (conceptual models) on mirror neuron function from more detailed computational models. We assess the strengths and weaknesses of current computational models in addressing the data and speculations on mirror neurons (macaque) and mirror systems (human). In particular, our mirror neuron system (MNS), mental state inference (MSI) and modular selection and identification for control (MOSAIC) models are analyzed in more detail. Conceptual models often overlook the computational requirements for posited functions, while too many computational models adopt the erroneous hypothesis that mirror neurons are interchangeable with imitation ability. Our meta-analysis underlines the gap between conceptual and computational models and points out the research effort required from both sides to reduce this gap.

  17. Ordering Dynamics in Neuron Activity Pattern Model: An Insight to Brain Functionality.

    Directory of Open Access Journals (Sweden)

    Jasleen Gundh

    Full Text Available We study the domain ordering kinetics in d = 2 ferromagnets which corresponds to populated neuron activities with both long-ranged interactions, V(r ∼ r-n and short-ranged interactions. We present the results from comprehensive Monte Carlo (MC simulations for the nonconserved Ising model with n ≥ 2, interaction range considering near and far neighbors. Our model results could represent the long-ranged neuron kinetics (n ≤ 4 in consistent with the same dynamical behaviour of short-ranged case (n ≥ 4 at far below and near criticality. We found that emergence of fast and slow kinetics of long and short ranged case could imitate the formation of connections among near and distant neurons. The calculated characteristic length scale in long-ranged interaction is found to be n independent (L(t ∼ t1/(n-2, whereas short-ranged interaction follows L(t ∼ t1/2 law and approximately preserve universality in domain kinetics. Further, we did the comparative study of phase ordering near the critical temperature which follows different behaviours of domain ordering near and far critical temperature but follows universal scaling law.

  18. Altering neuronal excitability to preserve network connectivity in a computational model of Alzheimer's disease.

    Directory of Open Access Journals (Sweden)

    Willem de Haan

    2017-09-01

    Full Text Available Neuronal hyperactivity and hyperexcitability of the cerebral cortex and hippocampal region is an increasingly observed phenomenon in preclinical Alzheimer's disease (AD. In later stages, oscillatory slowing and loss of functional connectivity are ubiquitous. Recent evidence suggests that neuronal dynamics have a prominent role in AD pathophysiology, making it a potentially interesting therapeutic target. However, although neuronal activity can be manipulated by various (non-pharmacological means, intervening in a highly integrated system that depends on complex dynamics can produce counterintuitive and adverse effects. Computational dynamic network modeling may serve as a virtual test ground for developing effective interventions. To explore this approach, a previously introduced large-scale neural mass network with human brain topology was used to simulate the temporal evolution of AD-like, activity-dependent network degeneration. In addition, six defense strategies that either enhanced or diminished neuronal excitability were tested against the degeneration process, targeting excitatory and inhibitory neurons combined or separately. Outcome measures described oscillatory, connectivity and topological features of the damaged networks. Over time, the various interventions produced diverse large-scale network effects. Contrary to our hypothesis, the most successful strategy was a selective stimulation of all excitatory neurons in the network; it substantially prolonged the preservation of network integrity. The results of this study imply that functional network damage due to pathological neuronal activity can be opposed by targeted adjustment of neuronal excitability levels. The present approach may help to explore therapeutic effects aimed at preserving or restoring neuronal network integrity and contribute to better-informed intervention choices in future clinical trials in AD.

  19. An electronic implementation for Liao's chaotic delayed neuron model with non-monotonous activation function

    Energy Technology Data Exchange (ETDEWEB)

    Duan Shukai [Department of Computer Science and Engineering, Chongqing University, Chongqing 400044 (China); School of Electronic and Information Engineering, Southwest University, Chongqing 400715 (China)], E-mail: duansk@swu.edu.cn; Liao Xiaofeng [Department of Computer Science and Engineering, Chongqing University, Chongqing 400044 (China)], E-mail: xfliao@cqu.edu.cn

    2007-09-10

    A new chaotic delayed neuron model with non-monotonously increasing transfer function, called as chaotic Liao's delayed neuron model, was recently reported and analyzed. An electronic implementation of this model is described in detail. At the same time, some methods in circuit design, especially for circuit with time delayed unit and non-monotonously increasing activation unit, are also considered carefully. We find that the dynamical behaviors of the designed circuits are closely similar to the results predicted by numerical experiments.

  20. Simple and effective graphene laser processing for neuron patterning application

    Science.gov (United States)

    Lorenzoni, Matteo; Brandi, Fernando; Dante, Silvia; Giugni, Andrea; Torre, Bruno

    2013-06-01

    A straightforward fabrication technique to obtain patterned substrates promoting ordered neuron growth is presented. Chemical vapor deposition (CVD) single layer graphene (SLG) was machined by means of single pulse UV laser ablation technique at the lowest effective laser fluence in order to minimize laser damage effects. Patterned substrates were then coated with poly-D-lysine by means of a simple immersion in solution. Primary embryonic hippocampal neurons were cultured on our substrate, demonstrating an ordered interconnected neuron pattern mimicking the pattern design. Surprisingly, the functionalization is more effective on the SLG, resulting in notably higher alignment for neuron adhesion and growth. Therefore the proposed technique should be considered a valuable candidate to realize a new generation of highly specialized biosensors.

  1. SH-SY5Y human neuroblastoma cell line: in vitro cell model of dopaminergic neurons in Parkinson's disease.

    Science.gov (United States)

    Xie, Hong-rong; Hu, Lin-sen; Li, Guo-yi

    2010-04-20

    To evaluate the human neuroblastoma SH-SY5Y cell line as an in vitro model of dopaminergic (DAergic) neurons for Parkinson's disease (PD) research and to determine the effect of differentiation on this cell model. The data of this review were selected from the original reports and reviews related to SH-SY5Y cells published in Chinese and foreign journals (Pubmed 1973 to 2009). After searching the literature, 60 articles were selected to address this review. The SH-SY5Y cell line has become a popular cell model for PD research because this cell line posses many characteristics of DAergic neurons. For example, these cells express tyrosine hydroxylase and dopamine-beta-hydroxylase, as well as the dopamine transporter. Moreover, this cell line can be differentiated into a functionally mature neuronal phenotype in the presence of various agents. Upon differentiation, SH-SY5Y cells stop proliferating and a constant cell number is subsequently maintained. However, different differentiating agents induce different neuronal phenotypes and biochemical changes. For example, retinoic acid induces differentiation toward a cholinergic neuronal phenotype and increases the susceptibility of SH-SY5Y cells to neurotoxins and neuroprotective agents, whereas treatment with retinoic acid followed by phorbol ester 12-O-tetradecanoylphorbol-13-acetate results in a DAergic neuronal phenotype and decreases the susceptibility of cells to neurotoxins and neuroprotective agents. Some differentiating agents also alter kinetics of 1-methyl-4-phenyl-pyridinium (MPP(+)) uptake, making SH-SY5Y cells more similar to primary mesencephalic neurons. Differentiated and undifferentiated SH-SY5Y cells have been widely used as a cell model of DAergic neurons for PD research. Some differentiating agents afford SH-SY5Y cells with more potential for studying neurotoxicity and neuroprotection and are thus more relevant to experimental PD research.

  2. Neuronal and astrocytic metabolism in a transgenic rat model of Alzheimer's disease.

    Science.gov (United States)

    Nilsen, Linn Hege; Witter, Menno P; Sonnewald, Ursula

    2014-05-01

    Regional hypometabolism of glucose in the brain is a hallmark of Alzheimer's disease (AD). However, little is known about the specific alterations of neuronal and astrocytic metabolism involved in homeostasis of glutamate and GABA in AD. Here, we investigated the effects of amyloid β (Aβ) pathology on neuronal and astrocytic metabolism and glial-neuronal interactions in amino acid neurotransmitter homeostasis in the transgenic McGill-R-Thy1-APP rat model of AD compared with healthy controls at age 15 months. Rats were injected with [1-(13)C]glucose and [1,2-(13)C]acetate, and extracts of the hippocampal formation as well as several cortical regions were analyzed using (1)H- and (13)C nuclear magnetic resonance spectroscopy and high-performance liquid chromatography. Reduced tricarboxylic acid cycle turnover was evident for glutamatergic and GABAergic neurons in hippocampal formation and frontal cortex, and for astrocytes in frontal cortex. Pyruvate carboxylation, which is necessary for de novo synthesis of amino acids, was decreased and affected the level of glutamine in hippocampal formation and those of glutamate, glutamine, GABA, and aspartate in the retrosplenial/cingulate cortex. Metabolic alterations were also detected in the entorhinal cortex. Overall, perturbations in energy- and neurotransmitter homeostasis, mitochondrial astrocytic and neuronal metabolism, and aspects of the glutamate-glutamine cycle were found in McGill-R-Thy1-APP rats.

  3. mTOR pathway inhibition prevents neuroinflammation and neuronal death in a mouse model of cerebral palsy.

    Science.gov (United States)

    Srivastava, Isha N; Shperdheja, Jona; Baybis, Marianna; Ferguson, Tanya; Crino, Peter B

    2016-01-01

    Mammalian target of rapamycin (mTOR) pathway signaling governs cellular responses to hypoxia and inflammation including induction of autophagy and cell survival. Cerebral palsy (CP) is a neurodevelopmental disorder linked to hypoxic and inflammatory brain injury however, a role for mTOR modulation in CP has not been investigated. We hypothesized that mTOR pathway inhibition would diminish inflammation and prevent neuronal death in a mouse model of CP. Mouse pups (P6) were subjected to hypoxia-ischemia and lipopolysaccharide-induced inflammation (HIL), a model of CP causing neuronal injury within the hippocampus, periventricular white matter, and neocortex. mTOR pathway inhibition was achieved with rapamycin (an mTOR inhibitor; 5mg/kg) or PF-4708671 (an inhibitor of the downstream p70S6kinase, S6K, 75 mg/kg) immediately following HIL, and then for 3 subsequent days. Phospho-activation of the mTOR effectors p70S6kinase and ribosomal S6 protein and expression of hypoxia inducible factor 1 (HIF-1α) were assayed. Neuronal cell death was defined with Fluoro-Jade C (FJC) and autophagy was measured using Beclin-1 and LC3II expression. Iba-1 labeled, activated microglia were quantified. Neuronal death, enhanced HIF-1α expression, and numerous Iba-1 labeled, activated microglia were evident at 24 and 48 h following HIL. Basal mTOR signaling, as evidenced by phosphorylated-S6 and -S6K levels, was unchanged by HIL. Rapamycin or PF-4,708,671 treatment significantly reduced mTOR signaling, neuronal death, HIF-1α expression, and microglial activation, coincident with enhanced expression of Beclin-1 and LC3II, markers of autophagy induction. mTOR pathway inhibition prevented neuronal death and diminished neuroinflammation in this model of CP. Persistent mTOR signaling following HIL suggests a failure of autophagy induction, which may contribute to neuronal death in CP. These results suggest that mTOR signaling may be a novel therapeutic target to reduce neuronal cell death in

  4. Gravity and neuronal adaptation, in vitro and in vivo-from neuronal cells up to neuromuscular responses: a first model.

    Science.gov (United States)

    Kohn, Florian P M; Ritzmann, Ramona

    2018-03-01

    For decades it has been shown that acute changes in gravity have an effect on neuronal systems of human and animals on different levels, from the molecular level to the whole nervous system. The functional properties and gravity-dependent adaptations of these system levels have been investigated with no or barely any interconnection. This review summarizes the gravity-dependent adaptation processes in human and animal organisms from the in vitro cellular level with its biophysical properties to the in vivo motor responses and underlying sensorimotor functions of human subjects. Subsequently, a first model for short-term adaptation of neuronal transmission is presented and discussed for the first time, which integrates the responses of the different levels of organization to changes in gravity.

  5. Composite mathematical modeling of calcium signaling behind neuronal cell death in Alzheimer's disease.

    Science.gov (United States)

    Ranjan, Bobby; Chong, Ket Hing; Zheng, Jie

    2018-04-11

    Alzheimer's disease (AD) is a progressive neurological disorder, recognized as the most common cause of dementia affecting people aged 65 and above. AD is characterized by an increase in amyloid metabolism, and by the misfolding and deposition of β-amyloid oligomers in and around neurons in the brain. These processes remodel the calcium signaling mechanism in neurons, leading to cell death via apoptosis. Despite accumulating knowledge about the biological processes underlying AD, mathematical models to date are restricted to depicting only a small portion of the pathology. Here, we integrated multiple mathematical models to analyze and understand the relationship among amyloid depositions, calcium signaling and mitochondrial permeability transition pore (PTP) related cell apoptosis in AD. The model was used to simulate calcium dynamics in the absence and presence of AD. In the absence of AD, i.e. without β-amyloid deposition, mitochondrial and cytosolic calcium level remains in the low resting concentration. However, our in silico simulation of the presence of AD with the β-amyloid deposition, shows an increase in the entry of calcium ions into the cell and dysregulation of Ca 2+ channel receptors on the Endoplasmic Reticulum. This composite model enabled us to make simulation that is not possible to measure experimentally. Our mathematical model depicting the mechanisms affecting calcium signaling in neurons can help understand AD at the systems level and has potential for diagnostic and therapeutic applications.

  6. Alterations in Neuronal Activity in Basal Ganglia-Thalamocortical Circuits in the Parkinsonian State

    Directory of Open Access Journals (Sweden)

    Adriana eGalvan

    2015-02-01

    Full Text Available In patients with Parkinson’s disease and in animal models of this disorder, neurons in the basal ganglia and related regions in thalamus and cortex show changes that can be recorded by using electrophysiologic single-cell recording techniques, including altered firing rates and patterns, pathologic oscillatory activity and increased inter-neuronal synchronization. In addition, changes in synaptic potentials or in the joint spiking activities of populations of neurons can be monitored as alterations in local field potentials, electroencephalograms or electrocorticograms. Most of the mentioned electrophysiologic changes are probably related to the degeneration of diencephalic dopaminergic neurons, leading to dopamine loss in the striatum and other basal ganglia nuclei, although degeneration of non-dopaminergic cell groups may also have a role. The altered electrical activity of the basal ganglia and associated nuclei may contribute to some of the motor signs of the disease. We here review the current knowledge of the electrophysiologic changes at the single cell level, the level of local populations of neural elements, and the level of the entire basal ganglia-thalamocortical network in parkinsonism, and discuss the possible use of this information to optimize treatment approaches to Parkinson’s disease, such as deep brain stimulation therapy.

  7. Alterations in neuronal activity in basal ganglia-thalamocortical circuits in the parkinsonian state

    Science.gov (United States)

    Galvan, Adriana; Devergnas, Annaelle; Wichmann, Thomas

    2015-01-01

    In patients with Parkinson’s disease and in animal models of this disorder, neurons in the basal ganglia and related regions in thalamus and cortex show changes that can be recorded by using electrophysiologic single-cell recording techniques, including altered firing rates and patterns, pathologic oscillatory activity and increased inter-neuronal synchronization. In addition, changes in synaptic potentials or in the joint spiking activities of populations of neurons can be monitored as alterations in local field potentials (LFPs), electroencephalograms (EEGs) or electrocorticograms (ECoGs). Most of the mentioned electrophysiologic changes are probably related to the degeneration of diencephalic dopaminergic neurons, leading to dopamine loss in the striatum and other basal ganglia nuclei, although degeneration of non-dopaminergic cell groups may also have a role. The altered electrical activity of the basal ganglia and associated nuclei may contribute to some of the motor signs of the disease. We here review the current knowledge of the electrophysiologic changes at the single cell level, the level of local populations of neural elements, and the level of the entire basal ganglia-thalamocortical network in parkinsonism, and discuss the possible use of this information to optimize treatment approaches to Parkinson’s disease, such as deep brain stimulation (DBS) therapy. PMID:25698937

  8. Successive neuron loss in the thalamus and cortex in a mouse model of infantile neuronal ceroid lipofuscinosis.

    Science.gov (United States)

    Kielar, Catherine; Maddox, Lucy; Bible, Ellen; Pontikis, Charlie C; Macauley, Shannon L; Griffey, Megan A; Wong, Michael; Sands, Mark S; Cooper, Jonathan D

    2007-01-01

    Infantile neuronal ceroid lipofuscinosis (INCL) is caused by deficiency of the lysosomal enzyme, palmitoyl protein thioesterase 1 (PPT1). We have investigated the onset and progression of pathological changes in Ppt1 deficient mice (Ppt1-/-) and the development of their seizure phenotype. Surprisingly, cortical atrophy and neuron loss occurred only late in disease progression but were preceded by localized astrocytosis within individual thalamic nuclei and the progressive loss of thalamic neurons that relay different sensory modalities to the cortex. This thalamic neuron loss occurred first within the visual system and only subsequently in auditory and somatosensory relay nuclei or the inhibitory reticular thalamic nucleus. The loss of granule neurons and GABAergic interneurons followed in each corresponding cortical region, before the onset of seizure activity. These findings provide novel evidence for successive neuron loss within the thalamus and cortex in Ppt1-/- mice, revealing the thalamus as an important early focus of INCL pathogenesis.

  9. Comparing Realistic Subthalamic Nucleus Neuron Models

    Science.gov (United States)

    Njap, Felix; Claussen, Jens C.; Moser, Andreas; Hofmann, Ulrich G.

    2011-06-01

    The mechanism of action of clinically effective electrical high frequency stimulation is still under debate. However, recent evidence points at the specific activation of GABA-ergic ion channels. Using a computational approach, we analyze temporal properties of the spike trains emitted by biologically realistic neurons of the subthalamic nucleus (STN) as a function of GABA-ergic synaptic input conductances. Our contribution is based on a model proposed by Rubin and Terman and exhibits a wide variety of different firing patterns, silent, low spiking, moderate spiking and intense spiking activity. We observed that most of the cells in our network turn to silent mode when we increase the GABAA input conductance above the threshold of 3.75 mS/cm2. On the other hand, insignificant changes in firing activity are observed when the input conductance is low or close to zero. We thus reproduce Rubin's model with vanishing synaptic conductances. To quantitatively compare spike trains from the original model with the modified model at different conductance levels, we apply four different (dis)similarity measures between them. We observe that Mahalanobis distance, Victor-Purpura metric, and Interspike Interval distribution are sensitive to different firing regimes, whereas Mutual Information seems undiscriminative for these functional changes.

  10. Coordinate control of integral reactor based on single neuron PID controller

    International Nuclear Information System (INIS)

    Liu Yan; Xia Hong

    2014-01-01

    As one of the main type of reactors in the future, the development of the integral reactor has attracted worldwide attention. On the basis of understanding the background of the integral reactor, the author will be familiar with and master the power control of reactor and the feedwater flow control of steam generator, and the speed control of turbine (turbine speed control is associated with the turbine load control). According to the expectative program 'reactor power following turbine load' of the reactor, it will make coordinate control of the three and come to a overall control scheme. The author will use the supervisory learning algorithm of Hebb for single neuron PID controller with self-adaptation to study the coordinate control of integral reactor. Compared with conventional PI or PID controller, to a certain extent, it solves the problems that traditional PID controller is not easy to tune real-time parameters and lack of effective control for a number of complex processes and slow-varying parameter systems. It improves the security, reliability, stability and flexibility of control process and achieves effective control of the system. (authors)

  11. Experimental Models of Status Epilepticus and Neuronal Injury for Evaluation of Therapeutic Interventions

    Directory of Open Access Journals (Sweden)

    Ramkumar Kuruba

    2013-09-01

    Full Text Available This article describes current experimental models of status epilepticus (SE and neuronal injury for use in the screening of new therapeutic agents. Epilepsy is a common neurological disorder characterized by recurrent unprovoked seizures. SE is an emergency condition associated with continuous seizures lasting more than 30 min. It causes significant mortality and morbidity. SE can cause devastating damage to the brain leading to cognitive impairment and increased risk of epilepsy. Benzodiazepines are the first-line drugs for the treatment of SE, however, many people exhibit partial or complete resistance due to a breakdown of GABA inhibition. Therefore, new drugs with neuroprotective effects against the SE-induced neuronal injury and degeneration are desirable. Animal models are used to study the pathophysiology of SE and for the discovery of newer anticonvulsants. In SE paradigms, seizures are induced in rodents by chemical agents or by electrical stimulation of brain structures. Electrical stimulation includes perforant path and self-sustaining stimulation models. Pharmacological models include kainic acid, pilocarpine, flurothyl, organophosphates and other convulsants that induce SE in rodents. Neuronal injury occurs within the initial SE episode, and animals exhibit cognitive dysfunction and spontaneous seizures several weeks after this precipitating event. Current SE models have potential applications but have some limitations. In general, the experimental SE model should be analogous to the human seizure state and it should share very similar neuropathological mechanisms. The pilocarpine and diisopropylfluorophosphate models are associated with prolonged, diazepam-insensitive seizures and neurodegeneration and therefore represent paradigms of refractory SE. Novel mechanism-based or clinically relevant models are essential to identify new therapies for SE and neuroprotective interventions.

  12. Wnt1 from cochlear schwann cells enhances neuronal differentiation of transplanted neural stem cells in a rat spiral ganglion neuron degeneration model.

    Science.gov (United States)

    He, Ya; Zhang, Peng-Zhi; Sun, Dong; Mi, Wen-Juan; Zhang, Xin-Yi; Cui, Yong; Jiang, Xing-Wang; Mao, Xiao-Bo; Qiu, Jian-Hua

    2014-04-01

    Although neural stem cell (NSC) transplantation is widely expected to become a therapy for nervous system degenerative diseases and injuries, the low neuronal differentiation rate of NSCs transplanted into the inner ear is a major obstacle for the successful treatment of spiral ganglion neuron (SGN) degeneration. In this study, we validated whether the local microenvironment influences the neuronal differentiation of transplanted NSCs in the inner ear. Using a rat SGN degeneration model, we demonstrated that transplanted NSCs were more likely to differentiate into microtubule-associated protein 2 (MAP2)-positive neurons in SGN-degenerated cochleae than in control cochleae. Using real-time quantitative PCR and an immunofluorescence assay, we also proved that the expression of Wnt1 (a ligand of Wnt signaling) increases significantly in Schwann cells in the SGN-degenerated cochlea. We further verified that NSC cultures express receptors and signaling components for Wnts. Based on these expression patterns, we hypothesized that Schwann cell-derived Wnt1 and Wnt signaling might be involved in the regulation of the neuronal differentiation of transplanted NSCs. We verified our hypothesis in vitro using a coculture system. We transduced a lentiviral vector expressing Wnt1 into cochlear Schwann cell cultures and cocultured them with NSC cultures. The coculture with Wnt1-expressing Schwann cells resulted in a significant increase in the percentage of NSCs that differentiated into MAP2-positive neurons, whereas this differentiation-enhancing effect was prevented by Dkk1 (an inhibitor of the Wnt signaling pathway). These results suggested that Wnt1 derived from cochlear Schwann cells enhanced the neuronal differentiation of transplanted NSCs through Wnt signaling pathway activation. Alterations of the microenvironment deserve detailed investigation because they may help us to conceive effective strategies to overcome the barrier of the low differentiation rate of transplanted

  13. Proton- and ammonium- sensing by histaminergic neurons controlling wakefulness.

    Directory of Open Access Journals (Sweden)

    Yvgenij eYanovsky

    2012-04-01

    Full Text Available Orexinergic and histaminergic neurons in the posterior hypothalamus are involved in the control of arousal. Extracellular levels of acid /CO2 are fundamental physicochemical signals controlling wakefulness and breathing. Acidification excites orexinergic neurons like the chemosensory neurons in the brain stem. Hypercapnia induces c-Fos expression, a marker for increased neuronal activity, in the rat histaminergic tuberomamillary nucleus (TMN, but the mechanisms of this excitation are unknown. Acid-sensing ion channels (ASICs are gated by protons and also by ammonium. Recordings in rat brain slices revealed now that acidification within the physiological range (pH from 7.3 to 7.0 as well as ammonium chloride (5mM excite histaminergic neurons. We detected variable combinations of 4 known types of ASICs in single TMN neurons, along with the pharmacological properties of pH-induced current. At pH 7.0 however, activation of ASICs in TMN neurons was negligible. Block of type I metabotropic glutamate receptors abolished proton- but not ammonium- induced excitation. Mouse TMN neurons were identified within a novel HDC-Cre transgenic reporter mouse line. In contrast to the rat these lacked pH 7.0-induced excitation and showed only a minimal response to the mGluR I agonist DHPG (0.5µM. Ammonium-induced excitation was similar in mouse and rat. Thus glutamate, which is released by glial cells and orexinergic axons amplifies CO2/acid-induced arousal through the recruitment of the histaminergic system in rat but not in mouse. These results are relevant for the understanding of neuronal mechanisms controlling H+/CO2-induced arousal in hepatic encephalopathy and obstructive sleep apnoea. The new HDC-Cre mouse model will be a useful tool for studying the physiological and pathophysiological roles of the histaminergic system.

  14. Subsampling effects in neuronal avalanche distributions recorded in vivo

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    Munk Matthias HJ

    2009-04-01

    Full Text Available Abstract Background Many systems in nature are characterized by complex behaviour where large cascades of events, or avalanches, unpredictably alternate with periods of little activity. Snow avalanches are an example. Often the size distribution f(s of a system's avalanches follows a power law, and the branching parameter sigma, the average number of events triggered by a single preceding event, is unity. A power law for f(s, and sigma = 1, are hallmark features of self-organized critical (SOC systems, and both have been found for neuronal activity in vitro. Therefore, and since SOC systems and neuronal activity both show large variability, long-term stability and memory capabilities, SOC has been proposed to govern neuronal dynamics in vivo. Testing this hypothesis is difficult because neuronal activity is spatially or temporally subsampled, while theories of SOC systems assume full sampling. To close this gap, we investigated how subsampling affects f(s and sigma by imposing subsampling on three different SOC models. We then compared f(s and sigma of the subsampled models with those of multielectrode local field potential (LFP activity recorded in three macaque monkeys performing a short term memory task. Results Neither the LFP nor the subsampled SOC models showed a power law for f(s. Both, f(s and sigma, depended sensitively on the subsampling geometry and the dynamics of the model. Only one of the SOC models, the Abelian Sandpile Model, exhibited f(s and sigma similar to those calculated from LFP activity. Conclusion Since subsampling can prevent the observation of the characteristic power law and sigma in SOC systems, misclassifications of critical systems as sub- or supercritical are possible. Nevertheless, the system specific scaling of f(s and sigma under subsampling conditions may prove useful to select physiologically motivated models of brain function. Models that better reproduce f(s and sigma calculated from the physiological

  15. Beyond Critical Exponents in Neuronal Avalanches

    Science.gov (United States)

    Friedman, Nir; Butler, Tom; Deville, Robert; Beggs, John; Dahmen, Karin

    2011-03-01

    Neurons form a complex network in the brain, where they interact with one another by firing electrical signals. Neurons firing can trigger other neurons to fire, potentially causing avalanches of activity in the network. In many cases these avalanches have been found to be scale independent, similar to critical phenomena in diverse systems such as magnets and earthquakes. We discuss models for neuronal activity that allow for the extraction of testable, statistical predictions. We compare these models to experimental results, and go beyond critical exponents.

  16. History-dependent excitability as a single-cell substrate of transient memory for information discrimination.

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    Fabiano Baroni

    Full Text Available Neurons react differently to incoming stimuli depending upon their previous history of stimulation. This property can be considered as a single-cell substrate for transient memory, or context-dependent information processing: depending upon the current context that the neuron "sees" through the subset of the network impinging on it in the immediate past, the same synaptic event can evoke a postsynaptic spike or just a subthreshold depolarization. We propose a formal definition of History-Dependent Excitability (HDE as a measure of the propensity to firing in any moment in time, linking the subthreshold history-dependent dynamics with spike generation. This definition allows the quantitative assessment of the intrinsic memory for different single-neuron dynamics and input statistics. We illustrate the concept of HDE by considering two general dynamical mechanisms: the passive behavior of an Integrate and Fire (IF neuron, and the inductive behavior of a Generalized Integrate and Fire (GIF neuron with subthreshold damped oscillations. This framework allows us to characterize the sensitivity of different model neurons to the detailed temporal structure of incoming stimuli. While a neuron with intrinsic oscillations discriminates equally well between input trains with the same or different frequency, a passive neuron discriminates better between inputs with different frequencies. This suggests that passive neurons are better suited to rate-based computation, while neurons with subthreshold oscillations are advantageous in a temporal coding scheme. We also address the influence of intrinsic properties in single-cell processing as a function of input statistics, and show that intrinsic oscillations enhance discrimination sensitivity at high input rates. Finally, we discuss how the recognition of these cell-specific discrimination properties might further our understanding of neuronal network computations and their relationships to the distribution and

  17. Divisive Normalization and Neuronal Oscillations in a Single Hierarchical Framework of Selective Visual Attention

    OpenAIRE

    Montijn, Jorrit Steven; Klink, P. Christaan; van Wezel, Richard J. A.

    2012-01-01

    Divisive normalization models of covert attention commonly use spike rate modulations as indicators of the effect of top-down attention. In addition, an increasing number of studies have shown that top-down attention increases the synchronization of neuronal oscillations as well, particularly in gamma-band frequencies (25–100 Hz). Although modulations of spike rate and synchronous oscillations are not mutually exclusive as mechanisms of attention, there has thus far been little effort to inte...

  18. ESCRT-mediated uptake and degradation of brain-targeted α-synuclein single chain antibody attenuates neuronal degeneration in vivo.

    Science.gov (United States)

    Spencer, Brian; Emadi, Sharareh; Desplats, Paula; Eleuteri, Simona; Michael, Sarah; Kosberg, Kori; Shen, Jay; Rockenstein, Edward; Patrick, Christina; Adame, Anthony; Gonzalez, Tania; Sierks, Michael; Masliah, Eliezer

    2014-10-01

    Parkinson's disease and dementia with Lewy bodies are neurodegenerative disorders characterized by accumulation of α-synuclein (α-syn). Recently, single-chain fragment variables (scFVs) have been developed against individual conformational species of α-syn. Unlike more traditional monoclonal antibodies, these scFVs will not activate or be endocytosed by Fc receptors. For this study, we investigated an scFV directed against oligomeric α-syn fused to the LDL receptor-binding domain from apolipoprotein B (apoB). The modified scFV showed enhanced brain penetration and was imported into neuronal cells through the endosomal sorting complex required for transport (ESCRT) pathway, leading to lysosomal degradation of α-syn aggregates. Further analysis showed that the scFV was effective at ameliorating neurodegenerative pathology and behavioral deficits observed in the mouse model of dementia with Lewy bodies/Parkinson's disease. Thus, the apoB modification had the effect of both increasing accumulation of the scFV in the brain and directing scFV/α-syn complexes for degradation through the ESCRT pathway, leading to improved therapeutic potential of immunotherapy.

  19. Energy consumption and information transmission in model neurons

    International Nuclear Information System (INIS)

    Torrealdea, Francisco J.; Sarasola, Cecilia; D'Anjou, Alicia

    2009-01-01

    This work deals with the problem of whether biological computation optimizes energy use in the way neurons communicate. By assigning an electrical energy function to a Hindmarsh-Rose neuron we are able to find its average energy consumption when it reacts to incoming signals sent by another neuron coupled to it by an electrical synapse. We find that there are values of the coupling strength at which the ratio of mutual information to energy consumption is maximum and, therefore, communicating at these coupling values would be energetically the most efficient option.

  20. Energy consumption and information transmission in model neurons

    Energy Technology Data Exchange (ETDEWEB)

    Torrealdea, Francisco J. [Department of Computer Science, University of the Basque Country, 20018 San Sebastian (Spain)], E-mail: francisco.torrealdea@ehu.es; Sarasola, Cecilia [Department of Physics of Materials, University of the Basque Country, 20018 San Sebastian (Spain); D' Anjou, Alicia [Department of Computer Science, University of the Basque Country, 20018 San Sebastian (Spain)

    2009-04-15

    This work deals with the problem of whether biological computation optimizes energy use in the way neurons communicate. By assigning an electrical energy function to a Hindmarsh-Rose neuron we are able to find its average energy consumption when it reacts to incoming signals sent by another neuron coupled to it by an electrical synapse. We find that there are values of the coupling strength at which the ratio of mutual information to energy consumption is maximum and, therefore, communicating at these coupling values would be energetically the most efficient option.

  1. A Single Neonatal Exposure to BMAA in a Rat Model Produces Neuropathology Consistent with Neurodegenerative Diseases

    Directory of Open Access Journals (Sweden)

    Laura Louise Scott

    2017-12-01

    Full Text Available Although cyanobacterial β-N-methylamino-l-alanine (BMAA has been implicated in the development of Alzheimer’s Disease (AD, Parkinson’s Disease (PD and Amyotrophic Lateral Sclerosis (ALS, no BMAA animal model has reproduced all the neuropathology typically associated with these neurodegenerative diseases. We present here a neonatal BMAA model that causes β-amyloid deposition, neurofibrillary tangles of hyper-phosphorylated tau, TDP-43 inclusions, Lewy bodies, microbleeds and microgliosis as well as severe neuronal loss in the hippocampus, striatum, substantia nigra pars compacta, and ventral horn of the spinal cord in rats following a single BMAA exposure. We also report here that BMAA exposure on particularly PND3, but also PND4 and 5, the critical period of neurogenesis in the rodent brain, is substantially more toxic than exposure to BMAA on G14, PND6, 7 and 10 which suggests that BMAA could potentially interfere with neonatal neurogenesis in rats. The observed selective toxicity of BMAA during neurogenesis and, in particular, the observed pattern of neuronal loss observed in BMAA-exposed rats suggest that BMAA elicits its effect by altering dopamine and/or serotonin signaling in rats.

  2. Morphine disinhibits glutamatergic input to VTA dopamine neurons and promotes dopamine neuron excitation.

    Science.gov (United States)

    Chen, Ming; Zhao, Yanfang; Yang, Hualan; Luan, Wenjie; Song, Jiaojiao; Cui, Dongyang; Dong, Yi; Lai, Bin; Ma, Lan; Zheng, Ping

    2015-07-24

    One reported mechanism for morphine activation of dopamine (DA) neurons of the ventral tegmental area (VTA) is the disinhibition model of VTA-DA neurons. Morphine inhibits GABA inhibitory neurons, which shifts the balance between inhibitory and excitatory input to VTA-DA neurons in favor of excitation and then leads to VTA-DA neuron excitation. However, it is not known whether morphine has an additional strengthening effect on excitatory input. Our results suggest that glutamatergic input to VTA-DA neurons is inhibited by GABAergic interneurons via GABAB receptors and that morphine promotes presynaptic glutamate release by removing this inhibition. We also studied the contribution of the morphine-induced disinhibitory effect on the presynaptic glutamate release to the overall excitatory effect of morphine on VTA-DA neurons and related behavior. Our results suggest that the disinhibitory action of morphine on presynaptic glutamate release might be the main mechanism for morphine-induced increase in VTA-DA neuron firing and related behaviors.

  3. Modeling the phenotype of spinal muscular atrophy by the direct conversion of human fibroblasts to motor neurons.

    Science.gov (United States)

    Zhang, Qi-Jie; Li, Jin-Jing; Lin, Xiang; Lu, Ying-Qian; Guo, Xin-Xin; Dong, En-Lin; Zhao, Miao; He, Jin; Wang, Ning; Chen, Wan-Jin

    2017-02-14

    Spinal muscular atrophy (SMA) is a lethal autosomal recessive neurological disease characterized by selective degeneration of motor neurons in the spinal cord. In recent years, the development of cellular reprogramming technology has provided an alternative and effective method for obtaining patient-specific neurons in vitro. In the present study, we applied this technology to the field of SMA to acquire patient-specific induced motor neurons that were directly converted from fibroblasts via the forced expression of 8 defined transcription factors. The infected fibroblasts began to grow in a dipolar manner, and the nuclei gradually enlarged. Typical Tuj1-positive neurons were generated at day 23. After day 35, induced neurons with multiple neurites were observed, and these neurons also expressed the hallmarks of Tuj1, HB9, ISL1 and CHAT. The conversion efficiencies were approximately 5.8% and 5.5% in the SMA and control groups, respectively. Additionally, the SMA-induced neurons exhibited a significantly reduced neurite outgrowth rate compared with the control neurons. After day 60, the SMA-induced neurons also exhibited a liability of neuronal degeneration and remarkable fracturing of the neurites was observed. By directly reprogramming fibroblasts, we established a feeder-free conversion system to acquire SMA patient-specific induced motor neurons that partially modeled the phenotype of SMA in vitro.

  4. Self-organized Criticality and Synchronization in a Pulse-coupled Integrate-and-Fire Neuron Model Based on Small World Networks

    International Nuclear Information System (INIS)

    Lin Min; Chen Tianlun

    2005-01-01

    A lattice model for a set of pulse-coupled integrate-and-fire neurons with small world structure is introduced. We find that our model displays the power-law behavior accompanied with the large-scale synchronized activities among the units. And the different connectivity topologies lead to different behaviors in models of integrate-and-fire neurons.

  5. Direct evidence for activity-dependent glucose phosphorylation in neurons with implications for the astrocyte-to-neuron lactate shuttle.

    Science.gov (United States)

    Patel, Anant B; Lai, James C K; Chowdhury, Golam M I; Hyder, Fahmeed; Rothman, Douglas L; Shulman, Robert G; Behar, Kevin L

    2014-04-08

    Previous (13)C magnetic resonance spectroscopy experiments have shown that over a wide range of neuronal activity, approximately one molecule of glucose is oxidized for every molecule of glutamate released by neurons and recycled through astrocytic glutamine. The measured kinetics were shown to agree with the stoichiometry of a hypothetical astrocyte-to-neuron lactate shuttle model, which predicted negligible functional neuronal uptake of glucose. To test this model, we measured the uptake and phosphorylation of glucose in nerve terminals isolated from rats infused with the glucose analog, 2-fluoro-2-deoxy-D-glucose (FDG) in vivo. The concentrations of phosphorylated FDG (FDG6P), normalized with respect to known neuronal metabolites, were compared in nerve terminals, homogenate, and cortex of anesthetized rats with and without bicuculline-induced seizures. The increase in FDG6P in nerve terminals agreed well with the increase in cortical neuronal glucose oxidation measured previously under the same conditions in vivo, indicating that direct uptake and oxidation of glucose in nerve terminals is substantial under resting and activated conditions. These results suggest that neuronal glucose-derived pyruvate is the major oxidative fuel for activated neurons, not lactate-derived from astrocytes, contradicting predictions of the original astrocyte-to-neuron lactate shuttle model under the range of study conditions.

  6. Progranulin gene delivery protects dopaminergic neurons in a mouse model of Parkinson's disease.

    Directory of Open Access Journals (Sweden)

    Jackalina M Van Kampen

    Full Text Available Parkinson's disease (PD is a progressive neurodegenerative disorder characterized by tremor, rigidity and akinesia/bradykinesia resulting from the progressive loss of nigrostriatal dopaminergic neurons. To date, only symptomatic treatment is available for PD patients, with no effective means of slowing or stopping the progression of the disease. Progranulin (PGRN is a 593 amino acid multifunction protein that is widely distributed throughout the CNS, localized primarily in neurons and microglia. PGRN has been demonstrated to be a potent regulator of neuroinflammation and also acts as an autocrine neurotrophic factor, important for long-term neuronal survival. Thus, enhancing PGRN expression may strengthen the cells resistance to disease. In the present study, we have used the 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP model of PD to investigate the possible use of PGRN gene delivery as a therapy for the prevention or treatment of PD. Viral vector delivery of the PGRN gene was an effective means of elevating PGRN expression in nigrostriatal neurons. When PGRN expression was elevated in the SNC, nigrostriatal neurons were protected from MPTP toxicity in mice, along with a preservation of striatal dopamine content and turnover. Further, protection of nigrostriatal neurons by PGRN gene therapy was accompanied by reductions in markers of MPTP-induced inflammation and apoptosis as well as a complete preservation of locomotor function. We conclude that PGRN gene therapy may have beneficial effects in the treatment of PD.

  7. Pervasive within-Mitochondrion Single-Nucleotide Variant Heteroplasmy as Revealed by Single-Mitochondrion Sequencing

    Directory of Open Access Journals (Sweden)

    Jacqueline Morris

    2017-12-01

    Full Text Available Summary: A number of mitochondrial diseases arise from single-nucleotide variant (SNV accumulation in multiple mitochondria. Here, we present a method for identification of variants present at the single-mitochondrion level in individual mouse and human neuronal cells, allowing for extremely high-resolution study of mitochondrial mutation dynamics. We identified extensive heteroplasmy between individual mitochondrion, along with three high-confidence variants in mouse and one in human that were present in multiple mitochondria across cells. The pattern of variation revealed by single-mitochondrion data shows surprisingly pervasive levels of heteroplasmy in inbred mice. Distribution of SNV loci suggests inheritance of variants across generations, resulting in Poisson jackpot lines with large SNV load. Comparison of human and mouse variants suggests that the two species might employ distinct modes of somatic segregation. Single-mitochondrion resolution revealed mitochondria mutational dynamics that we hypothesize to affect risk probabilities for mutations reaching disease thresholds. : Morris et al. use independent sequencing of multiple individual mitochondria from mouse and human brain cells to show high pervasiveness of mutations. The mutations are heteroplasmic within single mitochondria and within and between cells. These findings suggest mechanisms by which mutations accumulate over time, resulting in mitochondrial dysfunction and disease. Keywords: single mitochondrion, single cell, human neuron, mouse neuron, single-nucleotide variation

  8. Synaptic glutamate release by postnatal rat serotonergic neurons in microculture.

    Science.gov (United States)

    Johnson, M D

    1994-02-01

    Serotonergic neurons are thought to play a role in depression and obsessive compulsive disorder. However, their functional transmitter repertoire is incompletely known. To investigate this repertoire, intracellular recordings were obtained from 132 cytochemically identified rat mesopontine serotonergic neurons that had re-established synapses in microcultures. Approximately 60% of the neurons evoked excitatory glutamatergic potentials in themselves or in target neurons. Glutamatergic transmission was frequently observed in microcultures containing a solitary serotonergic neuron. Evidence for co-release of serotonin and glutamate from single raphe neurons was also obtained. However, evidence for gamma-aminobutyric acid release by serotonergic neurons was observed in only two cases. These findings indicate that many cultured serotonergic neurons form glutamatergic synapses and may explain several observations in slices and in vivo.

  9. Dynamics of Time Delay-Induced Multiple Synchronous Behaviors in Inhibitory Coupled Neurons

    Science.gov (United States)

    Gu, Huaguang; Zhao, Zhiguo

    2015-01-01

    The inhibitory synapse can induce synchronous behaviors different from the anti-phase synchronous behaviors, which have been reported in recent studies. In the present paper, synchronous behaviors are investigated in the motif model composed of reciprocal inhibitory coupled neurons with endogenous bursting and time delay. When coupling strength is weak, synchronous behavior appears at a single interval of time delay within a bursting period. When coupling strength is strong, multiple synchronous behaviors appear at different intervals of time delay within a bursting period. The different bursting patterns of synchronous behaviors, and time delays and coupling strengths that can induce the synchronous bursting patterns can be well interpreted by the dynamics of the endogenous bursting pattern of isolated neuron, which is acquired by the fast-slow dissection method, combined with the inhibitory coupling current. For an isolated neuron, when a negative impulsive current with suitable strength is applied at different phases of the bursting, multiple different bursting patterns can be induced. For a neuron in the motif, the inhibitory coupling current, of which the application time and strength is modulated by time delay and coupling strength, can cause single or multiple synchronous firing patterns like the negative impulsive current when time delay and coupling strength is suitable. The difference compared to the previously reported multiple synchronous behaviors that appear at time delays wider than a period of the endogenous firing is discussed. The results present novel examples of synchronous behaviors in the neuronal network with inhibitory synapses and provide a reasonable explanation. PMID:26394224

  10. Patch-clamp recordings of rat neurons from acute brain slices of the somatosensory cortex during magnetic stimulation.

    Science.gov (United States)

    Pashut, Tamar; Magidov, Dafna; Ben-Porat, Hana; Wolfus, Shuki; Friedman, Alex; Perel, Eli; Lavidor, Michal; Bar-Gad, Izhar; Yeshurun, Yosef; Korngreen, Alon

    2014-01-01

    Although transcranial magnetic stimulation (TMS) is a popular tool for both basic research and clinical applications, its actions on nerve cells are only partially understood. We have previously predicted, using compartmental modeling, that magnetic stimulation of central nervous system neurons depolarized the soma followed by initiation of an action potential in the initial segment of the axon. The simulations also predict that neurons with low current threshold are more susceptible to magnetic stimulation. Here we tested these theoretical predictions by combining in vitro patch-clamp recordings from rat brain slices with magnetic stimulation and compartmental modeling. In agreement with the modeling, our recordings demonstrate the dependence of magnetic stimulation-triggered action potentials on the type and state of the neuron and its orientation within the magnetic field. Our results suggest that the observed effects of TMS are deeply rooted in the biophysical properties of single neurons in the central nervous system and provide a framework both for interpreting existing TMS data and developing new simulation-based tools and therapies.

  11. Patch-clamp recordings of rat neurons from acute brain slices of the somatosensory cortex during magnetic stimulation

    Directory of Open Access Journals (Sweden)

    Tamar ePashut

    2014-06-01

    Full Text Available Although transcranial magnetic stimulation (TMS is a popular tool for both basic research and clinical applications, its actions on nerve cells are only partially understood. We have previously predicted, using compartmental modeling, that magnetic stimulation of central nervous system neurons depolarized the soma followed by initiation of an action potential in the initial segment of the axon. The simulations also predict that neurons with low current threshold are more susceptible to magnetic stimulation. Here we tested these theoretical predictions by combining in vitro patch-clamp recordings from rat brain slices with magnetic stimulation and compartmental modeling. In agreement with the modeling, our recordings demonstrate the dependence of magnetic stimulation-triggered action potentials on the type and state of the neuron and its orientation within the magnetic field. Our results suggest that the observed effects of TMS are deeply rooted in the biophysical properties of single neurons in the central nervous system and provide a framework both for interpreting existing TMS data and developing new simulation-based tools and therapies.

  12. Brains are not just neurons. Comment on “Toward a computational framework for cognitive biology: Unifying approaches from cognitive neuroscience and comparative cognition” by Fitch

    Science.gov (United States)

    Huber, Ludwig

    2014-09-01

    This comment addresses the first component of Fitch's framework: the computational power of single neurons [3]. Although I agree that traditional models of neural computation have vastly underestimated the computational power of single neurons, I am hesitant to follow him completely. The exclusive focus on neurons is likely to underestimate the importance of other cells in the brain. In the last years, two such cell types have received appropriate attention by neuroscientists: interneurons and glia. Interneurons are small, tightly packed cells involved in the control of information processing in learning and memory. Rather than transmitting externally (like motor or sensory neurons), these neurons process information within internal circuits of the brain (therefore also called 'relay neurons'). Some specialized interneuron subtypes temporally regulate the flow of information in a given cortical circuit during relevant behavioral events [4]. In the human brain approx. 100 billion interneurons control information processing and are implicated in disorders such as epilepsy and Parkinson's.

  13. Collective excitability in a mesoscopic neuronal model of epileptic activity

    Science.gov (United States)

    Jedynak, Maciej; Pons, Antonio J.; Garcia-Ojalvo, Jordi

    2018-01-01

    At the mesoscopic scale, the brain can be understood as a collection of interacting neuronal oscillators, but the extent to which its sustained activity is due to coupling among brain areas is still unclear. Here we address this issue in a simplified situation by examining the effect of coupling between two cortical columns described via Jansen-Rit neural mass models. Our results show that coupling between the two neuronal populations gives rise to stochastic initiations of sustained collective activity, which can be interpreted as epileptic events. For large enough coupling strengths, termination of these events results mainly from the emergence of synchronization between the columns, and thus it is controlled by coupling instead of noise. Stochastic triggering and noise-independent durations are characteristic of excitable dynamics, and thus we interpret our results in terms of collective excitability.

  14. Temporal structure of neuronal population oscillations with empirical model decomposition

    International Nuclear Information System (INIS)

    Li Xiaoli

    2006-01-01

    Frequency analysis of neuronal oscillation is very important for understanding the neural information processing and mechanism of disorder in the brain. This Letter addresses a new method to analyze the neuronal population oscillations with empirical mode decomposition (EMD). Following EMD of neuronal oscillation, a series of intrinsic mode functions (IMFs) are obtained, then Hilbert transform of IMFs can be used to extract the instantaneous time frequency structure of neuronal oscillation. The method is applied to analyze the neuronal oscillation in the hippocampus of epileptic rats in vivo, the results show the neuronal oscillations have different descriptions during the pre-ictal, seizure onset and ictal periods of the epileptic EEG at the different frequency band. This new method is very helpful to provide a view for the temporal structure of neural oscillation

  15. SELF-EXCITED WAVE PROCESSES IN CHAINS OF UNIDIRECTIONALLY COUPLED IMPULSE NEURONS

    Directory of Open Access Journals (Sweden)

    S. D. Glyzin

    2015-01-01

    Full Text Available The article is devoted to the mathematical modeling of neural activity. We propose new classes of singularly perturbed differential-difference equations with delay of Volterra type. With these systems, the models as a single neuron or neural networks are described. We study attractors of ring systems of unidirectionally coupled impulse neurons in the case where the number of links in the system increases indefinitely. In order to study periodic solutions of travelling wave type of this system, some special tricks are used which reduce the existence and stability problems for cycles to the investigation of auxiliary system with impulse actions. Using this approach, we establish that the number of stable self-excited waves simultaneously existing in the chain increases unboundedly as the number of links of the chain increases, that is, the well-known buffer phenomenon occurs.

  16. Small GSK-3 Inhibitor Shows Efficacy in a Motor Neuron Disease Murine Model Modulating Autophagy.

    Directory of Open Access Journals (Sweden)

    Estefanía de Munck

    Full Text Available Amyotrophic lateral sclerosis (ALS is a progressive motor neuron degenerative disease that has no effective treatment up to date. Drug discovery tasks have been hampered due to the lack of knowledge in its molecular etiology together with the limited animal models for research. Recently, a motor neuron disease animal model has been developed using β-N-methylamino-L-alanine (L-BMAA, a neurotoxic amino acid related to the appearing of ALS. In the present work, the neuroprotective role of VP2.51, a small heterocyclic GSK-3 inhibitor, is analysed in this novel murine model together with the analysis of autophagy. VP2.51 daily administration for two weeks, starting the first day after L-BMAA treatment, leads to total recovery of neurological symptoms and prevents the activation of autophagic processes in rats. These results show that the L-BMAA murine model can be used to test the efficacy of new drugs. In addition, the results confirm the therapeutic potential of GSK-3 inhibitors, and specially VP2.51, for the disease-modifying future treatment of motor neuron disorders like ALS.

  17. Recurrently connected and localized neuronal communities initiate coordinated spontaneous activity in neuronal networks

    Science.gov (United States)

    Amin, Hayder; Maccione, Alessandro; Nieus, Thierry

    2017-01-01

    Developing neuronal systems intrinsically generate coordinated spontaneous activity that propagates by involving a large number of synchronously firing neurons. In vivo, waves of spikes transiently characterize the activity of developing brain circuits and are fundamental for activity-dependent circuit formation. In vitro, coordinated spontaneous spiking activity, or network bursts (NBs), interleaved within periods of asynchronous spikes emerge during the development of 2D and 3D neuronal cultures. Several studies have investigated this type of activity and its dynamics, but how a neuronal system generates these coordinated events remains unclear. Here, we investigate at a cellular level the generation of network bursts in spontaneously active neuronal cultures by exploiting high-resolution multielectrode array recordings and computational network modelling. Our analysis reveals that NBs are generated in specialized regions of the network (functional neuronal communities) that feature neuronal links with high cross-correlation peak values, sub-millisecond lags and that share very similar structural connectivity motifs providing recurrent interactions. We show that the particular properties of these local structures enable locally amplifying spontaneous asynchronous spikes and that this mechanism can lead to the initiation of NBs. Through the analysis of simulated and experimental data, we also show that AMPA currents drive the coordinated activity, while NMDA and GABA currents are only involved in shaping the dynamics of NBs. Overall, our results suggest that the presence of functional neuronal communities with recurrent local connections allows a neuronal system to generate spontaneous coordinated spiking activity events. As suggested by the rules used for implementing our computational model, such functional communities might naturally emerge during network development by following simple constraints on distance-based connectivity. PMID:28749937

  18. Recurrently connected and localized neuronal communities initiate coordinated spontaneous activity in neuronal networks.

    Directory of Open Access Journals (Sweden)

    Davide Lonardoni

    2017-07-01

    Full Text Available Developing neuronal systems intrinsically generate coordinated spontaneous activity that propagates by involving a large number of synchronously firing neurons. In vivo, waves of spikes transiently characterize the activity of developing brain circuits and are fundamental for activity-dependent circuit formation. In vitro, coordinated spontaneous spiking activity, or network bursts (NBs, interleaved within periods of asynchronous spikes emerge during the development of 2D and 3D neuronal cultures. Several studies have investigated this type of activity and its dynamics, but how a neuronal system generates these coordinated events remains unclear. Here, we investigate at a cellular level the generation of network bursts in spontaneously active neuronal cultures by exploiting high-resolution multielectrode array recordings and computational network modelling. Our analysis reveals that NBs are generated in specialized regions of the network (functional neuronal communities that feature neuronal links with high cross-correlation peak values, sub-millisecond lags and that share very similar structural connectivity motifs providing recurrent interactions. We show that the particular properties of these local structures enable locally amplifying spontaneous asynchronous spikes and that this mechanism can lead to the initiation of NBs. Through the analysis of simulated and experimental data, we also show that AMPA currents drive the coordinated activity, while NMDA and GABA currents are only involved in shaping the dynamics of NBs. Overall, our results suggest that the presence of functional neuronal communities with recurrent local connections allows a neuronal system to generate spontaneous coordinated spiking activity events. As suggested by the rules used for implementing our computational model, such functional communities might naturally emerge during network development by following simple constraints on distance-based connectivity.

  19. Timing control by redundant inhibitory neuronal circuits

    International Nuclear Information System (INIS)

    Tristan, I.; Rulkov, N. F.; Huerta, R.; Rabinovich, M.

    2014-01-01

    Rhythms and timing control of sequential activity in the brain is fundamental to cognition and behavior. Although experimental and theoretical studies support the understanding that neuronal circuits are intrinsically capable of generating different time intervals, the dynamical origin of the phenomenon of functionally dependent timing control is still unclear. Here, we consider a new mechanism that is related to the multi-neuronal cooperative dynamics in inhibitory brain motifs consisting of a few clusters. It is shown that redundancy and diversity of neurons within each cluster enhances the sensitivity of the timing control with the level of neuronal excitation of the whole network. The generality of the mechanism is shown to work on two different neuronal models: a conductance-based model and a map-based model

  20. Timing control by redundant inhibitory neuronal circuits

    Energy Technology Data Exchange (ETDEWEB)

    Tristan, I., E-mail: itristan@ucsd.edu; Rulkov, N. F.; Huerta, R.; Rabinovich, M. [BioCircuits Institute, University of California, San Diego, La Jolla, California 92093-0402 (United States)

    2014-03-15

    Rhythms and timing control of sequential activity in the brain is fundamental to cognition and behavior. Although experimental and theoretical studies support the understanding that neuronal circuits are intrinsically capable of generating different time intervals, the dynamical origin of the phenomenon of functionally dependent timing control is still unclear. Here, we consider a new mechanism that is related to the multi-neuronal cooperative dynamics in inhibitory brain motifs consisting of a few clusters. It is shown that redundancy and diversity of neurons within each cluster enhances the sensitivity of the timing control with the level of neuronal excitation of the whole network. The generality of the mechanism is shown to work on two different neuronal models: a conductance-based model and a map-based model.

  1. Cerebellar Nuclear Neurons Use Time and Rate Coding to Transmit Purkinje Neuron Pauses.

    Science.gov (United States)

    Sudhakar, Shyam Kumar; Torben-Nielsen, Benjamin; De Schutter, Erik

    2015-12-01

    Neurons of the cerebellar nuclei convey the final output of the cerebellum to their targets in various parts of the brain. Within the cerebellum their direct upstream connections originate from inhibitory Purkinje neurons. Purkinje neurons have a complex firing pattern of regular spikes interrupted by intermittent pauses of variable length. How can the cerebellar nucleus process this complex input pattern? In this modeling study, we investigate different forms of Purkinje neuron simple spike pause synchrony and its influence on candidate coding strategies in the cerebellar nuclei. That is, we investigate how different alignments of synchronous pauses in synthetic Purkinje neuron spike trains affect either time-locking or rate-changes in the downstream nuclei. We find that Purkinje neuron synchrony is mainly represented by changes in the firing rate of cerebellar nuclei neurons. Pause beginning synchronization produced a unique effect on nuclei neuron firing, while the effect of pause ending and pause overlapping synchronization could not be distinguished from each other. Pause beginning synchronization produced better time-locking of nuclear neurons for short length pauses. We also characterize the effect of pause length and spike jitter on the nuclear neuron firing. Additionally, we find that the rate of rebound responses in nuclear neurons after a synchronous pause is controlled by the firing rate of Purkinje neurons preceding it.

  2. Cerebellar Nuclear Neurons Use Time and Rate Coding to Transmit Purkinje Neuron Pauses

    Science.gov (United States)

    Sudhakar, Shyam Kumar; Torben-Nielsen, Benjamin; De Schutter, Erik

    2015-01-01

    Neurons of the cerebellar nuclei convey the final output of the cerebellum to their targets in various parts of the brain. Within the cerebellum their direct upstream connections originate from inhibitory Purkinje neurons. Purkinje neurons have a complex firing pattern of regular spikes interrupted by intermittent pauses of variable length. How can the cerebellar nucleus process this complex input pattern? In this modeling study, we investigate different forms of Purkinje neuron simple spike pause synchrony and its influence on candidate coding strategies in the cerebellar nuclei. That is, we investigate how different alignments of synchronous pauses in synthetic Purkinje neuron spike trains affect either time-locking or rate-changes in the downstream nuclei. We find that Purkinje neuron synchrony is mainly represented by changes in the firing rate of cerebellar nuclei neurons. Pause beginning synchronization produced a unique effect on nuclei neuron firing, while the effect of pause ending and pause overlapping synchronization could not be distinguished from each other. Pause beginning synchronization produced better time-locking of nuclear neurons for short length pauses. We also characterize the effect of pause length and spike jitter on the nuclear neuron firing. Additionally, we find that the rate of rebound responses in nuclear neurons after a synchronous pause is controlled by the firing rate of Purkinje neurons preceding it. PMID:26630202

  3. Dissecting human cerebral organoids and fetal neocortex using single-cell RNAseq

    Science.gov (United States)

    Treutlein, Barbara

    Cerebral organoids - three-dimensional cultures of human cerebral tissue derived from pluripotent stem cells - have emerged as models of human cortical development. However, the extent to which in vitro organoid systems recapitulate neural progenitor cell proliferation and neuronal differentiation programs observed in vivo remains unclear. Here we use single-cell RNA sequencing (scRNA-seq) to dissect and compare cell composition and progenitor-to-neuron lineage relationships in human cerebral organoids and fetal neocortex. Covariation network analysis using the fetal neocortex data reveals known and novel interactions among genes central to neural progenitor proliferation and neuronal differentiation. In the organoid, we detect diverse progenitors and differentiated cell types of neuronal and mesenchymal lineages, and identify cells that derived from regions resembling the fetal neocortex. We find that these organoid cortical cells use gene expression programs remarkably similar to those of the fetal tissue in order to organize into cerebral cortex-like regions. Our comparison of in vivo and in vitro cortical single cell transcriptomes illuminates the genetic features underlying human cortical development that can be studied in organoid cultures.

  4. Replicating receptive fields of simple and complex cells in primary visual cortex in a neuronal network model with temporal and population sparseness and reliability.

    Science.gov (United States)

    Tanaka, Takuma; Aoyagi, Toshio; Kaneko, Takeshi

    2012-10-01

    We propose a new principle for replicating receptive field properties of neurons in the primary visual cortex. We derive a learning rule for a feedforward network, which maintains a low firing rate for the output neurons (resulting in temporal sparseness) and allows only a small subset of the neurons in the network to fire at any given time (resulting in population sparseness). Our learning rule also sets the firing rates of the output neurons at each time step to near-maximum or near-minimum levels, resulting in neuronal reliability. The learning rule is simple enough to be written in spatially and temporally local forms. After the learning stage is performed using input image patches of natural scenes, output neurons in the model network are found to exhibit simple-cell-like receptive field properties. When the output of these simple-cell-like neurons are input to another model layer using the same learning rule, the second-layer output neurons after learning become less sensitive to the phase of gratings than the simple-cell-like input neurons. In particular, some of the second-layer output neurons become completely phase invariant, owing to the convergence of the connections from first-layer neurons with similar orientation selectivity to second-layer neurons in the model network. We examine the parameter dependencies of the receptive field properties of the model neurons after learning and discuss their biological implications. We also show that the localized learning rule is consistent with experimental results concerning neuronal plasticity and can replicate the receptive fields of simple and complex cells.

  5. Selective increase of in vivo firing frequencies in DA SN neurons after proteasome inhibition in the ventral midbrain.

    Science.gov (United States)

    Subramaniam, Mahalakshmi; Kern, Beatrice; Vogel, Simone; Klose, Verena; Schneider, Gaby; Roeper, Jochen

    2014-09-01

    The impairment of protein degradation via the ubiquitin-proteasome system (UPS) is present in sporadic Parkinson's disease (PD), and might play a key role in selective degeneration of vulnerable dopamine (DA) neurons in the substantia nigra pars compacta (SN). Further evidence for a causal role of dysfunctional UPS in familial PD comes from mutations in parkin, which results in a loss of function of an E3-ubiquitin-ligase. In a mouse model, genetic inactivation of an essential component of the 26S proteasome lead to widespread neuronal degeneration including DA midbrain neurons and the formation of alpha-synuclein-positive inclusion bodies, another hallmark of PD. Studies using pharmacological UPS inhibition in vivo had more mixed results, varying from extensive degeneration to no loss of DA SN neurons. However, it is currently unknown whether UPS impairment will affect the neurophysiological functions of DA midbrain neurons. To answer this question, we infused a selective proteasome inhibitor into the ventral midbrain in vivo and recorded single DA midbrain neurons 2 weeks after the proteasome challenge. We found a selective increase in the mean in vivo firing frequencies of identified DA SN neurons in anesthetized mice, while those in the ventral tegmental area (VTA) were unaffected. Our results demonstrate that a single-hit UPS inhibition is sufficient to induce a stable and selective hyperexcitability phenotype in surviving DA SN neurons in vivo. This might imply that UPS dysfunction sensitizes DA SN neurons by enhancing 'stressful pacemaking'. © 2014 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

  6. Autapse-induced synchronization in a coupled neuronal network

    International Nuclear Information System (INIS)

    Ma, Jun; Song, Xinlin; Jin, Wuyin; Wang, Chuni

    2015-01-01

    Highlights: • The functional effect of autapse on neuronal activity is detected. • Autapse driving plays active role in regulating electrical activities as pacemaker. • It confirms biological experimental results for rhythm synchronization between heterogeneous cells. - Abstract: The effect of autapse on coupled neuronal network is detected. In our studies, three identical neurons are connected with ring type and autapse connected to one neuron of the network. The autapse connected to neuron can impose time-delayed feedback in close loop on the neuron thus the dynamics of membrane potentials can be changed. Firstly, the effect of autapse driving on single neuron is confirmed that negative feedback can calm down the neuronal activity while positive feedback can excite the neuronal activity. Secondly, the collective electrical behaviors of neurons are regulated by a pacemaker, which associated with the autapse forcing. By using appropriate gain and time delay in the autapse, the neurons can reach synchronization and the membrane potentials of all neurons can oscillate with the same rhythm under mutual coupling. It indicates that autapse forcing plays an important role in changing the collective electric activities of neuronal network, and appropriate electric modes can be selected due to the switch of feedback type(positive or negative) in autapse. And the autapse-induced synchronization in network is also consistent with some biological experiments about synchronization between nonidentical neurons.

  7. Molecular Logic of Neuronal Self-Recognition through Protocadherin Domain Interactions

    DEFF Research Database (Denmark)

    Rubinstein, Rotem; Thu, Chan Aye; Goodman, Kerry Marie

    2015-01-01

    Self-avoidance, a process preventing interactions of axons and dendrites from the same neuron during development, is mediated in vertebrates through the stochastic single-neuron expression of clustered protocadherin protein isoforms. Extracellular cadherin (EC) domains mediate isoform-specific ho...

  8. HCS-Neurons: identifying phenotypic changes in multi-neuron images upon drug treatments of high-content screening.

    Science.gov (United States)

    Charoenkwan, Phasit; Hwang, Eric; Cutler, Robert W; Lee, Hua-Chin; Ko, Li-Wei; Huang, Hui-Ling; Ho, Shinn-Ying

    2013-01-01

    High-content screening (HCS) has become a powerful tool for drug discovery. However, the discovery of drugs targeting neurons is still hampered by the inability to accurately identify and quantify the phenotypic changes of multiple neurons in a single image (named multi-neuron image) of a high-content screen. Therefore, it is desirable to develop an automated image analysis method for analyzing multi-neuron images. We propose an automated analysis method with novel descriptors of neuromorphology features for analyzing HCS-based multi-neuron images, called HCS-neurons. To observe multiple phenotypic changes of neurons, we propose two kinds of descriptors which are neuron feature descriptor (NFD) of 13 neuromorphology features, e.g., neurite length, and generic feature descriptors (GFDs), e.g., Haralick texture. HCS-neurons can 1) automatically extract all quantitative phenotype features in both NFD and GFDs, 2) identify statistically significant phenotypic changes upon drug treatments using ANOVA and regression analysis, and 3) generate an accurate classifier to group neurons treated by different drug concentrations using support vector machine and an intelligent feature selection method. To evaluate HCS-neurons, we treated P19 neurons with nocodazole (a microtubule depolymerizing drug which has been shown to impair neurite development) at six concentrations ranging from 0 to 1000 ng/mL. The experimental results show that all the 13 features of NFD have statistically significant difference with respect to changes in various levels of nocodazole drug concentrations (NDC) and the phenotypic changes of neurites were consistent to the known effect of nocodazole in promoting neurite retraction. Three identified features, total neurite length, average neurite length, and average neurite area were able to achieve an independent test accuracy of 90.28% for the six-dosage classification problem. This NFD module and neuron image datasets are provided as a freely downloadable

  9. Neuron-Type-Specific Utility in a Brain-Machine Interface: a Pilot Study.

    Science.gov (United States)

    Garcia-Garcia, Martha G; Bergquist, Austin J; Vargas-Perez, Hector; Nagai, Mary K; Zariffa, Jose; Marquez-Chin, Cesar; Popovic, Milos R

    2017-11-01

    Firing rates of single cortical neurons can be volitionally modulated through biofeedback (i.e. operant conditioning), and this information can be transformed to control external devices (i.e. brain-machine interfaces; BMIs). However, not all neurons respond to operant conditioning in BMI implementation. Establishing criteria that predict neuron utility will assist translation of BMI research to clinical applications. Single cortical neurons (n=7) were recorded extracellularly from primary motor cortex of a Long-Evans rat. Recordings were incorporated into a BMI involving up-regulation of firing rate to control the brightness of a light-emitting-diode and subsequent reward. Neurons were classified as 'fast-spiking', 'bursting' or 'regular-spiking' according to waveform-width and intrinsic firing patterns. Fast-spiking and bursting neurons were found to up-regulate firing rate by a factor of 2.43±1.16, demonstrating high utility, while regular-spiking neurons decreased firing rates on average by a factor of 0.73±0.23, demonstrating low utility. The ability to select neurons with high utility will be important to minimize training times and maximize information yield in future clinical BMI applications. The highly contrasting utility observed between fast-spiking and bursting neurons versus regular-spiking neurons allows for the hypothesis to be advanced that intrinsic electrophysiological properties may be useful criteria that predict neuron utility in BMI implementation.

  10. Characterization of three human cell line models for high-throughput neuronal cytotoxicity screening.

    Science.gov (United States)

    Tong, Zhi-Bin; Hogberg, Helena; Kuo, David; Sakamuru, Srilatha; Xia, Menghang; Smirnova, Lena; Hartung, Thomas; Gerhold, David

    2017-02-01

    More than 75 000 man-made chemicals contaminate the environment; many of these have not been tested for toxicities. These chemicals demand quantitative high-throughput screening assays to assess them for causative roles in neurotoxicities, including Parkinson's disease and other neurodegenerative disorders. To facilitate high throughput screening for cytotoxicity to neurons, three human neuronal cellular models were compared: SH-SY5Y neuroblastoma cells, LUHMES conditionally-immortalized dopaminergic neurons, and Neural Stem Cells (NSC) derived from human fetal brain. These three cell lines were evaluated for rapidity and degree of differentiation, and sensitivity to 32 known or candidate neurotoxicants. First, expression of neural differentiation genes was assayed during a 7-day differentiation period. Of the three cell lines, LUHMES showed the highest gene expression of neuronal markers after differentiation. Both in the undifferentiated state and after 7 days of neuronal differentiation, LUHMES cells exhibited greater cytotoxic sensitivity to most of 32 suspected or known neurotoxicants than SH-SY5Y or NSCs. LUHMES cells were also unique in being more susceptible to several compounds in the differentiating state than in the undifferentiated state; including known neurotoxicants colchicine, methyl-mercury (II), and vincristine. Gene expression results suggest that differentiating LUHMES cells may be susceptible to apoptosis because they express low levels of anti-apoptotic genes BCL2 and BIRC5/survivin, whereas SH-SY5Y cells may be resistant to apoptosis because they express high levels of BCL2, BIRC5/survivin, and BIRC3 genes. Thus, LUHMES cells exhibited favorable characteristics for neuro-cytotoxicity screening: rapid differentiation into neurons that exhibit high level expression neuronal marker genes, and marked sensitivity of LUHMES cells to known neurotoxicants. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

  11. I-123 iomazenil single photon emission computed tomography for detecting loss of neuronal integrity in patients with traumatic brain injury

    OpenAIRE

    Abiko, Kagari; Ikoma, Katsunori; Shiga, Tohru; Katoh, Chietsugu; Hirata, Kenji; Kuge, Yuji; Kobayashi, Kentaro; Tamaki, Nagara

    2017-01-01

    Background Traumatic brain injury (TBI) causes brain dysfunction in many patients. Using C-11 flumazenil (FMZ) positron emission tomography (PET), we have detected and reported the loss of neuronal integrity, leading to brain dysfunction in TBI patients. Similarly to FMZ PET, I-123 iomazenil (IMZ) single photon emission computed tomography (SPECT) is widely used to determine the distribution of the benzodiazepine receptor (BZR) in the brain cortex. The purpose of this study is to examine whet...

  12. Influence of Selective Edge Removal and Refractory Period in a Self-Organized Critical Neuron Model

    International Nuclear Information System (INIS)

    Lin Min; Gang, Zhao; Chen Tianlun

    2009-01-01

    A simple model for a set of integrate-and-fire neurons based on the weighted network is introduced. By considering the neurobiological phenomenon in brain development and the difference of the synaptic strength, we construct weighted networks develop with link additions and followed by selective edge removal. The network exhibits the small-world and scale-free properties with high network efficiency. The model displays an avalanche activity on a power-law distribution. We investigate the effect of selective edge removal and the neuron refractory period on the self-organized criticality of the system. (condensed matter: structural, mechanical, and thermal properties)

  13. Feedforward and feedback inhibition in neostriatal GABAergic spiny neurons.

    Science.gov (United States)

    Tepper, James M; Wilson, Charles J; Koós, Tibor

    2008-08-01

    There are two distinct inhibitory GABAergic circuits in the neostriatum. The feedforward circuit consists of a relatively small population of GABAergic interneurons that receives excitatory input from the neocortex and exerts monosynaptic inhibition onto striatal spiny projection neurons. The feedback circuit comprises the numerous spiny projection neurons and their interconnections via local axon collaterals. This network has long been assumed to provide the majority of striatal GABAergic inhibition and to sharpen and shape striatal output through lateral inhibition, producing increased activity in the most strongly excited spiny cells at the expense of their less strongly excited neighbors. Recent results, mostly from recording experiments of synaptically connected pairs of neurons, have revealed that the two GABAergic circuits differ markedly in terms of the total number of synapses made by each, the strength of the postsynaptic response detected at the soma, the extent of presynaptic convergence and divergence and the net effect of the activation of each circuit on the postsynaptic activity of the spiny neuron. These data have revealed that the feedforward inhibition is powerful and widespread, with spiking in a single interneuron being capable of significantly delaying or even blocking the generation of spikes in a large number of postsynaptic spiny neurons. In contrast, the postsynaptic effects of spiking in a single presynaptic spiny neuron on postsynaptic spiny neurons are weak when measured at the soma, and unable to significantly affect spike timing or generation. Further, reciprocity of synaptic connections between spiny neurons is only rarely observed. These results suggest that the bulk of the fast inhibition that has the strongest effects on spiny neuron spike timing comes from the feedforward interneuronal system whereas the axon collateral feedback system acts principally at the dendrites to control local excitability as well as the overall level of

  14. [The detector, the command neuron and plastic convergence].

    Science.gov (United States)

    Sokolov, E N

    1977-01-01

    The paper deals with the structure of detectors, the function of commanding neurones and the problem of relationship between detectors and commanding neurons. An example of hierarchial organization of detectors is provided by the colour analyser in which a layer of receptors, a layer of opponent neurones and a layer of colour-selective detectors are singled out. The colour detector is selectively sensitive to a certain combination of excitations at the input. If the detector is selectively activated by a certain combination of excitations at the input, the selective activation of the commanding neurone through a pool of motoneurones brings about a reaction at the output, specific in its organization. The reflexogenic zone of the reaction is determined by the detectors which converge on the commanding neurone controlling the given reaction. The plasticity of the reaction results from a plastic convergence of the detectors on the commanding neurone which controls the reaction. This comprises selective switching off the detectors from the commanding neurone (habituation) and connecting the detectors to the commanding neurone (facilitation).

  15. The neuronal and molecular basis of quinine-dependent bitter taste signaling in Drosophila larvae

    Science.gov (United States)

    Apostolopoulou, Anthi A.; Mazija, Lorena; Wüst, Alexander; Thum, Andreas S.

    2014-01-01

    The sensation of bitter substances can alert an animal that a specific type of food is harmful and should not be consumed. However, not all bitter compounds are equally toxic and some may even be beneficial in certain contexts. Thus, taste systems in general may have a broader range of functions than just in alerting the animal. In this study we investigate bitter sensing and processing in Drosophila larvae using quinine, a substance perceived by humans as bitter. We show that behavioral choice, feeding, survival, and associative olfactory learning are all directly affected by quinine. On the cellular level, we show that 12 gustatory sensory receptor neurons that express both GR66a and GR33a are required for quinine-dependent choice and feeding behavior. Interestingly, these neurons are not necessary for quinine-dependent survival or associative learning. On the molecular receptor gene level, the GR33a receptor, but not GR66a, is required for quinine-dependent choice behavior. A screen for gustatory sensory receptor neurons that trigger quinine-dependent choice behavior revealed that a single GR97a receptor gene expressing neuron located in the peripheral terminal sense organ is partially necessary and sufficient. For the first time, we show that the elementary chemosensory system of the Drosophila larva can serve as a simple model to understand the neuronal basis of taste information processing on the single cell level with respect to different behavioral outputs. PMID:24478653

  16. Hopf bifurcation of an (n + 1) -neuron bidirectional associative memory neural network model with delays.

    Science.gov (United States)

    Xiao, Min; Zheng, Wei Xing; Cao, Jinde

    2013-01-01

    Recent studies on Hopf bifurcations of neural networks with delays are confined to simplified neural network models consisting of only two, three, four, five, or six neurons. It is well known that neural networks are complex and large-scale nonlinear dynamical systems, so the dynamics of the delayed neural networks are very rich and complicated. Although discussing the dynamics of networks with a few neurons may help us to understand large-scale networks, there are inevitably some complicated problems that may be overlooked if simplified networks are carried over to large-scale networks. In this paper, a general delayed bidirectional associative memory neural network model with n + 1 neurons is considered. By analyzing the associated characteristic equation, the local stability of the trivial steady state is examined, and then the existence of the Hopf bifurcation at the trivial steady state is established. By applying the normal form theory and the center manifold reduction, explicit formulae are derived to determine the direction and stability of the bifurcating periodic solution. Furthermore, the paper highlights situations where the Hopf bifurcations are particularly critical, in the sense that the amplitude and the period of oscillations are very sensitive to errors due to tolerances in the implementation of neuron interconnections. It is shown that the sensitivity is crucially dependent on the delay and also significantly influenced by the feature of the number of neurons. Numerical simulations are carried out to illustrate the main results.

  17. Learning intrinsic excitability in medium spiny neurons [v2; ref status: indexed, http://f1000r.es/30b

    Directory of Open Access Journals (Sweden)

    Gabriele Scheler

    2014-02-01

    Full Text Available We present an unsupervised, local activation-dependent learning rule for intrinsic plasticity (IP which affects the composition of ion channel conductances for single neurons in a use-dependent way. We use a single-compartment conductance-based model for medium spiny striatal neurons in order to show the effects of parameterization of individual ion channels on the neuronal membrane potential-curent relationship (activation function. We show that parameter changes within the physiological ranges are sufficient to create an ensemble of neurons with significantly different activation functions. We emphasize that the effects of intrinsic neuronal modulation on spiking behavior require a distributed mode of synaptic input and can be eliminated by strongly correlated input. We show how modulation and adaptivity in ion channel conductances can be utilized to store patterns without an additional contribution by synaptic plasticity (SP. The adaptation of the spike response may result in either "positive" or "negative" pattern learning. However, read-out of stored information depends on a distributed pattern of synaptic activity to let intrinsic modulation determine spike response. We briefly discuss the implications of this conditional memory on learning and addiction.

  18. Calcitonin gene-related peptide alters the firing rates of hypothalamic temperature sensitive and insensitive neurons

    Directory of Open Access Journals (Sweden)

    Grimm Eleanor R

    2008-07-01

    Full Text Available Abstract Background Transient hyperthermic shifts in body temperature have been linked to the endogenous hormone calcitonin gene-related peptide (CGRP, which can increase sympathetic activation and metabolic heat production. Recent studies have demonstrated that these centrally mediated responses may result from CGRP dependent changes in the activity of thermoregulatory neurons in the preoptic and anterior regions of the hypothalamus (POAH. Results Using a tissue slice preparation, we recorded the single-unit activity of POAH neurons from the adult male rat, in response to temperature and CGRP (10 μM. Based on the slope of firing rate as a function of temperature, neurons were classified as either warm sensitive or temperature insensitive. All warm sensitive neurons responded to CGRP with a significant decrease in firing rate. While CGRP did not alter the firing rates of some temperature insensitive neurons, responsive neurons showed an increase in firing rate. Conclusion With respect to current models of thermoregulatory control, these CGRP dependent changes in firing rate would result in hyperthermia. This suggests that both warm sensitive and temperature insensitive neurons in the POAH may play a role in producing this hyperthermic shift in temperature.

  19. SIP-Based Single Neuron Stochastic Predictive Control for Non-Gaussian Networked Control Systems with Uncertain Metrology Delays

    Directory of Open Access Journals (Sweden)

    Xinying Xu

    2018-06-01

    Full Text Available In this paper, a novel data-driven single neuron predictive control strategy is proposed for non-Gaussian networked control systems with metrology delays in the information theory framework. Firstly, survival information potential (SIP, instead of minimum entropy, is used to formulate the performance index to characterize the randomness of the considered systems, which is calculated by oversampling method. Then the minimum values can be computed by optimizing the SIP-based performance index. Finally, the proposed strategy, minimum entropy method and mean square error (MSE are applied to a networked motor control system, and results demonstrated the effectiveness of the proposed strategy.

  20. Central auditory neurons have composite receptive fields.

    Science.gov (United States)

    Kozlov, Andrei S; Gentner, Timothy Q

    2016-02-02

    High-level neurons processing complex, behaviorally relevant signals are sensitive to conjunctions of features. Characterizing the receptive fields of such neurons is difficult with standard statistical tools, however, and the principles governing their organization remain poorly understood. Here, we demonstrate multiple distinct receptive-field features in individual high-level auditory neurons in a songbird, European starling, in response to natural vocal signals (songs). We then show that receptive fields with similar characteristics can be reproduced by an unsupervised neural network trained to represent starling songs with a single learning rule that enforces sparseness and divisive normalization. We conclude that central auditory neurons have composite receptive fields that can arise through a combination of sparseness and normalization in neural circuits. Our results, along with descriptions of random, discontinuous receptive fields in the central olfactory neurons in mammals and insects, suggest general principles of neural computation across sensory systems and animal classes.