WorldWideScience

Sample records for hodgkin-huxley neuron model

  1. Chimera states in a Hodgkin-Huxley model of thermally sensitive neurons

    Science.gov (United States)

    Glaze, Tera A.; Lewis, Scott; Bahar, Sonya

    2016-08-01

    Chimera states occur when identically coupled groups of nonlinear oscillators exhibit radically different dynamics, with one group exhibiting synchronized oscillations and the other desynchronized behavior. This dynamical phenomenon has recently been studied in computational models and demonstrated experimentally in mechanical, optical, and chemical systems. The theoretical basis of these states is currently under active investigation. Chimera behavior is of particular relevance in the context of neural synchronization, given the phenomenon of unihemispheric sleep and the recent observation of asymmetric sleep in human patients with sleep apnea. The similarity of neural chimera states to neural "bump" states, which have been suggested as a model for working memory and visual orientation tuning in the cortex, adds to their interest as objects of study. Chimera states have been demonstrated in the FitzHugh-Nagumo model of excitable cells and in the Hindmarsh-Rose neural model. Here, we demonstrate chimera states and chimera-like behaviors in a Hodgkin-Huxley-type model of thermally sensitive neurons both in a system with Abrams-Strogatz (mean field) coupling and in a system with Kuramoto (distance-dependent) coupling. We map the regions of parameter space for which chimera behavior occurs in each of the two coupling schemes.

  2. Unidirectional synchronization of Hodgkin-Huxley neurons

    Energy Technology Data Exchange (ETDEWEB)

    Cornejo-Perez, Octavio [Division de Matematicas Aplicadas y Sistemas, Computacionales, IPICYT, Apdo. Postal 3-74 Tangamanga, 78231 San Luis Potosi (Mexico)]. E-mail: octavio@ipicyt.edu.mx; Femat, Ricardo [Division de Matematicas Aplicadas y Sistemas, Computacionales, IPICYT, Apdo. Postal 3-74 Tangamanga, 78231 San Luis Potosi (Mexico)]. E-mail: rfemat@ipicyt.edu.mx

    2005-07-01

    Synchronization dynamics of two noiseless Hodgkin-Huxley (HH) neurons under the action of feedback control is studied. The spiking patterns of the action potentials evoked by periodic external modulations attain synchronization states under the feedback action. Numerical simulations for the synchronization dynamics of regular-irregular desynchronized spiking sequences are displayed. The results are discussed in context of generalized synchronization. It is also shown that the HH neurons can be synchronized in face of unmeasured states.

  3. [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.

  4. Canards Existence in FitzHugh-Nagumo and Hodgkin-Huxley Neuronal Models

    Directory of Open Access Journals (Sweden)

    Jean-Marc Ginoux

    2015-01-01

    Full Text Available In a previous paper we have proposed a new method for proving the existence of “canard solutions” for three- and four-dimensional singularly perturbed systems with only one fast variable which improves the methods used until now. The aim of this work is to extend this method to the case of four-dimensional singularly perturbed systems with two slow and two fast variables. This method enables stating a unique generic condition for the existence of “canard solutions” for such four-dimensional singularly perturbed systems which is based on the stability of folded singularities (pseudo singular points in this case of the normalized slow dynamics deduced from a well-known property of linear algebra. This unique generic condition is identical to that provided in previous works. Application of this method to the famous coupled FitzHugh-Nagumo equations and to the Hodgkin-Huxley model enables showing the existence of “canard solutions” in such systems.

  5. 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

  6. Teaching and learning the Hodgkin-Huxley model based on software developed in NEURON's programming language hoc.

    Science.gov (United States)

    Hernández, Oscar E; Zurek, Eduardo E

    2013-05-15

    We present a software tool called SENB, which allows the geometric and biophysical neuronal properties in a simple computational model of a Hodgkin-Huxley (HH) axon to be changed. The aim of this work is to develop a didactic and easy-to-use computational tool in the NEURON simulation environment, which allows graphical visualization of both the passive and active conduction parameters and the geometric characteristics of a cylindrical axon with HH properties. The SENB software offers several advantages for teaching and learning electrophysiology. First, SENB offers ease and flexibility in determining the number of stimuli. Second, SENB allows immediate and simultaneous visualization, in the same window and time frame, of the evolution of the electrophysiological variables. Third, SENB calculates parameters such as time and space constants, stimuli frequency, cellular area and volume, sodium and potassium equilibrium potentials, and propagation velocity of the action potentials. Furthermore, it allows the user to see all this information immediately in the main window. Finally, with just one click SENB can save an image of the main window as evidence. The SENB software is didactic and versatile, and can be used to improve and facilitate the teaching and learning of the underlying mechanisms in the electrical activity of an axon using the biophysical properties of the squid giant axon.

  7. The ISI distribution of the stochastic Hodgkin-Huxley neuron.

    Science.gov (United States)

    Rowat, Peter F; Greenwood, Priscilla E

    2014-01-01

    The simulation of ion-channel noise has an important role in computational neuroscience. In recent years several approximate methods of carrying out this simulation have been published, based on stochastic differential equations, and all giving slightly different results. The obvious, and essential, question is: which method is the most accurate and which is most computationally efficient? Here we make a contribution to the answer. We compare interspike interval histograms from simulated data using four different approximate stochastic differential equation (SDE) models of the stochastic Hodgkin-Huxley neuron, as well as the exact Markov chain model simulated by the Gillespie algorithm. One of the recent SDE models is the same as the Kurtz approximation first published in 1978. All the models considered give similar ISI histograms over a wide range of deterministic and stochastic input. Three features of these histograms are an initial peak, followed by one or more bumps, and then an exponential tail. We explore how these features depend on deterministic input and on level of channel noise, and explain the results using the stochastic dynamics of the model. We conclude with a rough ranking of the four SDE models with respect to the similarity of their ISI histograms to the histogram of the exact Markov chain model.

  8. Channel noise effects on first spike latency of a stochastic Hodgkin-Huxley neuron

    Science.gov (United States)

    Maisel, Brenton; Lindenberg, Katja

    2017-02-01

    While it is widely accepted that information is encoded in neurons via action potentials or spikes, it is far less understood what specific features of spiking contain encoded information. Experimental evidence has suggested that the timing of the first spike may be an energy-efficient coding mechanism that contains more neural information than subsequent spikes. Therefore, the biophysical features of neurons that underlie response latency are of considerable interest. Here we examine the effects of channel noise on the first spike latency of a Hodgkin-Huxley neuron receiving random input from many other neurons. Because the principal feature of a Hodgkin-Huxley neuron is the stochastic opening and closing of channels, the fluctuations in the number of open channels lead to fluctuations in the membrane voltage and modify the timing of the first spike. Our results show that when a neuron has a larger number of channels, (i) the occurrence of the first spike is delayed and (ii) the variation in the first spike timing is greater. We also show that the mean, median, and interquartile range of first spike latency can be accurately predicted from a simple linear regression by knowing only the number of channels in the neuron and the rate at which presynaptic neurons fire, but the standard deviation (i.e., neuronal jitter) cannot be predicted using only this information. We then compare our results to another commonly used stochastic Hodgkin-Huxley model and show that the more commonly used model overstates the first spike latency but can predict the standard deviation of first spike latencies accurately. We end by suggesting a more suitable definition for the neuronal jitter based upon our simulations and comparison of the two models.

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

    Directory of Open Access Journals (Sweden)

    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

  10. Crisis of interspike intervals in Hodgkin-Huxley model

    International Nuclear Information System (INIS)

    Jin Wuyin; Xu Jianxue; Wu Ying; Hong Ling; Wei Yaobing

    2006-01-01

    The bifurcations of the chaotic attractor in a Hodgkin-Huxley (H-H) model under stimulation of periodic signal is presented in this work, where the frequency of signal is taken as the controlling parameter. The chaotic behavior is realized over a wide range of frequency and is visualized by using interspike intervals (ISIs). Many kinds of abrupt undergoing changes of the ISIs are observed in different frequency regions, such as boundary crisis, interior crisis and merging crisis displaying alternately along with the changes of external signal frequency. And there are logistic-like bifurcation behaviors, e.g., periodic windows and fractal structures in ISIs dynamics. The saddle-node bifurcations resulting in collapses of chaos to period-6 orbit in dynamics of ISIs are identified

  11. Optimal decoding and information transmission in Hodgkin-Huxley neurons under metabolic cost constraints.

    Science.gov (United States)

    Kostal, Lubomir; Kobayashi, Ryota

    2015-10-01

    Information theory quantifies the ultimate limits on reliable information transfer by means of the channel capacity. However, the channel capacity is known to be an asymptotic quantity, assuming unlimited metabolic cost and computational power. We investigate a single-compartment Hodgkin-Huxley type neuronal model under the spike-rate coding scheme and address how the metabolic cost and the decoding complexity affects the optimal information transmission. We find that the sub-threshold stimulation regime, although attaining the smallest capacity, allows for the most efficient balance between the information transmission and the metabolic cost. Furthermore, we determine post-synaptic firing rate histograms that are optimal from the information-theoretic point of view, which enables the comparison of our results with experimental data. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

  12. Deriving Behaviour of Hodgkin Huxley model with fever dynamics: A computational study

    Directory of Open Access Journals (Sweden)

    Hasan ESKALEN

    2017-10-01

    Full Text Available A single neuron can be modeled by the set of differential equations. Hodgkin-Huxley (HH model, the one of the most famous neuron model, can be considered as a dynamical system with four independent variables. Here we studied to reduce the number of differential equation required for conductance based HH model under strong inhibitory noise. Exponential Integrate and Fire (EIF model, one independent variable, is used as a reduced model of HH model by using current-voltage (I-V curve of the original model. The required reduction parameters are determined from this curve. The behaviour of HH model and its reduced EIF (rEIF model are in good agreement in sub-threshold level. Above-threshold behaviour of reduced EIF model and original model compared in terms of threshold voltage under strong inhibitory noise. Our numerical simulations clearly show that sub-threshold behaviour of HH model perfectly reduced to rEIF model.

  13. 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.

  14. The Energy Coding of a Structural Neural Network Based on the Hodgkin-Huxley Model.

    Science.gov (United States)

    Zhu, Zhenyu; Wang, Rubin; Zhu, Fengyun

    2018-01-01

    Based on the Hodgkin-Huxley model, the present study established a fully connected structural neural network to simulate the neural activity and energy consumption of the network by neural energy coding theory. The numerical simulation result showed that the periodicity of the network energy distribution was positively correlated to the number of neurons and coupling strength, but negatively correlated to signal transmitting delay. Moreover, a relationship was established between the energy distribution feature and the synchronous oscillation of the neural network, which showed that when the proportion of negative energy in power consumption curve was high, the synchronous oscillation of the neural network was apparent. In addition, comparison with the simulation result of structural neural network based on the Wang-Zhang biophysical model of neurons showed that both models were essentially consistent.

  15. Synchronization of two Hodgkin-Huxley neurons due to internal noise

    International Nuclear Information System (INIS)

    Casado, Jose Manuel

    2003-01-01

    It is well known that a strong coupling can synchronize a population of nonlinear oscillators. This fact has deep implications for the current understanding of information processing by the brain. The focus of this Letter is on the role of conductance noise on a system of two coupled Hodgkin-Huxley neurons in the so-called excitable region, where both neurons are at rest in the absence of noise. It is shown that, in this region, conductance noise allows the neurons to achieve both frequency and phase synchronization. This suggests that internal noise could play a role in the emergence of synchronous neural activity in populations of weakly coupled neurons

  16. Synchronization of a coupled Hodgkin-Huxley neurons via high order sliding-mode feedback

    Energy Technology Data Exchange (ETDEWEB)

    Aguilar-Lopez, R. [Division de Ciencias Basicas e Ingenieria, Universidad Autonoma Metropolitana, Av. San Pablo No. 180, Reynosa-Tamaulipas, 02200 Azcapotzalco, Mexico, D.F. (Mexico)], E-mail: raguilar@correo.azc.uam.mx; Martinez-Guerra, R. [Departamento de Control Automatico, CINVESTAV-IPN, Apartado Postal 14-740, Mexico, D.F. C.P. 07360 (Mexico)], E-mail: rguerra@ctrl.cinvestav.mx

    2008-07-15

    This work deals with the synchronizations of two both coupled Hodgkin-Huxley (H-H) neurons, where the master neuron posses inner noise and the slave neuron is considered in a resting state, (without inner noise) and an exciting state (with inner noise). The synchronization procedure is done via a feedback control, considering a class of high order sliding-mode controller which provides chattering reduction and finite time synchronization convergence, with a satisfactory performance. Theoretical analysis is done in order to show the closed-loop stability of the proposed controller and the calculated finite time for convergence. The main results are illustrated via numerical experiments.

  17. Synchronization of a coupled Hodgkin-Huxley neurons via high order sliding-mode feedback

    International Nuclear Information System (INIS)

    Aguilar-Lopez, R.; Martinez-Guerra, R.

    2008-01-01

    This work deals with the synchronizations of two both coupled Hodgkin-Huxley (H-H) neurons, where the master neuron posses inner noise and the slave neuron is considered in a resting state, (without inner noise) and an exciting state (with inner noise). The synchronization procedure is done via a feedback control, considering a class of high order sliding-mode controller which provides chattering reduction and finite time synchronization convergence, with a satisfactory performance. Theoretical analysis is done in order to show the closed-loop stability of the proposed controller and the calculated finite time for convergence. The main results are illustrated via numerical experiments

  18. Delay-enhanced coherence of spiral waves in noisy Hodgkin-Huxley neuronal networks

    International Nuclear Information System (INIS)

    Wang Qingyun; Perc, Matjaz; Duan Zhisheng; Chen Guanrong

    2008-01-01

    We study the spatial dynamics of spiral waves in noisy Hodgkin-Huxley neuronal ensembles evoked by different information transmission delays and network topologies. In classical settings of coherence resonance the intensity of noise is fine-tuned so as to optimize the system's response. Here, we keep the noise intensity constant, and instead, vary the length of information transmission delay amongst coupled neurons. We show that there exists an intermediate transmission delay by which the spiral waves are optimally ordered, hence indicating the existence of delay-enhanced coherence of spatial dynamics in the examined system. Additionally, we examine the robustness of this phenomenon as the diffusive interaction topology changes towards the small-world type, and discover that shortcut links amongst distant neurons hinder the emergence of coherent spiral waves irrespective of transmission delay length. Presented results thus provide insights that could facilitate the understanding of information transmission delay on realistic neuronal networks

  19. Control of bursting synchronization in networks of Hodgkin-Huxley-type neurons with chemical synapses.

    Science.gov (United States)

    Batista, C A S; Viana, R L; Ferrari, F A S; Lopes, S R; Batista, A M; Coninck, J C P

    2013-04-01

    Thermally sensitive neurons present bursting activity for certain temperature ranges, characterized by fast repetitive spiking of action potential followed by a short quiescent period. Synchronization of bursting activity is possible in networks of coupled neurons, and it is sometimes an undesirable feature. Control procedures can suppress totally or partially this collective behavior, with potential applications in deep-brain stimulation techniques. We investigate the control of bursting synchronization in small-world networks of Hodgkin-Huxley-type thermally sensitive neurons with chemical synapses through two different strategies. One is the application of an external time-periodic electrical signal and another consists of a time-delayed feedback signal. We consider the effectiveness of both strategies in terms of protocols of applications suitable to be applied by pacemakers.

  20. Synchronization control of Hodgkin-Huxley neurons exposed to ELF electric field

    International Nuclear Information System (INIS)

    Che Yanqiu; Wang Jiang; Zhou Sisi; Deng Bin

    2009-01-01

    This paper presents an adaptive neural network H ∞ control for unidirectional synchronization of modified Hodgkin-Huxley (HH) neurons exposed to extremely low frequency (ELF) electric field. The proposed modified HH neurons exhibit periodic and chaotic dynamics in response to sinusoidal electric field stimulation. Based on the Lyapunov stability theory, we derive the updated laws of neural network for approximating the nonlinear uncertain functions of the error dynamical system. The H ∞ design technique makes the controller robust to unmodeled dynamics, disturbances and approximate errors. The proposed controller not only ensures closed-loop stability, but also guarantees an H ∞ performance for the synchronization error system. The states of the controlled slave system exponentially synchronize with that of the master one after control. The simulation results demonstrate the validity of the proposed method.

  1. Spike neural models (part I: The Hodgkin-Huxley model

    Directory of Open Access Journals (Sweden)

    Johnson, Melissa G.

    2017-05-01

    Full Text Available Artificial neural networks, or ANNs, have grown a lot since their inception back in the 1940s. But no matter the changes, one of the most important components of neural networks is still the node, which represents the neuron. Within spiking neural networks, the node is especially important because it contains the functions and properties of neurons that are necessary for their network. One important aspect of neurons is the ionic flow which produces action potentials, or spikes. Forces of diffusion and electrostatic pressure work together with the physical properties of the cell to move ions around changing the cell membrane potential which ultimately produces the action potential. This tutorial reviews the Hodkgin-Huxley model and shows how it simulates the ionic flow of the giant squid axon via four differential equations. The model is implemented in Matlab using Euler's Method to approximate the differential equations. By using Euler's method, an extra parameter is created, the time step. This new parameter needs to be carefully considered or the results of the node may be impaired.

  2. Anti-electromagnetic interference analysis of equivalent circuit of ion channel based on the Hodgkin-Huxley model

    International Nuclear Information System (INIS)

    Chu, J; Chang, X L; Zhao, M; Man, M H; Wei, M; Yuan, L

    2013-01-01

    With the continuous improvement of circuit integration and working clock frequency in the electronic system, it is increasingly easy for the system to be affected by electromagnetic waves, and electromagnetic susceptibility and vulnerability become more severe. However, living beings in nature have shown extraordinary compatibility, immunity and adaptability to the electromagnetism at the same time. In addition, the ion channel on the neuron cytomembrane is a typical representation of b ioelectrical immunity . So the Hodgkin-Huxley circuit model with one capacitor in parallel with some power supplies and resistors was adopted to simulate the ion channel on the neuron cytomembrane. Through analysis, the circuit model can be used to simulate some electrical characteristics of biological neuron cells, and then acquire a certain level of anti-electromagnetic interference ability. This method will be useful for improving the reliability, compatibility and anti-interference capability of the electronic system in the complicated electromagnetic environment.

  3. Spiral Wave in Small-World Networks of Hodgkin-Huxley Neurons

    International Nuclear Information System (INIS)

    Ma Jun; Zhang Cairong; Yang Lijian; Wu Ying

    2010-01-01

    The effect of small-world connection and noise on the formation and transition of spiral wave in the networks of Hodgkin-Huxley neurons are investigated in detail. Some interesting results are found in our numerical studies. i) The quiescent neurons are activated to propagate electric signal to others by generating and developing spiral wave from spiral seed in small area. ii) A statistical factor is defined to describe the collective properties and phase transition induced by the topology of networks and noise. iii) Stable rotating spiral wave can be generated and keeps robust when the rewiring probability is below certain threshold, otherwise, spiral wave can not be developed from the spiral seed and spiral wave breakup occurs for a stable rotating spiral wave. iv) Gaussian white noise is introduced on the membrane of neurons to study the noise-induced phase transition on spiral wave in small-world networks of neurons. It is confirmed that Gaussian white noise plays active role in supporting and developing spiral wave in the networks of neurons, and appearance of smaller factor of synchronization indicates high possibility to induce spiral wave. (interdisciplinary physics and related areas of science and technology)

  4. Optimal size of stochastic Hodgkin-Huxley neuronal systems for maximal energy efficiency in coding pulse signals

    Science.gov (United States)

    Yu, Lianchun; Liu, Liwei

    2014-03-01

    The generation and conduction of action potentials (APs) represents a fundamental means of communication in the nervous system and is a metabolically expensive process. In this paper, we investigate the energy efficiency of neural systems in transferring pulse signals with APs. By analytically solving a bistable neuron model that mimics the AP generation with a particle crossing the barrier of a double well, we find the optimal number of ion channels that maximizes the energy efficiency of a neuron. We also investigate the energy efficiency of a neuron population in which the input pulse signals are represented with synchronized spikes and read out with a downstream coincidence detector neuron. We find an optimal number of neurons in neuron population, as well as the number of ion channels in each neuron that maximizes the energy efficiency. The energy efficiency also depends on the characters of the input signals, e.g., the pulse strength and the interpulse intervals. These results are confirmed by computer simulation of the stochastic Hodgkin-Huxley model with a detailed description of the ion channel random gating. We argue that the tradeoff between signal transmission reliability and energy cost may influence the size of the neural systems when energy use is constrained.

  5. A path integral approach to the Hodgkin-Huxley model

    Science.gov (United States)

    Baravalle, Roman; Rosso, Osvaldo A.; Montani, Fernando

    2017-11-01

    To understand how single neurons process sensory information, it is necessary to develop suitable stochastic models to describe the response variability of the recorded spike trains. Spikes in a given neuron are produced by the synergistic action of sodium and potassium of the voltage-dependent channels that open or close the gates. Hodgkin and Huxley (HH) equations describe the ionic mechanisms underlying the initiation and propagation of action potentials, through a set of nonlinear ordinary differential equations that approximate the electrical characteristics of the excitable cell. Path integral provides an adequate approach to compute quantities such as transition probabilities, and any stochastic system can be expressed in terms of this methodology. We use the technique of path integrals to determine the analytical solution driven by a non-Gaussian colored noise when considering the HH equations as a stochastic system. The different neuronal dynamics are investigated by estimating the path integral solutions driven by a non-Gaussian colored noise q. More specifically we take into account the correlational structures of the complex neuronal signals not just by estimating the transition probability associated to the Gaussian approach of the stochastic HH equations, but instead considering much more subtle processes accounting for the non-Gaussian noise that could be induced by the surrounding neural network and by feedforward correlations. This allows us to investigate the underlying dynamics of the neural system when different scenarios of noise correlations are considered.

  6. Stochastic resonance on Newman-Watts networks of Hodgkin-Huxley neurons with local periodic driving

    Energy Technology Data Exchange (ETDEWEB)

    Ozer, Mahmut [Zonguldak Karaelmas University, Engineering Faculty, Department of Electrical and Electronics Engineering, 67100 Zonguldak (Turkey)], E-mail: mahmutozer2002@yahoo.com; Perc, Matjaz [University of Maribor, Faculty of Natural Sciences and Mathematics, Department of Physics, Koroska cesta 160, SI-2000 Maribor (Slovenia); Uzuntarla, Muhammet [Zonguldak Karaelmas University, Engineering Faculty, Department of Electrical and Electronics Engineering, 67100 Zonguldak (Turkey)

    2009-03-02

    We study the phenomenon of stochastic resonance on Newman-Watts small-world networks consisting of biophysically realistic Hodgkin-Huxley neurons with a tunable intensity of intrinsic noise via voltage-gated ion channels embedded in neuronal membranes. Importantly thereby, the subthreshold periodic driving is introduced to a single neuron of the network, thus acting as a pacemaker trying to impose its rhythm on the whole ensemble. We show that there exists an optimal intensity of intrinsic ion channel noise by which the outreach of the pacemaker extends optimally across the whole network. This stochastic resonance phenomenon can be further amplified via fine-tuning of the small-world network structure, and depends significantly also on the coupling strength among neurons and the driving frequency of the pacemaker. In particular, we demonstrate that the noise-induced transmission of weak localized rhythmic activity peaks when the pacemaker frequency matches the intrinsic frequency of subthreshold oscillations. The implications of our findings for weak signal detection and information propagation across neural networks are discussed.

  7. Spike trains in Hodgkin-Huxley model and ISIs of acupuncture manipulations

    International Nuclear Information System (INIS)

    Wang Jiang; Si Wenjie; Che Yanqiu; Fei Xiangyang

    2008-01-01

    The Hodgkin-Huxley equations (HH) are parameterized by a number of parameters and shows a variety of qualitatively different behaviors depending on the parameter values. Under stimulation of an external periodic voltage, the ISIs (interspike intervals) of a HH model are investigated in this work, while the frequency of the voltage is taken as the controlling parameter. As well-known, the science of acupuncture and moxibustion is an important component of Traditional Chinese Medicine with a long history. Although there are a number of different acupuncture manipulations, the method for distinguishing them is rarely investigated. With the idea of ISI, we study the electrical signal time series at the spinal dorsal horn produced by three different acupuncture manipulations in Zusanli point and present an effective way to distinguish them

  8. Spike trains in Hodgkin-Huxley model and ISIs of acupuncture manipulations

    Energy Technology Data Exchange (ETDEWEB)

    Wang Jiang [School of Electrical and Automation Engineering, Tianjin University, Tianjin 300072 (China)], E-mail: jiangwang@tju.edu.cn; Si Wenjie; Che Yanqiu; Fei Xiangyang [School of Electrical and Automation Engineering, Tianjin University, Tianjin 300072 (China)

    2008-05-15

    The Hodgkin-Huxley equations (HH) are parameterized by a number of parameters and shows a variety of qualitatively different behaviors depending on the parameter values. Under stimulation of an external periodic voltage, the ISIs (interspike intervals) of a HH model are investigated in this work, while the frequency of the voltage is taken as the controlling parameter. As well-known, the science of acupuncture and moxibustion is an important component of Traditional Chinese Medicine with a long history. Although there are a number of different acupuncture manipulations, the method for distinguishing them is rarely investigated. With the idea of ISI, we study the electrical signal time series at the spinal dorsal horn produced by three different acupuncture manipulations in Zusanli point and present an effective way to distinguish them.

  9. Identifying generalized Fitzhugh-Nagumo equation from a numerical solution of Hodgkin-Huxley model

    Directory of Open Access Journals (Sweden)

    Nikola V. Georgiev

    2003-01-01

    Full Text Available An analytic time series in the form of numerical solution (in an appropriate finite time interval of the Hodgkin-Huxley current clamped (HHCC system of four differential equations, well known in the neurophysiology as an exact empirical model of excitation of a giant axon of Loligo, is presented. Then we search for a second-order differential equation of generalized Fitzhugh-Nagumo (GFN type, having as a solution the given single component (action potential of the numerical solution. The given time series is used as a basis for reconstructing orders, powers, and coefficients of the polynomial right-hand sides of GFN equation approximately governing the process of action potential. For this purpose, a new geometrical method for determining phase space dimension of the unknown dynamical system (GFN equation and a specific modification of least squares method for identifying unknown coefficients are developed and applied.

  10. Phase-locking and chaos in a silent Hodgkin-Huxley neuron exposed to sinusoidal electric field

    International Nuclear Information System (INIS)

    Che Yanqiu; Wang Jiang; Si Wenjie; Fei Xiangyang

    2009-01-01

    Neuronal firing patterns are related to the information processing in neural system. This paper investigates the response characteristics of a silent Hodgkin-Huxley neuron to the stimulation of externally-applied sinusoidal electric field. The neuron exhibits both p:q phase-locked (i.e. a periodic oscillation defined as p action potentials generated by q cycle stimulations) and chaotic behaviors, depending on the values of stimulus frequencies and amplitudes. In one-parameter space, a rich bifurcation structure including period-adding without chaos and phase-locking alternated with chaos suggests frequency discrimination of the neuronal firing patterns. Furthermore, by mapping out Arnold tongues, we partition the amplitude-frequency parameter space in terms of the qualitative behaviors of the neuron. Thus the neuron's information (firing patterns) encodes the stimulus information (amplitude and frequency), and vice versa

  11. Saddle Slow Manifolds and Canard Orbits in [Formula: see text] and Application to the Full Hodgkin-Huxley Model.

    Science.gov (United States)

    Hasan, Cris R; Krauskopf, Bernd; Osinga, Hinke M

    2018-04-19

    Many physiological phenomena have the property that some variables evolve much faster than others. For example, neuron models typically involve observable differences in time scales. The Hodgkin-Huxley model is well known for explaining the ionic mechanism that generates the action potential in the squid giant axon. Rubin and Wechselberger (Biol. Cybern. 97:5-32, 2007) nondimensionalized this model and obtained a singularly perturbed system with two fast, two slow variables, and an explicit time-scale ratio ε. The dynamics of this system are complex and feature periodic orbits with a series of action potentials separated by small-amplitude oscillations (SAOs); also referred to as mixed-mode oscillations (MMOs). The slow dynamics of this system are organized by two-dimensional locally invariant manifolds called slow manifolds which can be either attracting or of saddle type.In this paper, we introduce a general approach for computing two-dimensional saddle slow manifolds and their stable and unstable fast manifolds. We also develop a technique for detecting and continuing associated canard orbits, which arise from the interaction between attracting and saddle slow manifolds, and provide a mechanism for the organization of SAOs in [Formula: see text]. We first test our approach with an extended four-dimensional normal form of a folded node. Our results demonstrate that our computations give reliable approximations of slow manifolds and canard orbits of this model. Our computational approach is then utilized to investigate the role of saddle slow manifolds and associated canard orbits of the full Hodgkin-Huxley model in organizing MMOs and determining the firing rates of action potentials. For ε sufficiently large, canard orbits are arranged in pairs of twin canard orbits with the same number of SAOs. We illustrate how twin canard orbits partition the attracting slow manifold into a number of ribbons that play the role of sectors of rotations. The upshot is that we

  12. Teaching and learning the Hodgkin-Huxley model based on software developed in NEURON’s programming language hoc

    Science.gov (United States)

    2013-01-01

    Background We present a software tool called SENB, which allows the geometric and biophysical neuronal properties in a simple computational model of a Hodgkin-Huxley (HH) axon to be changed. The aim of this work is to develop a didactic and easy-to-use computational tool in the NEURON simulation environment, which allows graphical visualization of both the passive and active conduction parameters and the geometric characteristics of a cylindrical axon with HH properties. Results The SENB software offers several advantages for teaching and learning electrophysiology. First, SENB offers ease and flexibility in determining the number of stimuli. Second, SENB allows immediate and simultaneous visualization, in the same window and time frame, of the evolution of the electrophysiological variables. Third, SENB calculates parameters such as time and space constants, stimuli frequency, cellular area and volume, sodium and potassium equilibrium potentials, and propagation velocity of the action potentials. Furthermore, it allows the user to see all this information immediately in the main window. Finally, with just one click SENB can save an image of the main window as evidence. Conclusions The SENB software is didactic and versatile, and can be used to improve and facilitate the teaching and learning of the underlying mechanisms in the electrical activity of an axon using the biophysical properties of the squid giant axon. PMID:23675833

  13. A study of quantum mechanical probabilities in the classical Hodgkin-Huxley model.

    Science.gov (United States)

    Moradi, N; Scholkmann, F; Salari, V

    2015-03-01

    The Hodgkin-Huxley (HH) model is a powerful model to explain different aspects of spike generation in excitable cells. However, the HH model was proposed in 1952 when the real structure of the ion channel was unknown. It is now common knowledge that in many ion-channel proteins the flow of ions through the pore is governed by a gate, comprising a so-called "selectivity filter" inside the ion channel, which can be controlled by electrical interactions. The selectivity filter (SF) is believed to be responsible for the selection and fast conduction of particular ions across the membrane of an excitable cell. Other (generally larger) parts of the molecule such as the pore-domain gate control the access of ions to the channel protein. In fact, two types of gates are considered here for ion channels: the "external gate", which is the voltage sensitive gate, and the "internal gate" which is the selectivity filter gate (SFG). Some quantum effects are expected in the SFG due to its small dimensions, which may play an important role in the operation of an ion channel. Here, we examine parameters in a generalized model of HH to see whether any parameter affects the spike generation. Our results indicate that the previously suggested semi-quantum-classical equation proposed by Bernroider and Summhammer (BS) agrees strongly with the HH equation under different conditions and may even provide a better explanation in some cases. We conclude that the BS model can refine the classical HH model substantially.

  14. Dynamics from seconds to hours in Hodgkin-Huxley model with time-dependent ion concentrations and buffer reservoirs.

    Directory of Open Access Journals (Sweden)

    Niklas Hübel

    2014-12-01

    Full Text Available The classical Hodgkin-Huxley (HH model neglects the time-dependence of ion concentrations in spiking dynamics. The dynamics is therefore limited to a time scale of milliseconds, which is determined by the membrane capacitance multiplied by the resistance of the ion channels, and by the gating time constants. We study slow dynamics in an extended HH framework that includes time-dependent ion concentrations, pumps, and buffers. Fluxes across the neuronal membrane change intra- and extracellular ion concentrations, whereby the latter can also change through contact to reservoirs in the surroundings. Ion gain and loss of the system is identified as a bifurcation parameter whose essential importance was not realized in earlier studies. Our systematic study of the bifurcation structure and thus the phase space structure helps to understand activation and inhibition of a new excitability in ion homeostasis which emerges in such extended models. Also modulatory mechanisms that regulate the spiking rate can be explained by bifurcations. The dynamics on three distinct slow times scales is determined by the cell volume-to-surface-area ratio and the membrane permeability (seconds, the buffer time constants (tens of seconds, and the slower backward buffering (minutes to hours. The modulatory dynamics and the newly emerging excitable dynamics corresponds to pathological conditions observed in epileptiform burst activity, and spreading depression in migraine aura and stroke, respectively.

  15. Dynamics from seconds to hours in Hodgkin-Huxley model with time-dependent ion concentrations and buffer reservoirs.

    Science.gov (United States)

    Hübel, Niklas; Dahlem, Markus A

    2014-12-01

    The classical Hodgkin-Huxley (HH) model neglects the time-dependence of ion concentrations in spiking dynamics. The dynamics is therefore limited to a time scale of milliseconds, which is determined by the membrane capacitance multiplied by the resistance of the ion channels, and by the gating time constants. We study slow dynamics in an extended HH framework that includes time-dependent ion concentrations, pumps, and buffers. Fluxes across the neuronal membrane change intra- and extracellular ion concentrations, whereby the latter can also change through contact to reservoirs in the surroundings. Ion gain and loss of the system is identified as a bifurcation parameter whose essential importance was not realized in earlier studies. Our systematic study of the bifurcation structure and thus the phase space structure helps to understand activation and inhibition of a new excitability in ion homeostasis which emerges in such extended models. Also modulatory mechanisms that regulate the spiking rate can be explained by bifurcations. The dynamics on three distinct slow times scales is determined by the cell volume-to-surface-area ratio and the membrane permeability (seconds), the buffer time constants (tens of seconds), and the slower backward buffering (minutes to hours). The modulatory dynamics and the newly emerging excitable dynamics corresponds to pathological conditions observed in epileptiform burst activity, and spreading depression in migraine aura and stroke, respectively.

  16. Resonant Activation in a Stochastic Hodgkin-Huxley Model: Interplay between noise and suprathreshold driving effect

    DEFF Research Database (Denmark)

    Pankratova, Evgeniya; Polovinkin, A.V.; Mosekilde, Erik

    2005-01-01

    The paper considers an excitable Hodgkin-Huxley system subjected to a strong periodic forcing in the presence of random noise. The influence of the forcing frequency on the response of the system is examined in the realm of suprathreshold amplitudes. Our results confirm that the presence of noise...... a minimum as functions of the forcing frequency. The destructive influence of noise on the interspike interval can also be reduced. With driving signals in a certain frequency range, the system can show stable periodic spiking even for relatively large noise intensities. Outside this frequency range, noise...... of similar intensity destroys the regularity of the spike trains by suppressing the generation of some of the spikes....

  17. From Knights Corner to Landing: a Case Study Based on a Hodgkin-Huxley Neuron Simulator

    OpenAIRE

    George Chatzikonstantis; Diego Jiménez; Esteban Meneses; Christos Strydis; Harry Sidiropoulos; Dimitrios Soudris

    2017-01-01

    Brain modeling has been presenting significant challenges to the world of high-performance computing (HPC) over the years. The field of computational neuroscience has been developing a demand for physiologically plausible neuron models, that feature increased complexity and thus, require greater computational power. We explore Intel’s newest generation of Xeon Phi computing platforms, named Knights Landing (KNL), as a way to match the need for processing power and as an upgrade over the previ...

  18. Effects of channel noise on firing coherence of small-world Hodgkin-Huxley neuronal networks

    Science.gov (United States)

    Sun, X. J.; Lei, J. Z.; Perc, M.; Lu, Q. S.; Lv, S. J.

    2011-01-01

    We investigate the effects of channel noise on firing coherence of Watts-Strogatz small-world networks consisting of biophysically realistic HH neurons having a fraction of blocked voltage-gated sodium and potassium ion channels embedded in their neuronal membranes. The intensity of channel noise is determined by the number of non-blocked ion channels, which depends on the fraction of working ion channels and the membrane patch size with the assumption of homogeneous ion channel density. We find that firing coherence of the neuronal network can be either enhanced or reduced depending on the source of channel noise. As shown in this paper, sodium channel noise reduces firing coherence of neuronal networks; in contrast, potassium channel noise enhances it. Furthermore, compared with potassium channel noise, sodium channel noise plays a dominant role in affecting firing coherence of the neuronal network. Moreover, we declare that the observed phenomena are independent of the rewiring probability.

  19. Numerical simulations of piecewise deterministic Markov processes with an application to the stochastic Hodgkin-Huxley model

    Science.gov (United States)

    Ding, Shaojie; Qian, Min; Qian, Hong; Zhang, Xuejuan

    2016-12-01

    The stochastic Hodgkin-Huxley model is one of the best-known examples of piecewise deterministic Markov processes (PDMPs), in which the electrical potential across a cell membrane, V(t), is coupled with a mesoscopic Markov jump process representing the stochastic opening and closing of ion channels embedded in the membrane. The rates of the channel kinetics, in turn, are voltage-dependent. Due to this interdependence, an accurate and efficient sampling of the time evolution of the hybrid stochastic systems has been challenging. The current exact simulation methods require solving a voltage-dependent hitting time problem for multiple path-dependent intensity functions with random thresholds. This paper proposes a simulation algorithm that approximates an alternative representation of the exact solution by fitting the log-survival function of the inter-jump dwell time, H(t), with a piecewise linear one. The latter uses interpolation points that are chosen according to the time evolution of the H(t), as the numerical solution to the coupled ordinary differential equations of V(t) and H(t). This computational method can be applied to all PDMPs. Pathwise convergence of the approximated sample trajectories to the exact solution is proven, and error estimates are provided. Comparison with a previous algorithm that is based on piecewise constant approximation is also presented.

  20. Coexistence between silent and bursting states in a biophysical Hodgkin-Huxley-type of model

    DEFF Research Database (Denmark)

    Stankevich, Nataliya; Mosekilde, Erik

    2017-01-01

    in a slightly modified, biophysical model that describe the dynamics of pancreatic beta-cells. To realize this form of coexistence, we have introduced an additional voltage-dependent potassium current that is activated in the region around the original, unstable equilibrium point. It is interesting to note...... that this modification also leads the model to display a blue-sky catastrophe in the transition region between chaotic and bursting states....

  1. Impact of Bounded Noise and Rewiring on the Formation and Instability of Spiral Waves in a Small-World Network of Hodgkin-Huxley Neurons.

    Science.gov (United States)

    Yao, Yuangen; Deng, Haiyou; Ma, Chengzhang; Yi, Ming; Ma, Jun

    2017-01-01

    Spiral waves are observed in the chemical, physical and biological systems, and the emergence of spiral waves in cardiac tissue is linked to some diseases such as heart ventricular fibrillation and epilepsy; thus it has importance in theoretical studies and potential medical applications. Noise is inevitable in neuronal systems and can change the electrical activities of neuron in different ways. Many previous theoretical studies about the impacts of noise on spiral waves focus an unbounded Gaussian noise and even colored noise. In this paper, the impacts of bounded noise and rewiring of network on the formation and instability of spiral waves are discussed in small-world (SW) network of Hodgkin-Huxley (HH) neurons through numerical simulations, and possible statistical analysis will be carried out. Firstly, we present SW network of HH neurons subjected to bounded noise. Then, it is numerically demonstrated that bounded noise with proper intensity σ, amplitude A, or frequency f can facilitate the formation of spiral waves when rewiring probability p is below certain thresholds. In other words, bounded noise-induced resonant behavior can occur in the SW network of neurons. In addition, rewiring probability p always impairs spiral waves, while spiral waves are confirmed to be robust for small p, thus shortcut-induced phase transition of spiral wave with the increase of p is induced. Furthermore, statistical factors of synchronization are calculated to discern the phase transition of spatial pattern, and it is confirmed that larger factor of synchronization is approached with increasing of rewiring probability p, and the stability of spiral wave is destroyed.

  2. On non-linear dynamics and an optimal control synthesis of the action potential of membranes (ideal and non-ideal cases) of the Hodgkin-Huxley (HH) mathematical model

    International Nuclear Information System (INIS)

    Chavarette, Fabio Roberto; Balthazar, Jose Manoel; Rafikov, Marat; Hermini, Helder Anibal

    2009-01-01

    In this paper, we have studied the plasmatic membrane behavior using an electric circuit developed by Hodgkin and Huxley in 1952 and have dealt with the variation of the amount of time related to the potassium and sodium conductances in the squid axon. They developed differential equations for the propagation of electric signals; the dynamics of the Hodgkin-Huxley model have been extensively studied both from the view point of its their biological implications and as a test bed for numerical methods, which can be applied to more complex models. Recently, an irregular chaotic movement of the action potential of the membrane was observed for a number of techniques of control with the objective to stabilize the variation of this potential. This paper analyzes the non-linear dynamics of the Hodgkin-Huxley mathematical model, and we present some modifications in the governing equations of the system in order to make it a non-ideal one (taking into account that the energy source has a limited power supply). We also developed an optimal linear control design for the action potential of membranes. Here, we discuss the conditions that allow the use of control linear feedback for this kind of non-linear system.

  3. The what and where of adding channel noise to the Hodgkin-Huxley equations.

    Directory of Open Access Journals (Sweden)

    Joshua H Goldwyn

    2011-11-01

    Full Text Available Conductance-based equations for electrically active cells form one of the most widely studied mathematical frameworks in computational biology. This framework, as expressed through a set of differential equations by Hodgkin and Huxley, synthesizes the impact of ionic currents on a cell's voltage--and the highly nonlinear impact of that voltage back on the currents themselves--into the rapid push and pull of the action potential. Later studies confirmed that these cellular dynamics are orchestrated by individual ion channels, whose conformational changes regulate the conductance of each ionic current. Thus, kinetic equations familiar from physical chemistry are the natural setting for describing conductances; for small-to-moderate numbers of channels, these will predict fluctuations in conductances and stochasticity in the resulting action potentials. At first glance, the kinetic equations provide a far more complex (and higher-dimensional description than the original Hodgkin-Huxley equations or their counterparts. This has prompted more than a decade of efforts to capture channel fluctuations with noise terms added to the equations of Hodgkin-Huxley type. Many of these approaches, while intuitively appealing, produce quantitative errors when compared to kinetic equations; others, as only very recently demonstrated, are both accurate and relatively simple. We review what works, what doesn't, and why, seeking to build a bridge to well-established results for the deterministic equations of Hodgkin-Huxley type as well as to more modern models of ion channel dynamics. As such, we hope that this review will speed emerging studies of how channel noise modulates electrophysiological dynamics and function. We supply user-friendly MATLAB simulation code of these stochastic versions of the Hodgkin-Huxley equations on the ModelDB website (accession number 138950 and http://www.amath.washington.edu/~etsb/tutorials.html.

  4. Efficient Spike-Coding with Multiplicative Adaptation in a Spike Response Model

    NARCIS (Netherlands)

    S.M. Bohte (Sander)

    2012-01-01

    htmlabstractNeural adaptation underlies the ability of neurons to maximize encoded informa- tion over a wide dynamic range of input stimuli. While adaptation is an intrinsic feature of neuronal models like the Hodgkin-Huxley model, the challenge is to in- tegrate adaptation in models of neural

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

    Directory of Open Access Journals (Sweden)

    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.

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. 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).

  11. 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)

  12. 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)

  13. 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.

  14. 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.

  15. Bistability induces episodic spike communication by inhibitory neurons in neuronal networks.

    Science.gov (United States)

    Kazantsev, V B; Asatryan, S Yu

    2011-09-01

    Bistability is one of the important features of nonlinear dynamical systems. In neurodynamics, bistability has been found in basic Hodgkin-Huxley equations describing the cell membrane dynamics. When the neuron is clamped near its threshold, the stable rest potential may coexist with the stable limit cycle describing periodic spiking. However, this effect is often neglected in network computations where the neurons are typically reduced to threshold firing units (e.g., integrate-and-fire models). We found that the bistability may induce spike communication by inhibitory coupled neurons in the spiking network. The communication is realized in the form of episodic discharges with synchronous (correlated) spikes during the episodes. A spiking phase map is constructed to describe the synchronization and to estimate basic spike phase locking modes.

  16. 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.

  17. Controlling Chaos in Neuron Based on Lasalle Invariance Principle

    International Nuclear Information System (INIS)

    Wei Duqu; Qin Yinghua

    2011-01-01

    A new control law is proposed to asymptotically stabilize the chaotic neuron system based on LaSalle invariant principle. The control technique does not require analytical knowledge of the system dynamics and operates without an explicit knowledge of the desired steady-state position. The well-known modified Hodgkin-Huxley (MHH) and Hindmarsh-Rose (HR) model neurons are taken as examples to verify the implementation of our method. Simulation results show the proposed control law is effective. The outcome of this study is significant since it is helpful to understand the learning process of a human brain towards the information processing, memory and abnormal discharge of the brain neurons. (general)

  18. 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)

  19. APPLICATION OF UKRAINIAN GRID INFRASTRUCTURE FOR INVESTIGATION OF NONLINEAR DYNAMICS IN LARGE NEURONAL NETWORKS

    Directory of Open Access Journals (Sweden)

    O. О. Sudakov

    2015-12-01

    Full Text Available In present work the Ukrainian National Grid (UNG infrastructure was applied for investigation of synchronization in large networks of interacting neurons. This application is important for solving of modern neuroscience problems related to mechanisms of nervous system activities (memory, cognition etc. and nervous pathologies (epilepsy, Parkinsonism, etc.. Modern non-linear dynamics theories and applications provides powerful basis for computer simulations of biological neuronal networks and investigation of phenomena which mechanisms hardly could be clarified by other approaches. Cubic millimeter of brain tissue contains about 105 neurons, so realistic (Hodgkin-Huxley model and phenomenological (Kuramoto-Sakaguchi, FitzHugh-Nagumo, etc. models simulations require consideration of large neurons numbers.

  20. Bistable dynamics underlying excitability of ion homeostasis in neuron models.

    Directory of Open Access Journals (Sweden)

    Niklas Hübel

    2014-05-01

    Full Text Available When neurons fire action potentials, dissipation of free energy is usually not directly considered, because the change in free energy is often negligible compared to the immense reservoir stored in neural transmembrane ion gradients and the long-term energy requirements are met through chemical energy, i.e., metabolism. However, these gradients can temporarily nearly vanish in neurological diseases, such as migraine and stroke, and in traumatic brain injury from concussions to severe injuries. We study biophysical neuron models based on the Hodgkin-Huxley (HH formalism extended to include time-dependent ion concentrations inside and outside the cell and metabolic energy-driven pumps. We reveal the basic mechanism of a state of free energy-starvation (FES with bifurcation analyses showing that ion dynamics is for a large range of pump rates bistable without contact to an ion bath. This is interpreted as a threshold reduction of a new fundamental mechanism of ionic excitability that causes a long-lasting but transient FES as observed in pathological states. We can in particular conclude that a coupling of extracellular ion concentrations to a large glial-vascular bath can take a role as an inhibitory mechanism crucial in ion homeostasis, while the Na⁺/K⁺ pumps alone are insufficient to recover from FES. Our results provide the missing link between the HH formalism and activator-inhibitor models that have been successfully used for modeling migraine phenotypes, and therefore will allow us to validate the hypothesis that migraine symptoms are explained by disturbed function in ion channel subunits, Na⁺/K⁺ pumps, and other proteins that regulate ion homeostasis.

  1. Chaos control and synchronization of two neurons exposed to ELF external electric field

    International Nuclear Information System (INIS)

    Wang Jiang; Zhang Ting; Che Yanqiu

    2007-01-01

    Chaos control and synchronization of two unidirectional coupled neurons exposed to ELF electrical field via nonlinear control technique is investigated. Based on results of space-time characteristics of trans-membrane voltage, the variation of cell trans-membrane voltage exposed to extremely low frequency (ELF) electric field is analyzed. The dynamical behaviors of the modified Hodgkin-Huxley (HH) model are identified under the periodic ELF electric field using both analytical and numerical analysis. Then, using the results of the analysis, a nonlinear feedback linearization control scheme and a modified adaptive control strategy are designed to synchronize the two unidirectional coupled neurons and stabilize the chaotic trajectory of the slave system to desired periodic orbit of the master system. The simulation results demonstrated the efficiency of the proposed algorithms

  2. Weak noise in neurons may powerfully inhibit the generation of repetitive spiking but not its propagation.

    Directory of Open Access Journals (Sweden)

    Henry C Tuckwell

    2010-05-01

    Full Text Available Many neurons have epochs in which they fire action potentials in an approximately periodic fashion. To see what effects noise of relatively small amplitude has on such repetitive activity we recently examined the response of the Hodgkin-Huxley (HH space-clamped system to such noise as the mean and variance of the applied current vary, near the bifurcation to periodic firing. This article is concerned with a more realistic neuron model which includes spatial extent. Employing the Hodgkin-Huxley partial differential equation system, the deterministic component of the input current is restricted to a small segment whereas the stochastic component extends over a region which may or may not overlap the deterministic component. For mean values below, near and above the critical values for repetitive spiking, the effects of weak noise of increasing strength is ascertained by simulation. As in the point model, small amplitude noise near the critical value dampens the spiking activity and leads to a minimum as noise level increases. This was the case for both additive noise and conductance-based noise. Uniform noise along the whole neuron is only marginally more effective in silencing the cell than noise which occurs near the region of excitation. In fact it is found that if signal and noise overlap in spatial extent, then weak noise may inhibit spiking. If, however, signal and noise are applied on disjoint intervals, then the noise has no effect on the spiking activity, no matter how large its region of application, though the trajectories are naturally altered slightly by noise. Such effects could not be discerned in a point model and are important for real neuron behavior. Interference with the spike train does nevertheless occur when the noise amplitude is larger, even when noise and signal do not overlap, being due to the instigation of secondary noise-induced wave phenomena rather than switching the system from one attractor (firing regularly to

  3. 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.

  4. 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

  5. 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.

  6. Simulations of the cardiac action potential based on the Hodgkin-Huxley kinetics with the use of Microsoft Excel spreadsheets.

    Science.gov (United States)

    Wu, Sheng-Nan

    2004-03-31

    The purpose of this study was to develop a method to simulate the cardiac action potential using a Microsoft Excel spreadsheet. The mathematical model contained voltage-gated ionic currents that were modeled using either Beeler-Reuter (B-R) or Luo-Rudy (L-R) phase 1 kinetics. The simulation protocol involves the use of in-cell formulas directly typed into a spreadsheet. The capability of spreadsheet iteration was used in these simulations. It does not require any prior knowledge of computer programming, although the use of the macro language can speed up the calculation. The normal configuration of the cardiac ventricular action potential can be well simulated in the B-R model that is defined by four individual ionic currents, each representing the diffusion of ions through channels in the membrane. The contribution of Na+ inward current to the rate of depolarization is reproduced in this model. After removal of Na+ current from the model, a constant current stimulus elicits an oscillatory change in membrane potential. In the L-R phase 1 model where six types of ionic currents were defined, the effect of extracellular K+ concentration on changes both in the time course of repolarization and in the time-independent K+ current can be demonstrated, when the solutions are implemented in Excel. Using the simulation protocols described here, the users can readily study and graphically display the underlying properties of ionic currents to see how changes in these properties determine the behavior of the heart cell. The method employed in these simulation protocols may also be extended or modified to other biological simulation programs.

  7. Mode-locking behavior of Izhikevich neurons under periodic external forcing

    Science.gov (United States)

    Farokhniaee, AmirAli; Large, Edward W.

    2017-06-01

    Many neurons in the auditory system of the brain must encode periodic signals. These neurons under periodic stimulation display rich dynamical states including mode locking and chaotic responses. Periodic stimuli such as sinusoidal waves and amplitude modulated sounds can lead to various forms of n :m mode-locked states, in which a neuron fires n action potentials per m cycles of the stimulus. Here, we study mode-locking in the Izhikevich neurons, a reduced model of the Hodgkin-Huxley neurons. The Izhikevich model is much simpler in terms of the dimension of the coupled nonlinear differential equations compared with other existing models, but excellent for generating the complex spiking patterns observed in real neurons. We obtained the regions of existence of the various mode-locked states on the frequency-amplitude plane, called Arnold tongues, for the Izhikevich neurons. Arnold tongue analysis provides useful insight into the organization of mode-locking behavior of neurons under periodic forcing. We find these tongues for both class-1 and class-2 excitable neurons in both deterministic and noisy regimes.

  8. A multiscale approach to modelling electrochemical processes occurring across the cell membrane with application to transmission of action potentials.

    Science.gov (United States)

    Richardson, G

    2009-09-01

    By application of matched asymptotic expansions, a simplified partial differential equation (PDE) model for the dynamic electrochemical processes occurring in the vicinity of a membrane, as ions selectively permeate across it, is formally derived from the Poisson-Nernst-Planck equations of electrochemistry. It is demonstrated that this simplified model reduces itself, in the limit of a long thin axon, to the cable equation used by Hodgkin and Huxley to describe the propagation of action potentials in the unmyelinated squid giant axon. The asymptotic reduction from the simplified PDE model to the cable equation leads to insights that are not otherwise apparent; these include an explanation of why the squid giant axon attains a diameter in the region of 1 mm. The simplified PDE model has more general application than the Hodgkin-Huxley cable equation and can, e.g. be used to describe action potential propagation in myelinated axons and neuronal cell bodies.

  9. Inverse stochastic resonance in networks of spiking neurons.

    Science.gov (United States)

    Uzuntarla, Muhammet; Barreto, Ernest; Torres, Joaquin J

    2017-07-01

    Inverse Stochastic Resonance (ISR) is a phenomenon in which the average spiking rate of a neuron exhibits a minimum with respect to noise. ISR has been studied in individual neurons, but here, we investigate ISR in scale-free networks, where the average spiking rate is calculated over the neuronal population. We use Hodgkin-Huxley model neurons with channel noise (i.e., stochastic gating variable dynamics), and the network connectivity is implemented via electrical or chemical connections (i.e., gap junctions or excitatory/inhibitory synapses). We find that the emergence of ISR depends on the interplay between each neuron's intrinsic dynamical structure, channel noise, and network inputs, where the latter in turn depend on network structure parameters. We observe that with weak gap junction or excitatory synaptic coupling, network heterogeneity and sparseness tend to favor the emergence of ISR. With inhibitory coupling, ISR is quite robust. We also identify dynamical mechanisms that underlie various features of this ISR behavior. Our results suggest possible ways of experimentally observing ISR in actual neuronal systems.

  10. Assimilation of Biophysical Neuronal Dynamics in Neuromorphic VLSI.

    Science.gov (United States)

    Wang, Jun; Breen, Daniel; Akinin, Abraham; Broccard, Frederic; Abarbanel, Henry D I; Cauwenberghs, Gert

    2017-12-01

    Representing the biophysics of neuronal dynamics and behavior offers a principled analysis-by-synthesis approach toward understanding mechanisms of nervous system functions. We report on a set of procedures assimilating and emulating neurobiological data on a neuromorphic very large scale integrated (VLSI) circuit. The analog VLSI chip, NeuroDyn, features 384 digitally programmable parameters specifying for 4 generalized Hodgkin-Huxley neurons coupled through 12 conductance-based chemical synapses. The parameters also describe reversal potentials, maximal conductances, and spline regressed kinetic functions for ion channel gating variables. In one set of experiments, we assimilated membrane potential recorded from one of the neurons on the chip to the model structure upon which NeuroDyn was designed using the known current input sequence. We arrived at the programmed parameters except for model errors due to analog imperfections in the chip fabrication. In a related set of experiments, we replicated songbird individual neuron dynamics on NeuroDyn by estimating and configuring parameters extracted using data assimilation from intracellular neural recordings. Faithful emulation of detailed biophysical neural dynamics will enable the use of NeuroDyn as a tool to probe electrical and molecular properties of functional neural circuits. Neuroscience applications include studying the relationship between molecular properties of neurons and the emergence of different spike patterns or different brain behaviors. Clinical applications include studying and predicting effects of neuromodulators or neurodegenerative diseases on ion channel kinetics.

  11. Transition Dynamics of a Dentate Gyrus-CA3 Neuronal Network during Temporal Lobe Epilepsy.

    Science.gov (United States)

    Zhang, Liyuan; Fan, Denggui; Wang, Qingyun

    2017-01-01

    In temporal lobe epilepsy (TLE), the variation of chemical receptor expression underlies the basis of neural network activity shifts, resulting in neuronal hyperexcitability and epileptiform discharges. However, dynamical mechanisms involved in the transitions of TLE are not fully understood, because of the neuronal diversity and the indeterminacy of network connection. Hence, based on Hodgkin-Huxley (HH) type neurons and Pinsky-Rinzel (PR) type neurons coupling with glutamatergic and GABAergic synaptic connections respectively, we propose a computational framework which contains dentate gyrus (DG) region and CA3 region. By regulating the concentration range of N-methyl-D-aspartate-type glutamate receptor (NMDAR), we demonstrate the pyramidal neuron can generate transitions from interictal to seizure discharges. This suggests that enhanced endogenous activity of NMDAR contributes to excitability in pyramidal neuron. Moreover, we conclude that excitatory discharges in CA3 region vary considerably on account of the excitatory currents produced by the excitatory pyramidal neuron. Interestingly, by changing the backprojection connection, we find that glutamatergic type backprojection can promote the dominant frequency of firings and further motivate excitatory counterpropagation from CA3 region to DG region. However, GABAergic type backprojection can reduce firing rate and block morbid counterpropagation, which may be factored into the terminations of TLE. In addition, neuronal diversity dominated network shows weak correlation with different backprojections. Our modeling and simulation studies provide new insights into the mechanisms of seizures generation and connectionism in local hippocampus, along with the synaptic mechanisms of this disease.

  12. Modeling the cellular mechanisms and olfactory input underlying the triphasic response of moth pheromone-sensitive projection neurons.

    Directory of Open Access Journals (Sweden)

    Yuqiao Gu

    Full Text Available In the antennal lobe of the noctuid moth Agrotis ipsilon, most pheromone-sensitive projection neurons (PNs exhibit a triphasic firing pattern of excitation (E1-inhibition (I-excitation (E2 in response to a pulse of the sex pheromone. To understand the mechanisms underlying this stereotypical discharge, we developed a biophysical model of a PN receiving inputs from olfactory receptor neurons (ORNs via nicotinic cholinergic synapses. The ORN is modeled as an inhomogeneous Poisson process whose firing rate is a function of time and is fitted to extracellular data recorded in response to pheromone stimulations at various concentrations and durations. The PN model is based on the Hodgkin-Huxley formalism with realistic ionic currents whose parameters were derived from previous studies. Simulations revealed that the inhibitory phase I can be produced by a SK current (Ca2+-gated small conductance K+ current and that the excitatory phase E2 can result from the long-lasting response of the ORNs. Parameter analysis further revealed that the ending time of E1 depends on some parameters of SK, Ca2+, nACh and Na+ currents; I duration mainly depends on the time constant of intracellular Ca2+ dynamics, conductance of Ca2+ currents and some parameters of nACh currents; The mean firing frequency of E1 and E2 depends differentially on the interaction of various currents. Thus it is likely that the interplay between PN intrinsic currents and feedforward synaptic currents are sufficient to generate the triphasic firing patterns observed in the noctuid moth A. ipsilon.

  13. Modelled temperature-dependent excitability behaviour of a generalised human peripheral sensory nerve fibre

    CSIR Research Space (South Africa)

    Smit, Jacoba E

    2009-09-01

    Full Text Available The objective of this study was to determine if a recently developed human Ranvier node model, which is based on a modified version of the Hodgkin-Huxley model, could predict the excitability behaviour in human peripheral sensory nerve fibres...

  14. Modeling removal of accumulated potassium from T-tubules by inward rectifier potassium channels

    NARCIS (Netherlands)

    Wallinga, W.; Vliek, M.; Wienk, E.D.; Alberink, M.J.; Ypey, D.L.; Ypey, D.L.

    1996-01-01

    The membrane models of Cannon et al. (1993) and Alberink et al. (1995) for mammalian skeletal muscle fibers are based upon Hodgkin-Huxley descriptions of sodium, potassium delayed rectifier and leak conductances and the capacitive current taking into account fast inactivation of sodium channels. Now

  15. Correlated conductance parameters in leech heart motor neurons contribute to motor pattern formation.

    Science.gov (United States)

    Lamb, Damon G; Calabrese, Ronald L

    2013-01-01

    Neurons can have widely differing intrinsic membrane properties, in particular the density of specific conductances, but how these contribute to characteristic neuronal activity or pattern formation is not well understood. To explore the relationship between conductances, and in particular how they influence the activity of motor neurons in the well characterized leech heartbeat system, we developed a new multi-compartmental Hodgkin-Huxley style leech heart motor neuron model. To do so, we evolved a population of model instances, which differed in the density of specific conductances, capable of achieving specific output activity targets given an associated input pattern. We then examined the sensitivity of measures of output activity to conductances and how the model instances responded to hyperpolarizing current injections. We found that the strengths of many conductances, including those with differing dynamics, had strong partial correlations and that these relationships appeared to be linked by their influence on heart motor neuron activity. Conductances that had positive correlations opposed one another and had the opposite effects on activity metrics when perturbed whereas conductances that had negative correlations could compensate for one another and had similar effects on activity metrics.

  16. Model-based analysis and control of a network of basal ganglia spiking neurons in the normal and Parkinsonian states

    Science.gov (United States)

    Liu, Jianbo; Khalil, Hassan K.; Oweiss, Karim G.

    2011-08-01

    Controlling the spatiotemporal firing pattern of an intricately connected network of neurons through microstimulation is highly desirable in many applications. We investigated in this paper the feasibility of using a model-based approach to the analysis and control of a basal ganglia (BG) network model of Hodgkin-Huxley (HH) spiking neurons through microstimulation. Detailed analysis of this network model suggests that it can reproduce the experimentally observed characteristics of BG neurons under a normal and a pathological Parkinsonian state. A simplified neuronal firing rate model, identified from the detailed HH network model, is shown to capture the essential network dynamics. Mathematical analysis of the simplified model reveals the presence of a systematic relationship between the network's structure and its dynamic response to spatiotemporally patterned microstimulation. We show that both the network synaptic organization and the local mechanism of microstimulation can impose tight constraints on the possible spatiotemporal firing patterns that can be generated by the microstimulated network, which may hinder the effectiveness of microstimulation to achieve a desired objective under certain conditions. Finally, we demonstrate that the feedback control design aided by the mathematical analysis of the simplified model is indeed effective in driving the BG network in the normal and Parskinsonian states to follow a prescribed spatiotemporal firing pattern. We further show that the rhythmic/oscillatory patterns that characterize a dopamine-depleted BG network can be suppressed as a direct consequence of controlling the spatiotemporal pattern of a subpopulation of the output Globus Pallidus internalis (GPi) neurons in the network. This work may provide plausible explanations for the mechanisms underlying the therapeutic effects of deep brain stimulation (DBS) in Parkinson's disease and pave the way towards a model-based, network level analysis and closed

  17. Switching neuronal state: optimal stimuli revealed using a stochastically-seeded gradient algorithm.

    Science.gov (United States)

    Chang, Joshua; Paydarfar, David

    2014-12-01

    Inducing a switch in neuronal state using energy optimal stimuli is relevant to a variety of problems in neuroscience. Analytical techniques from optimal control theory can identify such stimuli; however, solutions to the optimization problem using indirect variational approaches can be elusive in models that describe neuronal behavior. Here we develop and apply a direct gradient-based optimization algorithm to find stimulus waveforms that elicit a change in neuronal state while minimizing energy usage. We analyze standard models of neuronal behavior, the Hodgkin-Huxley and FitzHugh-Nagumo models, to show that the gradient-based algorithm: (1) enables automated exploration of a wide solution space, using stochastically generated initial waveforms that converge to multiple locally optimal solutions; and (2) finds optimal stimulus waveforms that achieve a physiological outcome condition, without a priori knowledge of the optimal terminal condition of all state variables. Analysis of biological systems using stochastically-seeded gradient methods can reveal salient dynamical mechanisms underlying the optimal control of system behavior. The gradient algorithm may also have practical applications in future work, for example, finding energy optimal waveforms for therapeutic neural stimulation that minimizes power usage and diminishes off-target effects and damage to neighboring tissue.

  18. Action potential energy efficiency varies among neuron types in vertebrates and invertebrates.

    Directory of Open Access Journals (Sweden)

    Biswa Sengupta

    2010-07-01

    Full Text Available The initiation and propagation of action potentials (APs places high demands on the energetic resources of neural tissue. Each AP forces ATP-driven ion pumps to work harder to restore the ionic concentration gradients, thus consuming more energy. Here, we ask whether the ionic currents underlying the AP can be predicted theoretically from the principle of minimum energy consumption. A long-held supposition that APs are energetically wasteful, based on theoretical analysis of the squid giant axon AP, has recently been overturned by studies that measured the currents contributing to the AP in several mammalian neurons. In the single compartment models studied here, AP energy consumption varies greatly among vertebrate and invertebrate neurons, with several mammalian neuron models using close to the capacitive minimum of energy needed. Strikingly, energy consumption can increase by more than ten-fold simply by changing the overlap of the Na(+ and K(+ currents during the AP without changing the APs shape. As a consequence, the height and width of the AP are poor predictors of energy consumption. In the Hodgkin-Huxley model of the squid axon, optimizing the kinetics or number of Na(+ and K(+ channels can whittle down the number of ATP molecules needed for each AP by a factor of four. In contrast to the squid AP, the temporal profile of the currents underlying APs of some mammalian neurons are nearly perfectly matched to the optimized properties of ionic conductances so as to minimize the ATP cost.

  19. Electrosensory Midbrain Neurons Display Feature Invariant Responses to Natural Communication Stimuli.

    Directory of Open Access Journals (Sweden)

    Tristan Aumentado-Armstrong

    2015-10-01

    Full Text Available Neurons that respond selectively but in an invariant manner to a given feature of natural stimuli have been observed across species and systems. Such responses emerge in higher brain areas, thereby suggesting that they occur by integrating afferent input. However, the mechanisms by which such integration occurs are poorly understood. Here we show that midbrain electrosensory neurons can respond selectively and in an invariant manner to heterogeneity in behaviorally relevant stimulus waveforms. Such invariant responses were not seen in hindbrain electrosensory neurons providing afferent input to these midbrain neurons, suggesting that response invariance results from nonlinear integration of such input. To test this hypothesis, we built a model based on the Hodgkin-Huxley formalism that received realistic afferent input. We found that multiple combinations of parameter values could give rise to invariant responses matching those seen experimentally. Our model thus shows that there are multiple solutions towards achieving invariant responses and reveals how subthreshold membrane conductances help promote robust and invariant firing in response to heterogeneous stimulus waveforms associated with behaviorally relevant stimuli. We discuss the implications of our findings for the electrosensory and other systems.

  20. Electrosensory Midbrain Neurons Display Feature Invariant Responses to Natural Communication Stimuli.

    Science.gov (United States)

    Aumentado-Armstrong, Tristan; Metzen, Michael G; Sproule, Michael K J; Chacron, Maurice J

    2015-10-01

    Neurons that respond selectively but in an invariant manner to a given feature of natural stimuli have been observed across species and systems. Such responses emerge in higher brain areas, thereby suggesting that they occur by integrating afferent input. However, the mechanisms by which such integration occurs are poorly understood. Here we show that midbrain electrosensory neurons can respond selectively and in an invariant manner to heterogeneity in behaviorally relevant stimulus waveforms. Such invariant responses were not seen in hindbrain electrosensory neurons providing afferent input to these midbrain neurons, suggesting that response invariance results from nonlinear integration of such input. To test this hypothesis, we built a model based on the Hodgkin-Huxley formalism that received realistic afferent input. We found that multiple combinations of parameter values could give rise to invariant responses matching those seen experimentally. Our model thus shows that there are multiple solutions towards achieving invariant responses and reveals how subthreshold membrane conductances help promote robust and invariant firing in response to heterogeneous stimulus waveforms associated with behaviorally relevant stimuli. We discuss the implications of our findings for the electrosensory and other systems.

  1. Bi-directional astrocytic regulation of neuronal activity within a network

    Directory of Open Access Journals (Sweden)

    Susan Yu Gordleeva

    2012-11-01

    Full Text Available The concept of a tripartite synapse holds that astrocytes can affect both the pre- and postsynaptic compartments through the Ca2+-dependent release of gliotransmitters. Because astrocytic Ca2+ transients usually last for a few seconds, we assumed that astrocytic regulation of synaptic transmission may also occur on the scale of seconds. Here, we considered the basic physiological functions of tripartite synapses and investigated astrocytic regulation at the level of neural network activity. The firing dynamics of individual neurons in a spontaneous firing network was described by the Hodgkin-Huxley model. The neurons received excitatory synaptic input driven by the Poisson spike train with variable frequency. The mean field concentration of the released neurotransmitter was used to describe the presynaptic dynamics. The amplitudes of the excitatory postsynaptic currents (PSCs obeyed the gamma distribution law. In our model, astrocytes depressed the presynaptic release and enhanced the postsynaptic currents. As a result, low frequency synaptic input was suppressed while high frequency input was amplified. The analysis of the neuron spiking frequency as an indicator of network activity revealed that tripartite synaptic transmission dramatically changed the local network operation compared to bipartite synapses. Specifically, the astrocytes supported homeostatic regulation of the network activity by increasing or decreasing firing of the neurons. Thus, the astrocyte activation may modulate a transition of neural network into bistable regime of activity with two stable firing levels and spontaneous transitions between them.

  2. Emulating the electrical activity of the neuron using a silicon oxide RRAM cell

    Directory of Open Access Journals (Sweden)

    Adnan eMehonic

    2016-02-01

    Full Text Available In recent years, formidable effort has been devoted to exploring the potential of Resistive RAM (RRAM devices to model key features of biological synapses. This is done to strengthen the link between neuro-computing architectures and neuroscience, bearing in mind the extremely low power consumption and immense parallelism of biological systems. Here we demonstrate the feasibility of using the RRAM cell to go further and to model aspects of the electrical activity of the neuron. We focus on the specific operational procedures required for the generation of controlled voltage transients, which resemble spike-like responses. Further, we demonstrate that RRAM devices are capable of integrating input current pulses over time to produce thresholded voltage transients. We show that the frequency of the output transients can be controlled by the input signal, and we relate recent models of the redox-based nanoionic resistive memory cell to two common neuronal models, the Hodgkin-Huxley (HH conductance model and the leaky integrate-and-fire model. We employ a simplified circuit model to phenomenologically describe voltage transient generation.

  3. Emulating the Electrical Activity of the Neuron Using a Silicon Oxide RRAM Cell

    Science.gov (United States)

    Mehonic, Adnan; Kenyon, Anthony J.

    2016-01-01

    In recent years, formidable effort has been devoted to exploring the potential of Resistive RAM (RRAM) devices to model key features of biological synapses. This is done to strengthen the link between neuro-computing architectures and neuroscience, bearing in mind the extremely low power consumption and immense parallelism of biological systems. Here we demonstrate the feasibility of using the RRAM cell to go further and to model aspects of the electrical activity of the neuron. We focus on the specific operational procedures required for the generation of controlled voltage transients, which resemble spike-like responses. Further, we demonstrate that RRAM devices are capable of integrating input current pulses over time to produce thresholded voltage transients. We show that the frequency of the output transients can be controlled by the input signal, and we relate recent models of the redox-based nanoionic resistive memory cell to two common neuronal models, the Hodgkin-Huxley (HH) conductance model and the leaky integrate-and-fire model. We employ a simplified circuit model to phenomenologically describe voltage transient generation. PMID:26941598

  4. Calcium current activation kinetics in isolated pyramidal neurones of the Ca1 region of the mature guinea-pig hippocampus.

    Science.gov (United States)

    Kay, A R; Wong, R K

    1987-11-01

    1. Neurones were isolated from the CA1 region of the guinea-pig hippocampus and subjected to the whole-cell mode of voltage clamping, to determine the kinetics of voltage-gated Ca2+ channel activation. 2. Isolated neurones had an abbreviated morphology, having lost most of the distal dendritic tree during the isolation procedure. The electrical compactness of the cells facilitates voltage clamp analysis. 3. Block of sodium and potassium currents revealed a persistent current activated on depolarization above -40 mV, which inactivated slowly when the intracellular medium contained EGTA. The current was blocked by Co2+ and Cd2+, augmented by increases in Ca2+ and could be carried by Ba2+, suggesting that the current is borne by Ca2+. 4. Steady-state activation of the Ca2+ current was found to be well described by the Boltzman equation raised to the second power. 5. The open channel's current-voltage (I-V) relationship rectified in the inward direction and was consistent with the constant-field equation. 6. The kinetics of Ca2+ current onset followed m2 kinetics throughout the range of its activation. Tail current kinetics were in accord with this model. A detailed Hodgkin-Huxley model was derived, defining the activation of this current. 7. The kinetics of the currents observed in this regionally and morphologically defined class of neurones were consistent with the existence of a single kinetic class of channels.

  5. Effects of the network structure and coupling strength on the noise-induced response delay of a neuronal network

    International Nuclear Information System (INIS)

    Ozer, Mahmut; Uzuntarla, Muhammet

    2008-01-01

    The Hodgkin-Huxley (H-H) neuron model driven by stimuli just above threshold shows a noise-induced response delay with respect to time to the first spike for a certain range of noise strengths, an effect called 'noise delayed decay' (NDD). We study the response time of a network of coupled H-H neurons, and investigate how the NDD can be affected by the connection topology of the network and the coupling strength. We show that the NDD effect exists for weak and intermediate coupling strengths, whereas it disappears for strong coupling strength regardless of the connection topology. We also show that although the network structure has very little effect on the NDD for a weak coupling strength, the network structure plays a key role for an intermediate coupling strength by decreasing the NDD effect with the increasing number of random shortcuts, and thus provides an additional operating regime, that is absent in the regular network, in which the neurons may also exploit a spike time code

  6. Top-down inputs enhance orientation selectivity in neurons of the primary visual cortex during perceptual learning.

    Directory of Open Access Journals (Sweden)

    Samat Moldakarimov

    2014-08-01

    Full Text Available Perceptual learning has been used to probe the mechanisms of cortical plasticity in the adult brain. Feedback projections are ubiquitous in the cortex, but little is known about their role in cortical plasticity. Here we explore the hypothesis that learning visual orientation discrimination involves learning-dependent plasticity of top-down feedback inputs from higher cortical areas, serving a different function from plasticity due to changes in recurrent connections within a cortical area. In a Hodgkin-Huxley-based spiking neural network model of visual cortex, we show that modulation of feedback inputs to V1 from higher cortical areas results in shunting inhibition in V1 neurons, which changes the response properties of V1 neurons. The orientation selectivity of V1 neurons is enhanced without changing orientation preference, preserving the topographic organizations in V1. These results provide new insights to the mechanisms of plasticity in the adult brain, reconciling apparently inconsistent experiments and providing a new hypothesis for a functional role of the feedback connections.

  7. Impact of adaptation currents on synchronization of coupled exponential integrate-and-fire neurons.

    Directory of Open Access Journals (Sweden)

    Josef Ladenbauer

    Full Text Available The ability of spiking neurons to synchronize their activity in a network depends on the response behavior of these neurons as quantified by the phase response curve (PRC and on coupling properties. The PRC characterizes the effects of transient inputs on spike timing and can be measured experimentally. Here we use the adaptive exponential integrate-and-fire (aEIF neuron model to determine how subthreshold and spike-triggered slow adaptation currents shape the PRC. Based on that, we predict how synchrony and phase locked states of coupled neurons change in presence of synaptic delays and unequal coupling strengths. We find that increased subthreshold adaptation currents cause a transition of the PRC from only phase advances to phase advances and delays in response to excitatory perturbations. Increased spike-triggered adaptation currents on the other hand predominantly skew the PRC to the right. Both adaptation induced changes of the PRC are modulated by spike frequency, being more prominent at lower frequencies. Applying phase reduction theory, we show that subthreshold adaptation stabilizes synchrony for pairs of coupled excitatory neurons, while spike-triggered adaptation causes locking with a small phase difference, as long as synaptic heterogeneities are negligible. For inhibitory pairs synchrony is stable and robust against conduction delays, and adaptation can mediate bistability of in-phase and anti-phase locking. We further demonstrate that stable synchrony and bistable in/anti-phase locking of pairs carry over to synchronization and clustering of larger networks. The effects of adaptation in aEIF neurons on PRCs and network dynamics qualitatively reflect those of biophysical adaptation currents in detailed Hodgkin-Huxley-based neurons, which underscores the utility of the aEIF model for investigating the dynamical behavior of networks. Our results suggest neuronal spike frequency adaptation as a mechanism synchronizing low frequency

  8. Impact of bounded noise on the formation and instability of spiral wave in a 2D Lattice of neurons

    Science.gov (United States)

    Yao, Yuangen; Deng, Haiyou; Yi, Ming; Ma, Jun

    2017-02-01

    Spiral waves in the neocortex may provide a spatial framework to organize cortical oscillations, thus help signal communication. However, noise influences spiral wave. Many previous theoretical studies about noise mainly focus on unbounded Gaussian noise, which contradicts that a real physical quantity is always bounded. Furthermore, non-Gaussian noise is also important for dynamical behaviors of excitable media. Nevertheless, there are no results concerning the effect of bounded noise on spiral wave till now. Based on Hodgkin-Huxley neuron model subjected to bounded noise with the form of Asin[ωt + σW(t)], the influences of bounded noise on the formation and instability of spiral wave in a two-dimensional (2D) square lattice of neurons are investigated in detail by separately adjusting the intensity σ, amplitude A, and frequency f of bounded noise. It is found that the increased intensity σ can facilitate the formation of spiral wave while the increased amplitude A tends to destroy spiral wave. Furthermore, frequency of bounded noise has the effect of facilitation or inhibition on pattern synchronization. Interestingly, for the appropriate intensity, amplitude and frequency can separately induce resonance-like phenomenon.

  9. Spike Neural Models Part II: Abstract Neural Models

    Directory of Open Access Journals (Sweden)

    Johnson, Melissa G.

    2018-02-01

    Full Text Available Neurons are complex cells that require a lot of time and resources to model completely. In spiking neural networks (SNN though, not all that complexity is required. Therefore simple, abstract models are often used. These models save time, use less computer resources, and are easier to understand. This tutorial presents two such models: Izhikevich's model, which is biologically realistic in the resulting spike trains but not in the parameters, and the Leaky Integrate and Fire (LIF model which is not biologically realistic but does quickly and easily integrate input to produce spikes. Izhikevich's model is based on Hodgkin-Huxley's model but simplified such that it uses only two differentiation equations and four parameters to produce various realistic spike patterns. LIF is based on a standard electrical circuit and contains one equation. Either of these two models, or any of the many other models in literature can be used in a SNN. Choosing a neural model is an important task that depends on the goal of the research and the resources available. Once a model is chosen, network decisions such as connectivity, delay, and sparseness, need to be made. Understanding neural models and how they are incorporated into the network is the first step in creating a SNN.

  10. [Effect of pulse magnetic field on distribution of neuronal action potential].

    Science.gov (United States)

    Zheng, Yu; Cai, Di; Wang, Jin-Hai; Li, Gang; Lin, Ling

    2014-08-25

    The biological effect on the organism generated by magnetic field is widely studied. The present study was aimed to observe the change of sodium channel under magnetic field in neurons. Cortical neurons of Kunming mice were isolated, subjected to 15 Hz, 1 mT pulse magnetic stimulation, and then the currents of neurons were recorded by whole-cell patch clamp. The results showed that, under magnetic stimulation, the activation process of Na(+) channel was delayed, and the inactivation process was accelerated. Given the classic three-layer model, the polarization diagram of cell membrane potential distribution under pulse magnetic field was simulated, and it was found that the membrane potential induced was associated with the frequency and intensity of magnetic field. Also the effect of magnetic field-induced current on action potential was simulated by Hodgkin-Huxley (H-H) model. The result showed that the generation of action potential was delayed, and frequency and the amplitudes were decreased when working current was between -1.32 μA and 0 μA. When the working current was higher than 0 μA, the generation frequency of action potential was increased, and the change of amplitudes was not obvious, and when the working current was lower than -1.32 μA, the time of rising edge and amplitudes of action potential were decreased drastically, and the action potential was unable to generate. These results suggest that the magnetic field simulation can affect the distribution frequency and amplitude of action potential of neuron via sodium channel mediation.

  11. Physiological models of the lateral superior olive.

    Directory of Open Access Journals (Sweden)

    Go Ashida

    2017-12-01

    Full Text Available In computational biology, modeling is a fundamental tool for formulating, analyzing and predicting complex phenomena. Most neuron models, however, are designed to reproduce certain small sets of empirical data. Hence their outcome is usually not compatible or comparable with other models or datasets, making it unclear how widely applicable such models are. In this study, we investigate these aspects of modeling, namely credibility and generalizability, with a specific focus on auditory neurons involved in the localization of sound sources. The primary cues for binaural sound localization are comprised of interaural time and level differences (ITD/ILD, which are the timing and intensity differences of the sound waves arriving at the two ears. The lateral superior olive (LSO in the auditory brainstem is one of the locations where such acoustic information is first computed. An LSO neuron receives temporally structured excitatory and inhibitory synaptic inputs that are driven by ipsi- and contralateral sound stimuli, respectively, and changes its spike rate according to binaural acoustic differences. Here we examine seven contemporary models of LSO neurons with different levels of biophysical complexity, from predominantly functional ones ('shot-noise' models to those with more detailed physiological components (variations of integrate-and-fire and Hodgkin-Huxley-type. These models, calibrated to reproduce known monaural and binaural characteristics of LSO, generate largely similar results to each other in simulating ITD and ILD coding. Our comparisons of physiological detail, computational efficiency, predictive performances, and further expandability of the models demonstrate (1 that the simplistic, functional LSO models are suitable for applications where low computational costs and mathematical transparency are needed, (2 that more complex models with detailed membrane potential dynamics are necessary for simulation studies where sub-neuronal

  12. N2A: a computational tool for modeling from neurons to algorithms

    Directory of Open Access Journals (Sweden)

    Fredrick eRothganger

    2014-01-01

    Full Text Available The exponential increase in available neural data has combined with the exponential growth in computing (Moore’s law to create new opportunities to understand neural systems at large scale and high detail. The ability to produce large and sophisticated simulations has introduced unique challenges to neuroscientists. Computational models in neuroscience are increasingly broad efforts, often involving the collaboration of experts in different domains. Furthermore, the size and detail of models have grown to levels for which understanding the implications of variability and assumptions is no longer trivial. Here, we introduce the model design platform N2A which aims to facilitate the design and validation of biologically realistic models. N2A uses a hierarchical representation of neural information to enable the integration of models from different users. N2A streamlines computational validation of a model by natively implementing standard tools in sensitivity analysis and uncertainty quantification. The part-relationship representation allows both network-level analysis and dynamical simulations. We will demonstrate how N2A can be used in a range of examples, including a simple Hodgkin-Huxley cable model, basic parameter sensitivity of an 80/20 network, and the expression of the structural plasticity of a growing dendrite and stem cell proliferation and differentiation.

  13. The role of glutamate in neuronal ion homeostasis: A case study of spreading depolarization.

    Directory of Open Access Journals (Sweden)

    Niklas Hübel

    2017-10-01

    Full Text Available Simultaneous changes in ion concentrations, glutamate, and cell volume together with exchange of matter between cell network and vasculature are ubiquitous in numerous brain pathologies. A complete understanding of pathological conditions as well as normal brain function, therefore, hinges on elucidating the molecular and cellular pathways involved in these mostly interdependent variations. In this paper, we develop the first computational framework that combines the Hodgkin-Huxley type spiking dynamics, dynamic ion concentrations and glutamate homeostasis, neuronal and astroglial volume changes, and ion exchange with vasculature into a comprehensive model to elucidate the role of glutamate uptake in the dynamics of spreading depolarization (SD-the electrophysiological event underlying numerous pathologies including migraine, ischemic stroke, aneurysmal subarachnoid hemorrhage, intracerebral hematoma, and trauma. We are particularly interested in investigating the role of glutamate in the duration and termination of SD caused by K+ perfusion and oxygen-glucose deprivation. Our results demonstrate that glutamate signaling plays a key role in the dynamics of SD, and that impaired glutamate uptake leads to recovery failure of neurons from SD. We confirm predictions from our model experimentally by showing that inhibiting astrocytic glutamate uptake using TFB-TBOA nearly quadruples the duration of SD in layers 2-3 of visual cortical slices from juvenile rats. The model equations are either derived purely from first physical principles of electroneutrality, osmosis, and conservation of particles or a combination of these principles and known physiological facts. Accordingly, we claim that our approach can be used as a future guide to investigate the role of glutamate, ion concentrations, and dynamics cell volume in other brain pathologies and normal brain function.

  14. The role of glutamate in neuronal ion homeostasis: A case study of spreading depolarization.

    Science.gov (United States)

    Hübel, Niklas; Hosseini-Zare, Mahshid S; Žiburkus, Jokūbas; Ullah, Ghanim

    2017-10-01

    Simultaneous changes in ion concentrations, glutamate, and cell volume together with exchange of matter between cell network and vasculature are ubiquitous in numerous brain pathologies. A complete understanding of pathological conditions as well as normal brain function, therefore, hinges on elucidating the molecular and cellular pathways involved in these mostly interdependent variations. In this paper, we develop the first computational framework that combines the Hodgkin-Huxley type spiking dynamics, dynamic ion concentrations and glutamate homeostasis, neuronal and astroglial volume changes, and ion exchange with vasculature into a comprehensive model to elucidate the role of glutamate uptake in the dynamics of spreading depolarization (SD)-the electrophysiological event underlying numerous pathologies including migraine, ischemic stroke, aneurysmal subarachnoid hemorrhage, intracerebral hematoma, and trauma. We are particularly interested in investigating the role of glutamate in the duration and termination of SD caused by K+ perfusion and oxygen-glucose deprivation. Our results demonstrate that glutamate signaling plays a key role in the dynamics of SD, and that impaired glutamate uptake leads to recovery failure of neurons from SD. We confirm predictions from our model experimentally by showing that inhibiting astrocytic glutamate uptake using TFB-TBOA nearly quadruples the duration of SD in layers 2-3 of visual cortical slices from juvenile rats. The model equations are either derived purely from first physical principles of electroneutrality, osmosis, and conservation of particles or a combination of these principles and known physiological facts. Accordingly, we claim that our approach can be used as a future guide to investigate the role of glutamate, ion concentrations, and dynamics cell volume in other brain pathologies and normal brain function.

  15. Calculus for cognitive scientists partial differential equation models

    CERN Document Server

    Peterson, James K

    2016-01-01

    This book shows cognitive scientists in training how mathematics, computer science and science can be usefully and seamlessly intertwined. It is a follow-up to the first two volumes on mathematics for cognitive scientists, and includes the mathematics and computational tools needed to understand how to compute the terms in the Fourier series expansions that solve the cable equation. The latter is derived from first principles by going back to cellular biology and the relevant biophysics.  A detailed discussion of ion movement through cellular membranes, and an explanation of how the equations that govern such ion movement leading to the standard transient cable equation are included. There are also solutions for the cable model using separation of variables, as well an explanation of why Fourier series converge and a description of the implementation of MatLab tools to compute the solutions. Finally, the standard Hodgkin - Huxley model is developed for an excitable neuron and is solved using MatLab.

  16. Dynamics of the exponential integrate-and-fire model with slow currents and adaptation.

    Science.gov (United States)

    Barranca, Victor J; Johnson, Daniel C; Moyher, Jennifer L; Sauppe, Joshua P; Shkarayev, Maxim S; Kovačič, Gregor; Cai, David

    2014-08-01

    In order to properly capture spike-frequency adaptation with a simplified point-neuron model, we study approximations of Hodgkin-Huxley (HH) models including slow currents by exponential integrate-and-fire (EIF) models that incorporate the same types of currents. We optimize the parameters of the EIF models under the external drive consisting of AMPA-type conductance pulses using the current-voltage curves and the van Rossum metric to best capture the subthreshold membrane potential, firing rate, and jump size of the slow current at the neuron's spike times. Our numerical simulations demonstrate that, in addition to these quantities, the approximate EIF-type models faithfully reproduce bifurcation properties of the HH neurons with slow currents, which include spike-frequency adaptation, phase-response curves, critical exponents at the transition between a finite and infinite number of spikes with increasing constant external drive, and bifurcation diagrams of interspike intervals in time-periodically forced models. Dynamics of networks of HH neurons with slow currents can also be approximated by corresponding EIF-type networks, with the approximation being at least statistically accurate over a broad range of Poisson rates of the external drive. For the form of external drive resembling realistic, AMPA-like synaptic conductance response to incoming action potentials, the EIF model affords great savings of computation time as compared with the corresponding HH-type model. Our work shows that the EIF model with additional slow currents is well suited for use in large-scale, point-neuron models in which spike-frequency adaptation is important.

  17. 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...

  18. Autaptic pacemaker mediated propagation of weak rhythmic activity across small-world neuronal networks

    Science.gov (United States)

    Yilmaz, Ergin; Baysal, Veli; Ozer, Mahmut; Perc, Matjaž

    2016-02-01

    We study the effects of an autapse, which is mathematically described as a self-feedback loop, on the propagation of weak, localized pacemaker activity across a Newman-Watts small-world network consisting of stochastic Hodgkin-Huxley neurons. We consider that only the pacemaker neuron, which is stimulated by a subthreshold periodic signal, has an electrical autapse that is characterized by a coupling strength and a delay time. We focus on the impact of the coupling strength, the network structure, the properties of the weak periodic stimulus, and the properties of the autapse on the transmission of localized pacemaker activity. Obtained results indicate the existence of optimal channel noise intensity for the propagation of the localized rhythm. Under optimal conditions, the autapse can significantly improve the propagation of pacemaker activity, but only for a specific range of the autaptic coupling strength. Moreover, the autaptic delay time has to be equal to the intrinsic oscillation period of the Hodgkin-Huxley neuron or its integer multiples. We analyze the inter-spike interval histogram and show that the autapse enhances or suppresses the propagation of the localized rhythm by increasing or decreasing the phase locking between the spiking of the pacemaker neuron and the weak periodic signal. In particular, when the autaptic delay time is equal to the intrinsic period of oscillations an optimal phase locking takes place, resulting in a dominant time scale of the spiking activity. We also investigate the effects of the network structure and the coupling strength on the propagation of pacemaker activity. We find that there exist an optimal coupling strength and an optimal network structure that together warrant an optimal propagation of the localized rhythm.

  19. Adaptive coupling optimized spiking coherence and synchronization in Newman-Watts neuronal networks.

    Science.gov (United States)

    Gong, Yubing; Xu, Bo; Wu, Ya'nan

    2013-09-01

    In this paper, we have numerically studied the effect of adaptive coupling on the temporal coherence and synchronization of spiking activity in Newman-Watts Hodgkin-Huxley neuronal networks. It is found that random shortcuts can enhance the spiking synchronization more rapidly when the increment speed of adaptive coupling is increased and can optimize the temporal coherence of spikes only when the increment speed of adaptive coupling is appropriate. It is also found that adaptive coupling strength can enhance the synchronization of spikes and can optimize the temporal coherence of spikes when random shortcuts are appropriate. These results show that adaptive coupling has a big influence on random shortcuts related spiking activity and can enhance and optimize the temporal coherence and synchronization of spiking activity of the network. These findings can help better understand the roles of adaptive coupling for improving the information processing and transmission in neural systems.

  20. BrainFrame: a node-level heterogeneous accelerator platform for neuron simulations

    Science.gov (United States)

    Smaragdos, Georgios; Chatzikonstantis, Georgios; Kukreja, Rahul; Sidiropoulos, Harry; Rodopoulos, Dimitrios; Sourdis, Ioannis; Al-Ars, Zaid; Kachris, Christoforos; Soudris, Dimitrios; De Zeeuw, Chris I.; Strydis, Christos

    2017-12-01

    Objective. The advent of high-performance computing (HPC) in recent years has led to its increasing use in brain studies through computational models. The scale and complexity of such models are constantly increasing, leading to challenging computational requirements. Even though modern HPC platforms can often deal with such challenges, the vast diversity of the modeling field does not permit for a homogeneous acceleration platform to effectively address the complete array of modeling requirements. Approach. In this paper we propose and build BrainFrame, a heterogeneous acceleration platform that incorporates three distinct acceleration technologies, an Intel Xeon-Phi CPU, a NVidia GP-GPU and a Maxeler Dataflow Engine. The PyNN software framework is also integrated into the platform. As a challenging proof of concept, we analyze the performance of BrainFrame on different experiment instances of a state-of-the-art neuron model, representing the inferior-olivary nucleus using a biophysically-meaningful, extended Hodgkin-Huxley representation. The model instances take into account not only the neuronal-network dimensions but also different network-connectivity densities, which can drastically affect the workload’s performance characteristics. Main results. The combined use of different HPC technologies demonstrates that BrainFrame is better able to cope with the modeling diversity encountered in realistic experiments while at the same time running on significantly lower energy budgets. Our performance analysis clearly shows that the model directly affects performance and all three technologies are required to cope with all the model use cases. Significance. The BrainFrame framework is designed to transparently configure and select the appropriate back-end accelerator technology for use per simulation run. The PyNN integration provides a familiar bridge to the vast number of models already available. Additionally, it gives a clear roadmap for extending the platform

  1. Modeling pathogenesis and treatment response in childhood absence epilepsy.

    Science.gov (United States)

    Knox, Andrew T; Glauser, Tracy; Tenney, Jeffrey; Lytton, William W; Holland, Katherine

    2018-01-01

    Childhood absence epilepsy (CAE) is a genetic generalized epilepsy syndrome with polygenic inheritance, with genes for γ-aminobutyric acid (GABA) receptors and T-type calcium channels implicated in the disorder. Previous studies of T-type calcium channel electrophysiology have shown genetic changes and medications have multiple effects. The aim of this study was to use an established thalamocortical computer model to determine how T-type calcium channels work in concert with cortical excitability to contribute to pathogenesis and treatment response in CAE. The model is comprised of cortical pyramidal, cortical inhibitory, thalamocortical relay, and thalamic reticular single-compartment neurons, implemented with Hodgkin-Huxley model ion channels and connected by AMPA, GABA A , and GABA B synapses. Network behavior was simulated for different combinations of T-type calcium channel conductance, inactivation time, steady state activation/inactivation shift, and cortical GABA A conductance. Decreasing cortical GABA A conductance and increasing T-type calcium channel conductance converted spindle to spike and wave oscillations; smaller changes were required if both were changed in concert. In contrast, left shift of steady state voltage activation/inactivation did not lead to spike and wave oscillations, whereas right shift reduced network propensity for oscillations of any type. These results provide a window into mechanisms underlying polygenic inheritance in CAE, as well as a mechanism for treatment effects and failures mediated by these channels. Although the model is a simplification of the human thalamocortical network, it serves as a useful starting point for predicting the implications of ion channel electrophysiology in polygenic epilepsy such as CAE. Wiley Periodicals, Inc. © 2017 International League Against Epilepsy.

  2. The stomatogastric nervous system as a model for studying sensorimotor interactions in real-time closed-loop conditions

    Directory of Open Access Journals (Sweden)

    Nelly eDaur

    2012-03-01

    Full Text Available The perception of proprioceptive signals that report the internal state of the body is one of the essential tasks of the nervous system and helps to continuously adapt body movements to changing circumstances. Despite the impact of proprioceptive feedback on motor activity it has rarely been studied in conditions in which motor output and sensory activity interact as they do in behaving animals, i.e. in closed-loop conditions. The interaction of motor and sensory activities, however, can create emergent properties that may govern the functional characteristics of the system. We here demonstrate the use of a well-characterized model system for central pattern generation, the stomatogastric nervous system, for studying these properties in vitro. We created a real-time computer model of a single-cell muscle tendon organ in the gastric mill of the crab foregut that uses intracellular current injections to control the activity of the biological proprioceptor. The resulting motor output of a gastric mill motor neuron is then recorded intracellularly and fed into a simple muscle model consisting of a series of low-pass filters. The muscle output is used to activate a one-dimensional Hodgkin-Huxley type model of the muscle tendon organ in real-time, allowing closed-loop conditions. Model properties were either hand-tuned to achieve the best match with data from semi-intact muscle preparations, or an exhaustive search was performed to determine the best set of parameters. We report the real-time capabilities of our models, its performance and its interaction with the biological motor system.

  3. Formation and Dynamics of Waves in a Cortical Model of Cholinergic Modulation.

    Directory of Open Access Journals (Sweden)

    James P Roach

    2015-08-01

    Full Text Available Acetylcholine (ACh is a regulator of neural excitability and one of the neurochemical substrates of sleep. Amongst the cellular effects induced by cholinergic modulation are a reduction in spike-frequency adaptation (SFA and a shift in the phase response curve (PRC. We demonstrate in a biophysical model how changes in neural excitability and network structure interact to create three distinct functional regimes: localized asynchronous, traveling asynchronous, and traveling synchronous. Our results qualitatively match those observed experimentally. Cortical activity during slow wave sleep (SWS differs from that during REM sleep or waking states. During SWS there are traveling patterns of activity in the cortex; in other states stationary patterns occur. Our model is a network composed of Hodgkin-Huxley type neurons with a M-current regulated by ACh. Regulation of ACh level can account for dynamical changes between functional regimes. Reduction of the magnitude of this current recreates the reduction in SFA the shift from a type 2 to a type 1 PRC observed in the presence of ACh. When SFA is minimal (in waking or REM sleep state, high ACh patterns of activity are localized and easily pinned by network inhomogeneities. When SFA is present (decreasing ACh, traveling waves of activity naturally arise. A further decrease in ACh leads to a high degree of synchrony within traveling waves. We also show that the level of ACh determines how sensitive network activity is to synaptic heterogeneity. These regimes may have a profound functional significance as stationary patterns may play a role in the proper encoding of external input as memory and traveling waves could lead to synaptic regularization, giving unique insights into the role and significance of ACh in determining patterns of cortical activity and functional differences arising from the patterns.

  4. Energy Model of Neuron Activation.

    Science.gov (United States)

    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.

  5. Synaptic Plasticity and Spike Synchronisation in Neuronal Networks

    Science.gov (United States)

    Borges, Rafael R.; Borges, Fernando S.; Lameu, Ewandson L.; Protachevicz, Paulo R.; Iarosz, Kelly C.; Caldas, Iberê L.; Viana, Ricardo L.; Macau, Elbert E. N.; Baptista, Murilo S.; Grebogi, Celso; Batista, Antonio M.

    2017-12-01

    Brain plasticity, also known as neuroplasticity, is a fundamental mechanism of neuronal adaptation in response to changes in the environment or due to brain injury. In this review, we show our results about the effects of synaptic plasticity on neuronal networks composed by Hodgkin-Huxley neurons. We show that the final topology of the evolved network depends crucially on the ratio between the strengths of the inhibitory and excitatory synapses. Excitation of the same order of inhibition revels an evolved network that presents the rich-club phenomenon, well known to exist in the brain. For initial networks with considerably larger inhibitory strengths, we observe the emergence of a complex evolved topology, where neurons sparsely connected to other neurons, also a typical topology of the brain. The presence of noise enhances the strength of both types of synapses, but if the initial network has synapses of both natures with similar strengths. Finally, we show how the synchronous behaviour of the evolved network will reflect its evolved topology.

  6. Efficient Coding and Energy Efficiency Are Promoted by Balanced Excitatory and Inhibitory Synaptic Currents in Neuronal Network.

    Science.gov (United States)

    Yu, Lianchun; Shen, Zhou; Wang, Chen; Yu, Yuguo

    2018-01-01

    Selective pressure may drive neural systems to process as much information as possible with the lowest energy cost. Recent experiment evidence revealed that the ratio between synaptic excitation and inhibition (E/I) in local cortex is generally maintained at a certain value which may influence the efficiency of energy consumption and information transmission of neural networks. To understand this issue deeply, we constructed a typical recurrent Hodgkin-Huxley network model and studied the general principles that governs the relationship among the E/I synaptic current ratio, the energy cost and total amount of information transmission. We observed in such a network that there exists an optimal E/I synaptic current ratio in the network by which the information transmission achieves the maximum with relatively low energy cost. The coding energy efficiency which is defined as the mutual information divided by the energy cost, achieved the maximum with the balanced synaptic current. Although background noise degrades information transmission and imposes an additional energy cost, we find an optimal noise intensity that yields the largest information transmission and energy efficiency at this optimal E/I synaptic transmission ratio. The maximization of energy efficiency also requires a certain part of energy cost associated with spontaneous spiking and synaptic activities. We further proved this finding with analytical solution based on the response function of bistable neurons, and demonstrated that optimal net synaptic currents are capable of maximizing both the mutual information and energy efficiency. These results revealed that the development of E/I synaptic current balance could lead a cortical network to operate at a highly efficient information transmission rate at a relatively low energy cost. The generality of neuronal models and the recurrent network configuration used here suggest that the existence of an optimal E/I cell ratio for highly efficient energy

  7. Modeling of action potential generation in NG108-15 cells.

    Science.gov (United States)

    Molnar, Peter; Hickman, James J

    2014-01-01

    In order to explore the possibility of identifying toxins based on their effect on the shape of action potentials, we created a computer model of the action potential generation in NG108-15 cells (a neuroblastoma/glioma hybrid cell line). To generate the experimental data for model validation, voltage-dependent sodium, potassium and high-threshold calcium currents, as well as action potentials, were recorded from NG108-15 cells with conventional whole-cell patch-clamp methods. Based on the classic Hodgkin-Huxley formalism and the linear thermodynamic description of the rate constants, ion-channel parameters were estimated using an automatic fitting method. Utilizing the established parameters, action potentials were generated using the Hodgkin-Huxley formalism and were fitted to the recorded action potentials. To demonstrate the applicability of the method for toxin detection and discrimination, the effect of tetrodotoxin (a sodium channel blocker) and tefluthrin (a pyrethroid that is a sodium channel opener) were studied. The two toxins affected the shape of the action potentials differently, and their respective effects were identified based on the predicted changes in the fitted parameters.

  8. Optimal physiological structure of small neurons to guarantee stable information processing

    Science.gov (United States)

    Zeng, S. Y.; Zhang, Z. Z.; Wei, D. Q.; Luo, X. S.; Tang, W. Y.; Zeng, S. W.; Wang, R. F.

    2013-02-01

    Spike is the basic element for neuronal information processing and the spontaneous spiking frequency should be less than 1 Hz for stable information processing. If the neuronal membrane area is small, the frequency of neuronal spontaneous spiking caused by ion channel noise may be high. Therefore, it is important to suppress the deleterious spontaneous spiking of the small neurons. We find by simulation of stochastic neurons with Hodgkin-Huxley-type channels that the leakage system is critical and extremely efficient to suppress the spontaneous spiking and guarantee stable information processing of the small neurons. However, within the physiological limit the potassium system cannot do so. The suppression effect of the leakage system is super-exponential, but that of the potassium system is quasi-linear. With the minor physiological cost and the minimal consumption of metabolic energy, a slightly lower reversal potential and a relatively larger conductance of the leakage system give the optimal physiological structure to suppress the deleterious spontaneous spiking and guarantee stable information processing of small neurons, dendrites and axons.

  9. Ordering chaos and synchronization transitions by chemical delay and coupling on scale-free neuronal networks

    International Nuclear Information System (INIS)

    Gong Yubing; Xie Yanhang; Lin Xiu; Hao Yinghang; Ma Xiaoguang

    2010-01-01

    Research highlights: → Chemical delay and chemical coupling can tame chaotic bursting. → Chemical delay-induced transitions from bursting synchronization to intermittent multiple spiking synchronizations. → Chemical coupling-induced different types of delay-dependent firing transitions. - Abstract: Chemical synaptic connections are more common than electric ones in neurons, and information transmission delay is especially significant for the synapses of chemical type. In this paper, we report a phenomenon of ordering spatiotemporal chaos and synchronization transitions by the delays and coupling through chemical synapses of modified Hodgkin-Huxley (MHH) neurons on scale-free networks. As the delay τ is increased, the neurons exhibit transitions from bursting synchronization (BS) to intermittent multiple spiking synchronizations (SS). As the coupling g syn is increased, the neurons exhibit different types of firing transitions, depending on the values of τ. For a smaller τ, there are transitions from spatiotemporal chaotic bursting (SCB) to BS or SS; while for a larger τ, there are transitions from SCB to intermittent multiple SS. These findings show that the delays and coupling through chemical synapses can tame the chaotic firings and repeatedly enhance the firing synchronization of neurons, and hence could play important roles in the firing activity of the neurons on scale-free networks.

  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. 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...

  13. A novel electrical model of nerve and muscle using Pspice

    CERN Document Server

    Peasgood, W; Lam, C K; Armstrong, A G; Wood, W

    2003-01-01

    In this work, a model is developed to simulate the biological processes involved in nerve fibre transmission and subsequent muscle contraction. The model has been based on approximating biological structure and function to electrical circuits and as such was implemented on an electronics simulation software package called Pspice. Models of nerve, the nerve-muscle interface and muscle fibre have been implemented. The time dependent ionic properties of the nerve and muscle membranes have been simulated using the Hodgkin-Huxley equations and for the muscle fibre, the implementation of the Huxley sliding filament theory for muscular contraction. The results show that nerve may be considered as a fractal transmission line and that the amplitude of the nerve membrane depolarization is dependent on the dimensions of the fibre. Additionally, simulation of the nerve-muscle interface allows the fractal nerve model to be connected to the muscle fibre model and it is shown that a two sarcomere molecular simulation can pr...

  14. 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.

  15. Acceleration of spiking neural network based pattern recognition on NVIDIA graphics processors.

    Science.gov (United States)

    Han, Bing; Taha, Tarek M

    2010-04-01

    There is currently a strong push in the research community to develop biological scale implementations of neuron based vision models. Systems at this scale are computationally demanding and generally utilize more accurate neuron models, such as the Izhikevich and the Hodgkin-Huxley models, in favor of the more popular integrate and fire model. We examine the feasibility of using graphics processing units (GPUs) to accelerate a spiking neural network based character recognition network to enable such large scale systems. Two versions of the network utilizing the Izhikevich and Hodgkin-Huxley models are implemented. Three NVIDIA general-purpose (GP) GPU platforms are examined, including the GeForce 9800 GX2, the Tesla C1060, and the Tesla S1070. Our results show that the GPGPUs can provide significant speedup over conventional processors. In particular, the fastest GPGPU utilized, the Tesla S1070, provided a speedup of 5.6 and 84.4 over highly optimized implementations on the fastest central processing unit (CPU) tested, a quadcore 2.67 GHz Xeon processor, for the Izhikevich and the Hodgkin-Huxley models, respectively. The CPU implementation utilized all four cores and the vector data parallelism offered by the processor. The results indicate that GPUs are well suited for this application domain.

  16. Stochastic resonance of ensemble neurons for transient spike trains: Wavelet analysis

    International Nuclear Information System (INIS)

    Hasegawa, Hideo

    2002-01-01

    By using the wavelet transformation (WT), I have analyzed the response of an ensemble of N (=1, 10, 100, and 500) Hodgkin-Huxley neurons to transient M-pulse spike trains (M=1 to 3) with independent Gaussian noises. The cross correlation between the input and output signals is expressed in terms of the WT expansion coefficients. The signal-to-noise ratio (SNR) is evaluated by using the denoising method within the WT, by which the noise contribution is extracted from the output signals. Although the response of a single (N=1) neuron to subthreshold transient signals with noises is quite unreliable, the transmission fidelity assessed by the cross correlation and SNR is shown to be much improved by increasing the value of N: a population of neurons plays an indispensable role in the stochastic resonance (SR) for transient spike inputs. It is also shown that in a large-scale ensemble, the transmission fidelity for suprathreshold transient spikes is not significantly degraded by a weak noise which is responsible to SR for subthreshold inputs

  17. Linear stability analysis of retrieval state in associative memory neural networks of spiking neurons

    International Nuclear Information System (INIS)

    Yoshioka, Masahiko

    2002-01-01

    We study associative memory neural networks of the Hodgkin-Huxley type of spiking neurons in which multiple periodic spatiotemporal patterns of spike timing are memorized as limit-cycle-type attractors. In encoding the spatiotemporal patterns, we assume the spike-timing-dependent synaptic plasticity with the asymmetric time window. Analysis for periodic solution of retrieval state reveals that if the area of the negative part of the time window is equivalent to the positive part, then crosstalk among encoded patterns vanishes. Phase transition due to the loss of the stability of periodic solution is observed when we assume fast α function for direct interaction among neurons. In order to evaluate the critical point of this phase transition, we employ Floquet theory in which the stability problem of the infinite number of spiking neurons interacting with α function is reduced to the eigenvalue problem with the finite size of matrix. Numerical integration of the single-body dynamics yields the explicit value of the matrix, which enables us to determine the critical point of the phase transition with a high degree of precision

  18. 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.

  19. A COMPUTATIONAL MODEL OF MOTOR NEURON DEGENERATION

    Science.gov (United States)

    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

  20. A computational model of motor neuron degeneration.

    Science.gov (United States)

    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.

  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. On the identification of multiple space dependent ionic parameters in cardiac electrophysiology modelling

    Science.gov (United States)

    Abidi, Yassine; Bellassoued, Mourad; Mahjoub, Moncef; Zemzemi, Nejib

    2018-03-01

    In this paper, we consider the inverse problem of space dependent multiple ionic parameters identification in cardiac electrophysiology modelling from a set of observations. We use the monodomain system known as a state-of-the-art model in cardiac electrophysiology and we consider a general Hodgkin-Huxley formalism to describe the ionic exchanges at the microscopic level. This formalism covers many physiological transmembrane potential models including those in cardiac electrophysiology. Our main result is the proof of the uniqueness and a Lipschitz stability estimate of ion channels conductance parameters based on some observations on an arbitrary subdomain. The key idea is a Carleman estimate for a parabolic operator with multiple coefficients and an ordinary differential equation system.

  3. Parametric Modeling of Nerve Cell under the Sinusoidal Environmental 50 Hz Extremely Low Frequency Magnetic Fields

    Directory of Open Access Journals (Sweden)

    Homayoun Ebrahimian

    2013-06-01

    Full Text Available Background & Objectives: The development of technology has naturally given rise to an increase in environmental low-frequency electromagnetic fields and consequently has attracted scholars' attention. Most of the studies have focused on transmission lines and power system distribution with 50 Hz. This research is an attempt to show the effect of 50 Hz magnetic fields on bioelectric parameters and indicates the possible influence of this change in F1 cells of Helix aspersa .   Methods: The present research used Helix aspersa neuron F1 to identify the location of magnetic fields as well as the rate of effects of environmental magnetic fields on nervous system. Control group was used to study the effect of elapsed time, electrode entering and the cell membrane rupture. Intuition group and environmental group were considered in order to study the potential impact of interfering environmental factors and identify the effectiveness rate of magnetic fields, respectively. For the purpose of producing uniform magnetic field Helmholtz coil was used. Electrophysiological recording was realized under the requirements of current clamp. And, in order to show the impacts from magnetic fields on ion channels Hodgkin-Huxley cell model was applied. All data were analyzed taking the advantage of SPSS 16 software and two-way ANOVA statistical test. P < 0.05 was considered as significance level. And MATLAB software environment and PSO were used in order for applying the algorithm and estimating the parameters.   Result: No statistically significant difference was found between control and sham groups in different time intervals. Once the 45.87 microtesla was applied significant differences were observed 12 minutes after the application. The highest amount of change happened 14 minutes after the application of more fields. With the application of the field, the amplitude of the sodium action potential shows decreasing trend . No significant changes were observed in

  4. 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.

  5. 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.

  6. 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. .

  7. 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.

  8. 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.

  9. 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.

  10. Novel model of neuronal bioenergetics

    DEFF Research Database (Denmark)

    Bak, Lasse Kristoffer; Obel, Linea Lykke Frimodt; Walls, Anne B

    2012-01-01

    -methyl-d-aspartate)-induced synaptic activity and that lactate alone is not able to support neurotransmitter glutamate homoeostasis. Subsequently, a model was proposed to explain these results at the cellular level. In brief, the intermittent rises in intracellular Ca2+ during activation cause influx of Ca2+ into the mitochondrial...

  11. 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

  12. 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.

  13. 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...

  14. 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.

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

    Science.gov (United States)

    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.

  16. 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

  17. 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...

  18. 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.

  19. 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.

  20. 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...

  1. Memristive Systems Analysis of 3-Terminal Devices

    OpenAIRE

    Mouttet, Blaise

    2010-01-01

    Memristive systems were proposed in 1976 by Leon Chua and Sung Mo Kang as a model for 2-terminal passive nonlinear dynamical systems which exhibit memory effects. Such systems were originally shown to be relevant to the modeling of action potentials in neurons in regards to the Hodgkin-Huxley model and, more recently, to the modeling of thin film materials such as TiO2-x proposed for non-volatile resistive memory. However, over the past 50 years a variety of 3-terminal non-passive dynamical d...

  2. 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.

  3. 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.

  4. 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.

  5. Study Under AC Stimulation on Excitement Properties of Weighted Small-World Biological Neural Networks with Side-Restrain Mechanism

    International Nuclear Information System (INIS)

    Yuan Wujie; Luo Xiaoshu; Jiang Pinqun

    2007-01-01

    In this paper, we propose a new model of weighted small-world biological neural networks based on biophysical Hodgkin-Huxley neurons with side-restrain mechanism. Then we study excitement properties of the model under alternating current (AC) stimulation. The study shows that the excitement properties in the networks are preferably consistent with the behavior properties of a brain nervous system under different AC stimuli, such as refractory period and the brain neural excitement response induced by different intensities of noise and coupling. The results of the study have reference worthiness for the brain nerve electrophysiology and epistemological science.

  6. 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...

  7. 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.

  8. 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

  9. 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

  10. 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.

  11. Toward Petascale Biologically Plausible Neural Networks

    Science.gov (United States)

    Long, Lyle

    This talk will describe an approach to achieving petascale neural networks. Artificial intelligence has been oversold for many decades. Computers in the beginning could only do about 16,000 operations per second. Computer processing power, however, has been doubling every two years thanks to Moore's law, and growing even faster due to massively parallel architectures. Finally, 60 years after the first AI conference we have computers on the order of the performance of the human brain (1016 operations per second). The main issues now are algorithms, software, and learning. We have excellent models of neurons, such as the Hodgkin-Huxley model, but we do not know how the human neurons are wired together. With careful attention to efficient parallel computing, event-driven programming, table lookups, and memory minimization massive scale simulations can be performed. The code that will be described was written in C + + and uses the Message Passing Interface (MPI). It uses the full Hodgkin-Huxley neuron model, not a simplified model. It also allows arbitrary network structures (deep, recurrent, convolutional, all-to-all, etc.). The code is scalable, and has, so far, been tested on up to 2,048 processor cores using 107 neurons and 109 synapses.

  12. 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.

  13. 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...

  14. Automatic fitting of spiking neuron models to electrophysiological recordings

    Directory of Open Access Journals (Sweden)

    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.

  15. 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.

  16. 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)

  17. A Neuronal Model of Classical Conditioning.

    Science.gov (United States)

    1987-10-01

    suggests that nervous system activity can be understood in terms of two- 20. DISTRIBUTION/ AVAILABILIT Y OF ABSTRACT 21 ABSTRACT SECURITY CLASSIF ICATION...unconditionea stimuli having an external source (food and water are examples). Acquired neuronal drives, likewise, are expected to have internal...Moore, B. R. (19731. The form of the autushdpeo response with food or water reitArcers. Journal of the Experitiental Analykis uf Behavior, 20, 1b3-18

  18. 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.

  19. [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.

  20. 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.

  1. 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

  2. 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.

  3. 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.

  4. 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.

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

    Energy Technology Data Exchange (ETDEWEB)

    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.

  6. 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.

  7. Cochlear spike synchronization and neuron coincidence detection model

    Science.gov (United States)

    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.

  8. 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

  9. 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.

  10. The somatic shunt cable model for neurons.

    OpenAIRE

    Durand, D

    1984-01-01

    The derivation of the equations for an electrical model of nerve cells is presented. The model consists of an equivalent cylinder, a lumped somatic impedance, and a variable shunt at the soma. This shunt was introduced to take into account the fast voltage decays observed following the injections of current pulses in some motoneurons and hippocampal granule cells that could not be explained by existing models. The shunt can be interpreted either by penetration damage with the electrode or by ...

  11. Neuronal Models for Studying Tau Pathology

    Directory of Open Access Journals (Sweden)

    Thorsten Koechling

    2010-01-01

    Full Text Available Alzheimer's disease (AD is the most frequent neurodegenerative disorder leading to dementia in the aged human population. It is characterized by the presence of two main pathological hallmarks in the brain: senile plaques containing -amyloid peptide and neurofibrillary tangles (NFTs, consisting of fibrillar polymers of abnormally phosphorylated tau protein. Both of these histological characteristics of the disease have been simulated in genetically modified animals, which today include numerous mouse, fish, worm, and fly models of AD. The objective of this review is to present some of the main animal models that exist for reproducing symptoms of the disorder and their advantages and shortcomings as suitable models of the pathological processes. Moreover, we will discuss the results and conclusions which have been drawn from the use of these models so far and their contribution to the development of therapeutic applications for AD.

  12. 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

  13. Stein's neuronal model with pooled renewal input

    Czech Academy of Sciences Publication Activity Database

    Rajdl, K.; Lánský, Petr

    2015-01-01

    Roč. 109, č. 3 (2015), s. 389-399 ISSN 0340-1200 Institutional support: RVO:67985823 Keywords : Stein’s model * Poisson process * pooled renewal processes * first-passage time Subject RIV: BA - General Mathematics Impact factor: 1.611, year: 2015

  14. 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...

  15. 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

  16. Modelled temperature-dependent excitability behaviour of a generalised human peripheral sensory nerve fibre.

    Science.gov (United States)

    Smit, Jacoba E; Hanekom, Tania; Hanekom, Johan J

    2009-08-01

    The objective of this study was to determine if a recently developed human Ranvier node model, which is based on a modified version of the Hodgkin-Huxley model, could predict the excitability behaviour in human peripheral sensory nerve fibres with diameters ranging from 5.0 to 15.0 microm. The Ranvier node model was extended to include a persistent sodium current and was incorporated into a generalised single cable nerve fibre model. Parameter temperature dependence was included. All calculations were performed in Matlab. Sensory nerve fibre excitability behaviour characteristics predicted by the new nerve fibre model at different temperatures and fibre diameters compared well with measured data. Absolute refractory periods deviated from measured data, while relative refractory periods were similar to measured data. Conduction velocities showed both fibre diameter and temperature dependence and were underestimated in fibres thinner than 12.5 microm. Calculated strength-duration time constants ranged from 128.5 to 183.0 micros at 37 degrees C over the studied nerve fibre diameter range, with chronaxie times about 30% shorter than strength-duration time constants. Chronaxie times exhibited temperature dependence, with values overestimated by a factor 5 at temperatures lower than body temperature. Possible explanations include the deviated absolute refractory period trend and inclusion of a nodal strangulation relationship.

  17. 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.

  18. 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.

  19. 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.

  20. 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.

  1. When long-range zero-lag synchronization is feasible in cortical networks

    Directory of Open Access Journals (Sweden)

    Atthaphon eViriyopase

    2012-07-01

    Full Text Available Many studies have reported long-range synchronization of neuronal activity between brain areas, in particular in the gamma-band with frequencies in the range of 40-80 Hz. Several studies have reported synchrony with zero phase lag, which is remarkable considering the synaptic and conduction delays inherent in the connections between distant brain areas. This result has led to many speculations about the possible functional role of zero-lag synchrony, e.g., for neuronal communication in attention, memory and feature binding. However, recent studies using recordings of single-unit activity and local field potentials report that neuronal synchronization occurs with non-zero phase lags. This raises the questions whether zero-lag synchrony can occur in the brain and, if so, under which conditions.We used analytical methods and computer simulations to investigate which connectivity between neuronal populations allows or prohibits zero-lag synchrony. We did so for a model where two oscillators interact via a relay oscillator. Analytical results and computer simulations were obtained for both type I Mirollo-Strogatz neurons and type II Hodgkin-Huxley neurons. We have investigated the dynamics of the model for various types of synaptic coupling and importantly considered the potential impact of Spike-Timing Dependent Plasticity (STDP and its learning window. We confirm previous results that zero-lag synchrony can be achieved in this configuration. This is much easier to achieve with Hodgkin-Huxley neurons, which have a biphasic phase response curve, than for type I neurons. STDP facilitates zero-lag synchrony as it adjusts the synaptic strengths such that zero-lag synchrony is feasible for a much larger range of parameters than without STDP.

  2. 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.

  3. Dynamics in the Parameter Space of a Neuron Model

    Science.gov (United States)

    Paulo, C. Rech

    2012-06-01

    Some two-dimensional parameter-space diagrams are numerically obtained by considering the largest Lyapunov exponent for a four-dimensional thirteen-parameter Hindmarsh—Rose neuron model. Several different parameter planes are considered, and it is shown that depending on the combination of parameters, a typical scenario can be preserved: for some choice of two parameters, the parameter plane presents a comb-shaped chaotic region embedded in a large periodic region. It is also shown that there exist regions close to these comb-shaped chaotic regions, separated by the comb teeth, organizing themselves in period-adding bifurcation cascades.

  4. 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

  5. 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

  6. Neurons compute internal models of the physical laws of motion.

    Science.gov (United States)

    Angelaki, Dora E; Shaikh, Aasef G; Green, Andrea M; Dickman, J David

    2004-07-29

    A critical step in self-motion perception and spatial awareness is the integration of motion cues from multiple sensory organs that individually do not provide an accurate representation of the physical world. One of the best-studied sensory ambiguities is found in visual processing, and arises because of the inherent uncertainty in detecting the motion direction of an untextured contour moving within a small aperture. A similar sensory ambiguity arises in identifying the actual motion associated with linear accelerations sensed by the otolith organs in the inner ear. These internal linear accelerometers respond identically during translational motion (for example, running forward) and gravitational accelerations experienced as we reorient the head relative to gravity (that is, head tilt). Using new stimulus combinations, we identify here cerebellar and brainstem motion-sensitive neurons that compute a solution to the inertial motion detection problem. We show that the firing rates of these populations of neurons reflect the computations necessary to construct an internal model representation of the physical equations of motion.

  7. 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.

  8. 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.

  9. 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...

  10. 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...

  11. 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

  12. 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.

  13. Data assimilation using a GPU accelerated path integral Monte Carlo approach

    Science.gov (United States)

    Quinn, John C.; Abarbanel, Henry D. I.

    2011-09-01

    The answers to data assimilation questions can be expressed as path integrals over all possible state and parameter histories. We show how these path integrals can be evaluated numerically using a Markov Chain Monte Carlo method designed to run in parallel on a graphics processing unit (GPU). We demonstrate the application of the method to an example with a transmembrane voltage time series of a simulated neuron as an input, and using a Hodgkin-Huxley neuron model. By taking advantage of GPU computing, we gain a parallel speedup factor of up to about 300, compared to an equivalent serial computation on a CPU, with performance increasing as the length of the observation time used for data assimilation increases.

  14. 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....

  15. 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.

  16. 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

  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. Relaxation Cycles in a Generalized Neuron Model with Two Delays

    Directory of Open Access Journals (Sweden)

    S. D. Glyzin

    2013-01-01

    Full Text Available A method of modeling the phenomenon of bursting behavior in neural systems based on delay equations is proposed. A singularly perturbed scalar nonlinear differentialdifference equation of Volterra type is a mathematical model of a neuron and a separate pulse containing one function without delay and two functions with different lags. It is established that this equation, for a suitable choice of parameters, has a stable periodic motion with any preassigned number of bursts in the time interval of the period length. To prove this assertion we first go to a relay-type equation and then determine the asymptotic solutions of a singularly perturbed equation. On the basis of this asymptotics the Poincare operator is constructed. The resulting operator carries a closed bounded convex set of initial conditions into itself, which suggests that it has at least one fixed point. The Frechet derivative evaluation of the succession operator, made in the paper, allows us to prove the uniqueness and stability of the resulting relax of the periodic solution.

  19. 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

  20. 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...

  1. Electroneutral models for dynamic Poisson-Nernst-Planck systems

    Science.gov (United States)

    Song, Zilong; Cao, Xiulei; Huang, Huaxiong

    2018-01-01

    The Poisson-Nernst-Planck (PNP) system is a standard model for describing ion transport. In many applications, e.g., ions in biological tissues, the presence of thin boundary layers poses both modeling and computational challenges. In this paper, we derive simplified electroneutral (EN) models where the thin boundary layers are replaced by effective boundary conditions. There are two major advantages of EN models. First, it is much cheaper to solve them numerically. Second, EN models are easier to deal with compared to the original PNP system; therefore, it would also be easier to derive macroscopic models for cellular structures using EN models. Even though the approach used here is applicable to higher-dimensional cases, this paper mainly focuses on the one-dimensional system, including the general multi-ion case. Using systematic asymptotic analysis, we derive a variety of effective boundary conditions directly applicable to the EN system for the bulk region. This EN system can be solved directly and efficiently without computing the solution in the boundary layer. The derivation is based on matched asymptotics, and the key idea is to bring back higher-order contributions into the effective boundary conditions. For Dirichlet boundary conditions, the higher-order terms can be neglected and the classical results (continuity of electrochemical potential) are recovered. For flux boundary conditions, higher-order terms account for the accumulation of ions in boundary layer and neglecting them leads to physically incorrect solutions. To validate the EN model, numerical computations are carried out for several examples. Our results show that solving the EN model is much more efficient than the original PNP system. Implemented with the Hodgkin-Huxley model, the computational time for solving the EN model is significantly reduced without sacrificing the accuracy of the solution due to the fact that it allows for relatively large mesh and time-step sizes.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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

  7. 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.

  8. 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.

  9. 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

  10. 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

  11. Dynamic behaviors in directed networks

    International Nuclear Information System (INIS)

    Park, Sung Min; Kim, Beom Jun

    2006-01-01

    Motivated by the abundance of directed synaptic couplings in a real biological neuronal network, we investigate the synchronization behavior of the Hodgkin-Huxley model in a directed network. We start from the standard model of the Watts-Strogatz undirected network and then change undirected edges to directed arcs with a given probability, still preserving the connectivity of the network. A generalized clustering coefficient for directed networks is defined and used to investigate the interplay between the synchronization behavior and underlying structural properties of directed networks. We observe that the directedness of complex networks plays an important role in emerging dynamical behaviors, which is also confirmed by a numerical study of the sociological game theoretic voter model on directed networks

  12. 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.

  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. 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.

  15. 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.

  16. 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.

  17. 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.

  18. 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.

  19. 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

  20. 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.

  1. 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.

  2. 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.

  3. 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

  4. 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

  5. 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

  6. 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

  7. 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.

  8. 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

  9. Modeling motor neuron disease: the matter of time

    NARCIS (Netherlands)

    Arbab, Manda; Baars, S.E.; geijsen, n

    2014-01-01

    Stem cell technologies have created new opportunities to generate unlimited numbers of human neurons in the lab and study neurodegenerative disorders such as amyotrophic lateral sclerosis (ALS) and spinal muscular atrophy (SMA). Although some disease hallmarks have been reported in patient-derived

  10. Modeling motor neuron disease : the matter of time

    NARCIS (Netherlands)

    Arbab, Mandana; Baars, Susanne; Geijsen, Niels

    2014-01-01

    Stem cell technologies have created new opportunities to generate unlimited numbers of human neurons in the lab and study neurodegenerative disorders such as amyotrophic lateral sclerosis (ALS) and spinal muscular atrophy (SMA). Although some disease hallmarks have been reported in patient-derived

  11. 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.

  12. 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.

  13. 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.

  14. 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.

  15. 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.

  16. 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.

  17. 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.

  18. A Parsimonious Model of the Rabbit Action Potential Elucidates the Minimal Physiological Requirements for Alternans and Spiral Wave Breakup.

    Science.gov (United States)

    Gray, Richard A; Pathmanathan, Pras

    2016-10-01

    Elucidating the underlying mechanisms of fatal cardiac arrhythmias requires a tight integration of electrophysiological experiments, models, and theory. Existing models of transmembrane action potential (AP) are complex (resulting in over parameterization) and varied (leading to dissimilar predictions). Thus, simpler models are needed to elucidate the "minimal physiological requirements" to reproduce significant observable phenomena using as few parameters as possible. Moreover, models have been derived from experimental studies from a variety of species under a range of environmental conditions (for example, all existing rabbit AP models incorporate a formulation of the rapid sodium current, INa, based on 30 year old data from chick embryo cell aggregates). Here we develop a simple "parsimonious" rabbit AP model that is mathematically identifiable (i.e., not over parameterized) by combining a novel Hodgkin-Huxley formulation of INa with a phenomenological model of repolarization similar to the voltage dependent, time-independent rectifying outward potassium current (IK). The model was calibrated using the following experimental data sets measured from the same species (rabbit) under physiological conditions: dynamic current-voltage (I-V) relationships during the AP upstroke; rapid recovery of AP excitability during the relative refractory period; and steady-state INa inactivation via voltage clamp. Simulations reproduced several important "emergent" phenomena including cellular alternans at rates > 250 bpm as observed in rabbit myocytes, reentrant spiral waves as observed on the surface of the rabbit heart, and spiral wave breakup. Model variants were studied which elucidated the minimal requirements for alternans and spiral wave break up, namely the kinetics of INa inactivation and the non-linear rectification of IK.The simplicity of the model, and the fact that its parameters have physiological meaning, make it ideal for engendering generalizable mechanistic

  19. 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

  20. 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.

  1. 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...

  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. Extending the mirror neuron system model, II: what did I just do? A new role for mirror neurons.

    Science.gov (United States)

    Bonaiuto, James; Arbib, Michael A

    2010-04-01

    A mirror system is active both when an animal executes a class of actions (self-actions) and when it sees another execute an action of that class. Much attention has been given to the possible roles of mirror systems in responding to the actions of others but there has been little attention paid to their role in self-actions. In the companion article (Bonaiuto et al. Biol Cybern 96:9-38, 2007) we presented MNS2, an extension of the Mirror Neuron System model of the monkey mirror system trained to recognize the external appearance of its own actions as a basis for recognizing the actions of other animals when they perform similar actions. Here we further extend the study of the mirror system by introducing the novel hypotheses that a mirror system may additionally help in monitoring the success of a self-action and may also be activated by recognition of one's own apparent actions as well as efference copy from one's intended actions. The framework for this computational demonstration is a model of action sequencing, called augmented competitive queuing, in which action choice is based on the desirability of executable actions. We show how this "what did I just do?" function of mirror neurons can contribute to the learning of both executability and desirability which in certain cases supports rapid reorganization of motor programs in the face of disruptions.

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. 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

  10. 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

  11. 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.

  12. 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.

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

    Science.gov (United States)

    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

  14. Aberrant neuronal activity-induced signaling and gene expression in a mouse model of RASopathy.

    Directory of Open Access Journals (Sweden)

    Franziska Altmüller

    2017-03-01

    Full Text Available Noonan syndrome (NS is characterized by reduced growth, craniofacial abnormalities, congenital heart defects, and variable cognitive deficits. NS belongs to the RASopathies, genetic conditions linked to mutations in components and regulators of the Ras signaling pathway. Approximately 50% of NS cases are caused by mutations in PTPN11. However, the molecular mechanisms underlying cognitive impairments in NS patients are still poorly understood. Here, we report the generation and characterization of a new conditional mouse strain that expresses the overactive Ptpn11D61Y allele only in the forebrain. Unlike mice with a global expression of this mutation, this strain is viable and without severe systemic phenotype, but shows lower exploratory activity and reduced memory specificity, which is in line with a causal role of disturbed neuronal Ptpn11 signaling in the development of NS-linked cognitive deficits. To explore the underlying mechanisms we investigated the neuronal activity-regulated Ras signaling in brains and neuronal cultures derived from this model. We observed an altered surface expression and trafficking of synaptic glutamate receptors, which are crucial for hippocampal neuronal plasticity. Furthermore, we show that the neuronal activity-induced ERK signaling, as well as the consecutive regulation of gene expression are strongly perturbed. Microarray-based hippocampal gene expression profiling revealed profound differences in the basal state and upon stimulation of neuronal activity. The neuronal activity-dependent gene regulation was strongly attenuated in Ptpn11D61Y neurons. In silico analysis of functional networks revealed changes in the cellular signaling beyond the dysregulation of Ras/MAPK signaling that is nearly exclusively discussed in the context of NS at present. Importantly, changes in PI3K/AKT/mTOR and JAK/STAT signaling were experimentally confirmed. In summary, this study uncovers aberrant neuronal activity

  15. 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.

  16. 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.

  17. 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.

  18. 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

  19. Changes in the Excitability of Neocortical Neurons in a Mouse Model of Amyotrophic Lateral Sclerosis Are Not Specific to Corticospinal Neurons and Are Modulated by Advancing Disease.

    Science.gov (United States)

    Kim, Juhyun; Hughes, Ethan G; Shetty, Ashwin S; Arlotta, Paola; Goff, Loyal A; Bergles, Dwight E; Brown, Solange P

    2017-09-13

    Cell type-specific changes in neuronal excitability have been proposed to contribute to the selective degeneration of corticospinal neurons in amyotrophic lateral sclerosis (ALS) and to neocortical hyperexcitability, a prominent feature of both inherited and sporadic variants of the disease, but the mechanisms underlying selective loss of specific cell types in ALS are not known. We analyzed the physiological properties of distinct classes of cortical neurons in the motor cortex of hSOD1 G93A mice of both sexes and found that they all exhibit increases in intrinsic excitability that depend on disease stage. Targeted recordings and in vivo calcium imaging further revealed that neurons adapt their functional properties to normalize cortical excitability as the disease progresses. Although different neuron classes all exhibited increases in intrinsic excitability, transcriptional profiling indicated that the molecular mechanisms underlying these changes are cell type specific. The increases in excitability in both excitatory and inhibitory cortical neurons show that selective dysfunction of neuronal cell types cannot account for the specific vulnerability of corticospinal motor neurons in ALS. Furthermore, the stage-dependent alterations in neuronal function highlight the ability of cortical circuits to adapt as disease progresses. These findings show that both disease stage and cell type must be considered when developing therapeutic strategies for treating ALS. SIGNIFICANCE STATEMENT It is not known why certain classes of neurons preferentially die in different neurodegenerative diseases. It has been proposed that the enhanced excitability of affected neurons is a major contributor to their selective loss. We show using a mouse model of amyotrophic lateral sclerosis (ALS), a disease in which corticospinal neurons exhibit selective vulnerability, that changes in excitability are not restricted to this neuronal class and that excitability does not increase

  20. 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

  1. 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.

  2. Minocycline reduces neuroinflammation but does not ameliorate neuron loss in a mouse model of neurodegeneration

    Science.gov (United States)

    Cheng, Shanshan; Hou, Jinxing; Zhang, Chen; Xu, Congyu; Wang, Long; Zou, Xiaoxia; Yu, Huahong; Shi, Yun; Yin, Zhenyu; Chen, Guiquan

    2015-01-01

    Minocycline is a broad-spectrum tetracycline antibiotic. A number of preclinical studies have shown that minocycline exhibits neuroprotective effects in various animal models of neurological diseases. However, it remained unknown whether minocycline is effective to prevent neuron loss. To systematically evaluate its effects, minocycline was used to treat Dicer conditional knockout (cKO) mice which display age-related neuron loss. The drug was given to mutant mice prior to the occurrence of neuroinflammation and neurodegeneration, and the treatment had lasted 2 months. Levels of inflammation markers, including glial fibrillary acidic protein (GFAP), ionized calcium-binding adapter molecule1 (Iba1) and interleukin6 (IL6), were significantly reduced in minocycline-treated Dicer cKO mice. In contrast, levels of neuronal markers and the total number of apoptotic cells in Dicer cKO mice were not affected by the drug. In summary, inhibition of neuroinflammation by minocycline is insufficient to prevent neuron loss and apoptosis. PMID:26000566

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. 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...

  9. 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.

  10. 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.

  11. Neuron-specific antioxidant OXR1 extends survival of a mouse model of amyotrophic lateral sclerosis.

    Science.gov (United States)

    Liu, Kevin X; Edwards, Benjamin; Lee, Sheena; Finelli, Mattéa J; Davies, Ben; Davies, Kay E; Oliver, Peter L

    2015-05-01

    Amyotrophic lateral sclerosis is a devastating neurodegenerative disorder characterized by the progressive loss of spinal motor neurons. While the aetiological mechanisms underlying the disease remain poorly understood, oxidative stress is a central component of amyotrophic lateral sclerosis and contributes to motor neuron injury. Recently, oxidation resistance 1 (OXR1) has emerged as a critical regulator of neuronal survival in response to oxidative stress, and is upregulated in the spinal cord of patients with amyotrophic lateral sclerosis. Here, we tested the hypothesis that OXR1 is a key neuroprotective factor during amyotrophic lateral sclerosis pathogenesis by crossing a new transgenic mouse line that overexpresses OXR1 in neurons with the SOD1(G93A) mouse model of amyotrophic lateral sclerosis. Interestingly, we report that overexpression of OXR1 significantly extends survival, improves motor deficits, and delays pathology in the spinal cord and in muscles of SOD1(G93A) mice. Furthermore, we find that overexpression of OXR1 in neurons significantly delays non-cell-autonomous neuroinflammatory response, classic complement system activation, and STAT3 activation through transcriptomic analysis of spinal cords of SOD1(G93A) mice. Taken together, these data identify OXR1 as the first neuron-specific antioxidant modulator of pathogenesis and disease progression in SOD1-mediated amyotrophic lateral sclerosis, and suggest that OXR1 may serve as a novel target for future therapeutic strategies. © The Author (2015). Published by Oxford University Press on behalf of the Guarantors of Brain.

  12. Rat model of cancer-induced bone pain: changes in nonnociceptive sensory neurons in vivo

    Directory of Open Access Journals (Sweden)

    Yong Fang Zhu

    2017-08-01

    Conclusion:. After induction of the CIBP model, Aβ-fiber LTMs at >2 weeks but not <1 week had undergone changes in electrophysiological properties. Importantly, changes observed are consistent with observations in models of peripheral neuropathy. Thus, Aβ-fiber nonnociceptive primary sensory neurons might be involved in the peripheral sensitization and tumor-induced tactile hypersensitivity in CIBP.

  13. 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

  14. 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.

  15. 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)

  16. 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.

  17. 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.

  18. 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.

  19. 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.

  20. Postnatal Gene Therapy Improves Spatial Learning Despite the Presence of Neuronal Ectopia in a Model of Neuronal Migration Disorder

    Directory of Open Access Journals (Sweden)

    Huaiyu Hu

    2016-11-01

    Full Text Available Patients with type II lissencephaly, a neuronal migration disorder with ectopic neurons, suffer from severe mental retardation, including learning deficits. There is no effective therapy to prevent or correct the formation of neuronal ectopia, which is presumed to cause cognitive deficits. We hypothesized that learning deficits were not solely caused by neuronal ectopia and that postnatal gene therapy could improve learning without correcting the neuronal ectopia formed during fetal development. To test this hypothesis, we evaluated spatial learning of cerebral cortex-specific protein O-mannosyltransferase 2 (POMT2, an enzyme required for O-mannosyl glycosylation knockout mice and compared to the knockout mice that were injected with an adeno-associated viral vector (AAV encoding POMT2 into the postnatal brains with Barnes maze. The data showed that the knockout mice exhibited reduced glycosylation in the cerebral cortex, reduced dendritic spine density on CA1 neurons, and increased latency to the target hole in the Barnes maze, indicating learning deficits. Postnatal gene therapy restored functional glycosylation, rescued dendritic spine defects, and improved performance on the Barnes maze by the knockout mice even though neuronal ectopia was not corrected. These results indicate that postnatal gene therapy improves spatial learning despite the presence of neuronal ectopia.

  1. 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

  2. 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.

  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. 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

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. 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.

  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. 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.

  13. 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.

  14. 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

  15. Metastable states and quasicycles in a stochastic Wilson-Cowan model of neuronal population dynamics

    KAUST Repository

    Bressloff, Paul C.

    2010-01-01

    We analyze a stochastic model of neuronal population dynamics with intrinsic noise. In the thermodynamic limit N→∞, where N determines the size of each population, the dynamics is described by deterministic Wilson-Cowan equations. On the other hand

  16. Computational Model of a Positive BDNF Feedback Loop in Hippocampal Neurons Following Inhibitory Avoidance Training

    Science.gov (United States)

    Zhang, Yili; Smolen, Paul; Alberini, Cristina M.; Baxter, Douglas A.; Byrne, John H.

    2016-01-01

    Inhibitory avoidance (IA) training in rodents initiates a molecular cascade within hippocampal neurons. This cascade contributes to the transition of short- to long-term memory (i.e., consolidation). Here, a differential equation-based model was developed to describe a positive feedback loop within this molecular cascade. The feedback loop begins…

  17. 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

  18. 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)

  19. 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.

  20. A Model to Explain the Emergence of Reward Expectancy neurons using Reinforcement Learning and Neural Network

    OpenAIRE

    Shinya, Ishii; Munetaka, Shidara; Katsunari, Shibata

    2006-01-01

    In an experiment of multi-trial task to obtain a reward, reward expectancy neurons,###which responded only in the non-reward trials that are necessary to advance###toward the reward, have been observed in the anterior cingulate cortex of monkeys.###In this paper, to explain the emergence of the reward expectancy neuron in###terms of reinforcement learning theory, a model that consists of a recurrent neural###network trained based on reinforcement learning is proposed. The analysis of the###hi...

  1. 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.

  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. 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. 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

  5. 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.

  6. 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

  7. 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...

  8. 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.

  9. 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.

  10. 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.

  11. 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

  12. 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

  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. 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.

  15. 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

  16. Selective disruption of acetylcholine synthesis in subsets of motor neurons: a new model of late-onset motor neuron disease.

    Science.gov (United States)

    Lecomte, Marie-José; Bertolus, Chloé; Santamaria, Julie; Bauchet, Anne-Laure; Herbin, Marc; Saurini, Françoise; Misawa, Hidemi; Maisonobe, Thierry; Pradat, Pierre-François; Nosten-Bertrand, Marika; Mallet, Jacques; Berrard, Sylvie

    2014-05-01

    Motor neuron diseases are characterized by the selective chronic dysfunction of a subset of motor neurons and the subsequent impairment of neuromuscular function. To reproduce in the mouse these hallmarks of diseases affecting motor neurons, we generated a mouse line in which ~40% of motor neurons in the spinal cord and the brainstem become unable to sustain neuromuscular transmission. These mice were obtained by conditional knockout of the gene encoding choline acetyltransferase (ChAT), the biosynthetic enzyme for acetylcholine. The mutant mice are viable and spontaneously display abnormal phenotypes that worsen with age including hunched back, reduced lifespan, weight loss, as well as striking deficits in muscle strength and motor function. This slowly progressive neuromuscular dysfunction is accompanied by muscle fiber histopathological features characteristic of neurogenic diseases. Unexpectedly, most changes appeared with a 6-month delay relative to the onset of reduction in ChAT levels, suggesting that compensatory mechanisms preserve muscular function for several months and then are overwhelmed. Deterioration of mouse phenotype after ChAT gene disruption is a specific aging process reminiscent of human pathological situations, particularly among survivors of paralytic poliomyelitis. These mutant mice may represent an invaluable tool to determine the sequence of events that follow the loss of function of a motor neuron subset as the disease progresses, and to evaluate therapeutic strategies. They also offer the opportunity to explore fundamental issues of motor neuron biology. Copyright © 2014 Elsevier Inc. All rights reserved.

  17. 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.

  18. 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.

  19. 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.

  20. Vagus nerve stimulation improves locomotion and neuronal populations in a model of Parkinson's disease.

    Science.gov (United States)

    Farrand, Ariana Q; Helke, Kristi L; Gregory, Rebecca A; Gooz, Monika; Hinson, Vanessa K; Boger, Heather A

    Parkinson's disease (PD) is a progressive, neurodegenerative disorder with no disease-modifying therapies, and symptomatic treatments are often limited by debilitating side effects. In PD, locus coeruleus noradrenergic (LC-NE) neurons degenerate prior to substantia nigra dopaminergic (SN-DA) neurons. Vagus nerve stimulation (VNS) activates LC neurons, and decreases pro-inflammatory markers, allowing improvement of LC targets, making it a potential PD therapeutic. To assess therapeutic potential of VNS in a PD model. To mimic the progression of PD degeneration, rats received a systemic injection of noradrenergic neurotoxin DSP-4, followed one week later by bilateral intrastriatal injection of dopaminergic neurotoxin 6-hydroxydopamine. At this time, a subset of rats also had vagus cuffs implanted. After eleven days, rats received a precise VNS regimen twice a day for ten days, and locomotion was measured during each afternoon session. Immediately following final stimulation, rats were euthanized, and left dorsal striatum, bilateral SN and LC were sectioned for immunohistochemical detection of monoaminergic neurons (tyrosine hydroxylase, TH), α-synuclein, astrocytes (GFAP) and microglia (Iba-1). VNS significantly increased locomotion of lesioned rats. VNS also resulted in increased expression of TH in striatum, SN, and LC; decreased SN α-synuclein expression; and decreased expression of glial markers in the SN and LC of lesioned rats. Additionally, saline-treated rats after VNS, had higher LC TH and lower SN Iba-1. Our findings of increased locomotion, beneficial effects on LC-NE and SN-DA neurons, decreased α-synuclein density in SN TH-positive neurons, and neuroinflammation suggest VNS has potential as a novel PD therapeutic. Copyright © 2017 Elsevier Inc. All rights reserved.

  1. Complex Behavior in an Integrate-and-Fire Neuron Model Based on Small World Networks

    International Nuclear Information System (INIS)

    Lin Min; Chen Tianlun

    2005-01-01

    Based on our previously pulse-coupled integrate-and-fire neuron model in small world networks, we investigate the complex behavior of electroencephalographic (EEG)-like activities produced by such a model. We find EEG-like activities have obvious chaotic characteristics. We also analyze the complex behaviors of EEG-like signals, such as spectral analysis, reconstruction of the phase space, the correlation dimension, and so on.

  2. 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

  3. 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

  4. Oleuropein Prevents Neuronal Death, Mitigates Mitochondrial Superoxide Production and Modulates Autophagy in a Dopaminergic Cellular Model

    Directory of Open Access Journals (Sweden)

    Imène Achour

    2016-08-01

    Full Text Available Parkinson’s disease (PD is a progressive neurodegenerative disorder, primarily affecting dopaminergic neurons in the substantia nigra. There is currently no cure for PD and present medications aim to alleviate clinical symptoms, thus prevention remains the ideal strategy to reduce the prevalence of this disease. The goal of this study was to investigate whether oleuropein (OLE, the major phenolic compound in olive derivatives, may prevent neuronal degeneration in a cellular dopaminergic model of PD, differentiated PC12 cells exposed to the potent parkinsonian toxin 6-hydroxydopamine (6-OHDA. We also investigated OLE’s ability to mitigate mitochondrial oxidative stress and modulate the autophagic flux. Our results obtained by measuring cytotoxicity and apoptotic events demonstrate that OLE significantly decreases neuronal death. OLE could also reduce mitochondrial production of reactive oxygen species resulting from blocking superoxide dismutase activity. Moreover, quantification of autophagic and acidic vesicles in the cytoplasm alongside expression of specific autophagic markers uncovered a regulatory role for OLE against autophagic flux impairment induced by bafilomycin A1. Altogether, our results define OLE as a neuroprotective, anti-oxidative and autophagy-regulating molecule, in a neuronal dopaminergic cellular model.

  5. Seizure-like afterdischarges simulated in a model neuron.

    NARCIS (Netherlands)

    Kager, J.; Wadman, W.J.; Somjen, G.G.

    2006-01-01

    To explore non-synaptic mechanisms in paroxysmal discharges, we used a computer model of a simplified hippocampal pyramidal cell, surrounded by interstitial space and a "glial-endothelial" buffer system. Ion channels for Na(+), K(+), Ca(2+) and Cl(-) (,) ion antiport 3Na/Ca, and "active" ion pumps

  6. 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.

  7. 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.

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

    Science.gov (United States)

    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.

  9. 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.

  10. 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.

  11. Electrodiffusive model for astrocytic and neuronal ion concentration dynamics.

    Directory of Open Access Journals (Sweden)

    Geir Halnes

    Full Text Available The cable equation is a proper framework for modeling electrical neural signalling that takes place at a timescale at which the ionic concentrations vary little. However, in neural tissue there are also key dynamic processes that occur at longer timescales. For example, endured periods of intense neural signaling may cause the local extracellular K(+-concentration to increase by several millimolars. The clearance of this excess K(+ depends partly on diffusion in the extracellular space, partly on local uptake by astrocytes, and partly on intracellular transport (spatial buffering within astrocytes. These processes, that take place at the time scale of seconds, demand a mathematical description able to account for the spatiotemporal variations in ion concentrations as well as the subsequent effects of these variations on the membrane potential. Here, we present a general electrodiffusive formalism for modeling of ion concentration dynamics in a one-dimensional geometry, including both the intra- and extracellular domains. Based on the Nernst-Planck equations, this formalism ensures that the membrane potential and ion concentrations are in consistency, it ensures global particle/charge conservation and it accounts for diffusion and concentration dependent variations in resistivity. We apply the formalism to a model of astrocytes exchanging ions with the extracellular space. The simulations show that K(+-removal from high-concentration regions is driven by a local depolarization of the astrocyte membrane, which concertedly (i increases the local astrocytic uptake of K(+, (ii suppresses extracellular transport of K(+, (iii increases axial transport of K(+ within astrocytes, and (iv facilitates astrocytic relase of K(+ in regions where the extracellular concentration is low. Together, these mechanisms seem to provide a robust regulatory scheme for shielding the extracellular space from excess K(+.

  12. Mitochondrial mislocalization underlies Abeta42-induced neuronal dysfunction in a Drosophila model of Alzheimer's disease.

    Directory of Open Access Journals (Sweden)

    Kanae Iijima-Ando

    2009-12-01

    Full Text Available The amyloid-beta 42 (Abeta42 is thought to play a central role in the pathogenesis of Alzheimer's disease (AD. However, the molecular mechanisms by which Abeta42 induces neuronal dysfunction and degeneration remain elusive. Mitochondrial dysfunctions are implicated in AD brains. Whether mitochondrial dysfunctions are merely a consequence of AD pathology, or are early seminal events in AD pathogenesis remains to be determined. Here, we show that Abeta42 induces mitochondrial mislocalization, which contributes to Abeta42-induced neuronal dysfunction in a transgenic Drosophila model. In the Abeta42 fly brain, mitochondria were reduced in axons and dendrites, and accumulated in the somata without severe mitochondrial damage or neurodegeneration. In contrast, organization of microtubule or global axonal transport was not significantly altered at this stage. Abeta42-induced behavioral defects were exacerbated by genetic reductions in mitochondrial transport, and were modulated by cAMP levels and PKA activity. Levels of putative PKA substrate phosphoproteins were reduced in the Abeta42 fly brains. Importantly, perturbations in mitochondrial transport in neurons were sufficient to disrupt PKA signaling and induce late-onset behavioral deficits, suggesting a mechanism whereby mitochondrial mislocalization contributes to Abeta42-induced neuronal dysfunction. These results demonstrate that mislocalization of mitochondria underlies the pathogenic effects of Abeta42 in vivo.

  13. 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.

  14. 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.

  15. 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.

  16. Channel noise enhances signal detectability in a model of acoustic neuron through the stochastic resonance paradigm.

    Science.gov (United States)

    Liberti, M; Paffi, A; Maggio, F; De Angelis, A; Apollonio, F; d'Inzeo, G

    2009-01-01

    A number of experimental investigations have evidenced the extraordinary sensitivity of neuronal cells to weak input stimulations, including electromagnetic (EM) fields. Moreover, it has been shown that biological noise, due to random channels gating, acts as a tuning factor in neuronal processing, according to the stochastic resonant (SR) paradigm. In this work the attention is focused on noise arising from the stochastic gating of ionic channels in a model of Ranvier node of acoustic fibers. The small number of channels gives rise to a high noise level, which is able to cause a spike train generation even in the absence of stimulations. A SR behavior has been observed in the model for the detection of sinusoidal signals at frequencies typical of the speech.

  17. 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.

  18. Spread of neuronal degeneration in a dopaminergic, Lrrk-G2019S model of Parkinson disease

    Science.gov (United States)

    Hindle, Samantha J.; Elliott, Christopher J.H.

    2013-01-01

    Flies expressing the most common Parkinson disease (PD)-related mutation, LRRK2-G2019S, in their dopaminergic neurons show loss of visual function and degeneration of the retina, including mitochondrial abnormalities, apoptosis and autophagy. Since the photoreceptors that degenerate are not dopaminergic, this demonstrates nonautonomous degeneration, and a spread of pathology. This provides a model consistent with Braak’s hypothesis on progressive PD. The loss of visual function is specific for the G2019S mutation, implying the cause is its increased kinase activity, and is enhanced by increased neuronal activity. These data suggest novel explanations for the variability in animal models of PD. The specificity of visual loss to G2019S, coupled with the differences in neural firing rate, provide an explanation for the variability between people with PD in visual tests. PMID:23529190

  19. Arnold tongues and the Devil's Staircase in a discrete-time Hindmarsh–Rose neuron model

    International Nuclear Information System (INIS)

    Felicio, Carolini C.; Rech, Paulo C.

    2015-01-01

    We investigate a three-dimensional discrete-time dynamical system, described by a three-dimensional map derived from a continuous-time Hindmarsh–Rose neuron model by the forward Euler method. For a fixed integration step size, we report a two-dimensional parameter-space for this system, where periodic structures, the so-called Arnold tongues, can be seen with periods organized in a Farey tree sequence. We also report possible modifications in this parameter-space, as a function of the integration step size. - Highlights: • We investigate the parameter-space of a particular 3D map. • Periodic structures, namely Arnold tongues, can be seen there. • They are organized in a Farey tree sequence. • The map was derived from a continuous-time Hindmarsh–Rose neuron model. • The forward Euler method was used for such purpose.

  20. Dynamics in a Delayed Neural Network Model of Two Neurons with Inertial Coupling

    Directory of Open Access Journals (Sweden)

    Changjin Xu

    2012-01-01

    Full Text Available A delayed neural network model of two neurons with inertial coupling is dealt with in this paper. The stability is investigated and Hopf bifurcation is demonstrated. Applying the normal form theory and the center manifold argument, we derive the explicit formulas for determining the properties of the bifurcating periodic solutions. An illustrative example is given to demonstrate the effectiveness of the obtained results.

  1. Errors in estimation of the input signal for integrate-and-fire neuronal models

    Czech Academy of Sciences Publication Activity Database

    Bibbona, E.; Lánský, Petr; Sacerdote, L.; Sirovich, R.

    2008-01-01

    Roč. 78, č. 1 (2008), s. 1-10 ISSN 1539-3755 R&D Projects: GA MŠk(CZ) LC554; GA AV ČR(CZ) 1ET400110401 Grant - others:EC(XE) MIUR PRIN 2005 Institutional research plan: CEZ:AV0Z50110509 Keywords : parameter estimation * stochastic neuronal model Subject RIV: BO - Biophysics Impact factor: 2.508, year: 2008 http://link.aps.org/abstract/PRE/v78/e011918

  2. 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.

  3. 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.

  4. An ultra-low-voltage electronic implementation of inertial neuron model with nonmonotonous Liao's activation function.

    Science.gov (United States)

    Kant, Nasir Ali; Dar, Mohamad Rafiq; Khanday, Farooq Ahmad

    2015-01-01

    The output of every neuron in neural network is specified by the employed activation function (AF) and therefore forms the heart of neural networks. As far as the design of artificial neural networks (ANNs) is concerned, hardware approach is preferred over software one because it promises the full utilization of the application potential of ANNs. Therefore, besides some arithmetic blocks, designing AF in hardware is the most important for designing ANN. While attempting to design the AF in hardware, the designs should be compatible with the modern Very Large Scale Integration (VLSI) design techniques. In this regard, the implemented designs should: only be in Metal Oxide Semiconductor (MOS) technology in order to be compatible with the digital designs, provide electronic tunability feature, and be able to operate at ultra-low voltage. Companding is one of the promising circuit design techniques for achieving these goals. In this paper, 0.5 V design of Liao's AF using sinh-domain technique is introduced. Furthermore, the function is tested by implementing inertial neuron model. The performance of the AF and inertial neuron model have been evaluated through simulation results, using the PSPICE software with the MOS transistor models provided by the 0.18-μm Taiwan Semiconductor Manufacturer Complementary Metal Oxide Semiconductor (TSM CMOS) process.

  5. Functional integration of grafted neural stem cell-derived dopaminergic neurons monitored by optogenetics in an in vitro Parkinson model.

    Directory of Open Access Journals (Sweden)

    Jan Tønnesen

    Full Text Available Intrastriatal grafts of stem cell-derived dopamine (DA neurons induce behavioral recovery in animal models of Parkinson's disease (PD, but how they functionally integrate in host neural circuitries is poorly understood. Here, Wnt5a-overexpressing neural stem cells derived from embryonic ventral mesencephalon of tyrosine hydroxylase-GFP transgenic mice were expanded as neurospheres and transplanted into organotypic cultures of wild type mouse striatum. Differentiated GFP-labeled DA neurons in the grafts exhibited mature neuronal properties, including spontaneous firing of action potentials, presence of post-synaptic currents, and functional expression of DA D₂ autoreceptors. These properties resembled those recorded from identical cells in acute slices of intrastriatal grafts in the 6-hydroxy-DA-induced mouse PD model and from DA neurons in intact substantia nigra. Optogenetic activation or inhibition of grafted cells and host neurons using channelrhodopsin-2 (ChR2 and halorhodopsin (NpHR, respectively, revealed complex, bi-directional synaptic interactions between grafted cells and host neurons and extensive synaptic connectivity within the graft. Our data demonstrate for the first time using optogenetics that ectopically grafted stem cell-derived DA neurons become functionally integrated in the DA-denervated striatum. Further optogenetic dissection of the synaptic wiring between grafted and host neurons will be crucial to clarify the cellular and synaptic mechanisms underlying behavioral recovery as well as adverse effects following stem cell-based DA cell replacement strategies in PD.

  6. Stochastic resonance induced by novel random transitions of motion of FitzHugh-Nagumo neuron model

    International Nuclear Information System (INIS)

    Zhang Guangjun; Xu Jianxue

    2005-01-01

    In contrast to the previous studies which have dealt with stochastic resonance induced by random transitions of system motion between two coexisting limit cycle attractors in the FitzHugh-Nagumo (FHN) neuron model after Hopf bifurcation and which have dealt with the phenomenon of stochastic resonance induced by external noise when the model with periodic input has only one attractor before Hopf bifurcation, in this paper we have focused our attention on stochastic resonance (SR) induced by a novel transition behavior, the transitions of motion of the model among one attractor on the left side of bifurcation point and two attractors on the right side of bifurcation point under the perturbation of noise. The results of research show: since one bifurcation of transition from one to two limit cycle attractors and the other bifurcation of transition from two to one limit cycle attractors occur in turn besides Hopf bifurcation, the novel transitions of motion of the model occur when bifurcation parameter is perturbed by weak internal noise; the bifurcation point of the model may stochastically slightly shift to the left or right when FHN neuron model is perturbed by external Gaussian distributed white noise, and then the novel transitions of system motion also occur under the perturbation of external noise; the novel transitions could induce SR alone, and when the novel transitions of motion of the model and the traditional transitions between two coexisting limit cycle attractors after bifurcation occur in the same process the SR also may occur with complicated behaviors types; the mechanism of SR induced by external noise when FHN neuron model with periodic input has only one attractor before Hopf bifurcation is related to this kind of novel transition mentioned above

  7. 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.

  8. Human Brain Networks: Spiking Neuron Models, Multistability, Synchronization, Thermodynamics, Maximum Entropy Production, and Anesthetic Cascade Mechanisms

    Directory of Open Access Journals (Sweden)

    Wassim M. Haddad

    2014-07-01

    Full Text Available Advances in neuroscience have been closely linked to mathematical modeling beginning with the integrate-and-fire model of Lapicque and proceeding through the modeling of the action potential by Hodgkin and Huxley to the current era. The fundamental building block of the central nervous system, the neuron, may be thought of as a dynamic element that is “excitable”, and can generate a pulse or spike whenever the electrochemical potential across the cell membrane of the neuron exceeds a threshold. A key application of nonlinear dynamical systems theory to the neurosciences is to study phenomena of the central nervous system that exhibit nearly discontinuous transitions between macroscopic states. A very challenging and clinically important problem exhibiting this phenomenon is the induction of general anesthesia. In any specific patient, the transition from consciousness to unconsciousness as the concentration of anesthetic drugs increases is very sharp, resembling a thermodynamic phase transition. This paper focuses on multistability theory for continuous and discontinuous dynamical systems having a set of multiple isolated equilibria and/or a continuum of equilibria. Multistability is the property whereby the solutions of a dynamical system can alternate between two or more mutually exclusive Lyapunov stable and convergent equilibrium states under asymptotically slowly changing inputs or system parameters. In this paper, we extend the theory of multistability to continuous, discontinuous, and stochastic nonlinear dynamical systems. In particular, Lyapunov-based tests for multistability and synchronization of dynamical systems with continuously differentiable and absolutely continuous flows are established. The results are then applied to excitatory and inhibitory biological neuronal networks to explain the underlying mechanism of action for anesthesia and consciousness from a multistable dynamical system perspective, thereby providing a

  9. A Computational Model to Investigate Astrocytic Glutamate Uptake Influence on Synaptic Transmission and Neuronal Spiking

    Directory of Open Access Journals (Sweden)

    Sushmita Lakshmi Allam

    2012-10-01

    Full Text Available Over the past decades, our view of astrocytes has switched from passive support cells to active processing elements in the brain. The current view is that astrocytes shape neuronal communication and also play an important role in many neurodegenerative diseases. Despite the growing awareness of the importance of astrocytes, the exact mechanisms underlying neuron-astrocyte communication and the physiological consequences of astrocytic-neuronal interactions remain largely unclear. In this work, we define a modeling framework that will permit to address unanswered questions regarding the role of astrocytes. Our computational model of a detailed glutamatergic synapse facilitates the analysis of neural system responses to various stimuli and conditions that are otherwise difficult to obtain experimentally, in particular the readouts at the sub-cellular level. In this paper, we extend a detailed glutamatergic synaptic model, to include astrocytic glutamate transporters. We demonstrate how these glial transporters, responsible for the majority of glutamate uptake, modulate synaptic transmission mediated by ionotropic AMPA and NMDA receptors at glutamatergic synapses. Furthermore, we investigate how these local signaling effects at the synaptic level are translated into varying spatio-temporal patterns of neuron firing. Paired pulse stimulation results reveal that the effect of astrocytic glutamate uptake is more apparent when the input inter-spike interval is sufficiently long to allow the receptors to recover from desensitization. These results suggest an important functional role of astrocytes in spike timing dependent processes and demand further investigation of the molecular basis of certain neurological diseases specifically related to alterations in astrocytic glutamate uptake, such as epilepsy.

  10. Mathematical models for sleep-wake dynamics: comparison of the two-process model and a mutual inhibition neuronal model.

    Directory of Open Access Journals (Sweden)

    Anne C Skeldon

    Full Text Available Sleep is essential for the maintenance of the brain and the body, yet many features of sleep are poorly understood and mathematical models are an important tool for probing proposed biological mechanisms. The most well-known mathematical model of sleep regulation, the two-process model, models the sleep-wake cycle by two oscillators: a circadian oscillator and a homeostatic oscillator. An alternative, more recent, model considers the mutual inhibition of sleep promoting neurons and the ascending arousal system regulated by homeostatic and circadian processes. Here we show there are fundamental similarities between these two models. The implications are illustrated with two important sleep-wake phenomena. Firstly, we show that in the two-process model, transitions between different numbers of daily sleep episodes can be classified as grazing bifurcations. This provides the theoretical underpinning for numerical results showing that the sleep patterns of many mammals can be explained by the mutual inhibition model. Secondly, we show that when sleep deprivation disrupts the sleep-wake cycle, ostensibly different measures of sleepiness in the two models are closely related. The demonstration of the mathematical similarities of the two models is valuable because not only does it allow some features of the two-process model to be interpreted physiologically but it also means that knowledge gained from study of the two-process model can be used to inform understanding of the behaviour of the mutual inhibition model. This is important because the mutual inhibition model and its extensions are increasingly being used as a tool to understand a diverse range of sleep-wake phenomena such as the design of optimal shift-patterns, yet the values it uses for parameters associated with the circadian and homeostatic processes are very different from those that have been experimentally measured in the context of the two-process model.

  11. 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

  12. 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.

  13. Using chick forebrain neurons to model neurodegeneration and protection in an undergraduate neuroscience laboratory course.

    Science.gov (United States)

    Burdo, Joseph R

    2013-01-01

    Since 2009 at Boston College, we have been offering a Research in Neuroscience course using cultured neurons in an in vitro model of stroke. The students work in groups to learn how to perform sterile animal cell culture and run several basic bioassays to assess cell viability. They are then tasked with analyzing the scientific literature in an attempt to identify and predict the intracellular pathways involved in neuronal death, and identify dietary antioxidant compounds that may provide protection based on their known effects in other cells. After each group constructs a hypothesis pertaining to the potential neuroprotection, we purchase one compound per group and the students test their hypotheses using a commonly performed viability assay. The groups generate quantitative data and perform basic statistics on that data to analyze it for statistical significance. Finally, the groups compile their data and other elements of their research experience into a poster for our departmental research celebration at the end of the spring semester.

  14. The CNP signal is able to silence a supra threshold neuronal model

    Directory of Open Access Journals (Sweden)

    Francesca eCamera

    2015-04-01

    Full Text Available Several experimental results published in the literature showed that weak pulsed magnetic fields affected the response of the central nervous system. However, the specific biological mechanisms that regulate the observed behaviors are still unclear and further scientific investigation is required. In this work we performed simulations on a neuronal network model exposed to a specific pulsed magnetic field signal that seems to be very effective in modulating the brain activity: the Complex Neuroelectromagnetic Pulse (CNP. Results show that CNP can silence the neurons of a feed-forward network for signal intensities that depend on the strength of the bias current, the endogenous noise level and the specific waveforms of the pulses.

  15. Alzheimer's Proteins, Oxidative Stress, and Mitochondrial Dysfunction Interplay in a Neuronal Model of Alzheimer's Disease

    Directory of Open Access Journals (Sweden)

    Antonella Bobba

    2010-01-01

    Full Text Available In this paper, we discuss the interplay between beta-amyloid (A peptide, Tau fragments, oxidative stress, and mitochondria in the neuronal model of cerebellar granule neurons (CGNs in which the molecular events reminiscent of AD are activated. The identification of the death route and the cause/effect relationships between the events leading to death could be helpful to manage the progression of apoptosis in neurodegeneration and to define antiapoptotic treatments acting on precocious steps of the death process. Mitochondrial dysfunction is among the earliest events linked to AD and might play a causative role in disease onset and progression. Recent studies on CGNs have shown that adenine nucleotide translocator (ANT impairment, due to interaction with toxic N-ter Tau fragment, contributes in a significant manner to bioenergetic failure and mitochondrial dysfunction. These findings open a window for new therapeutic strategies aimed at preserving and/or improving mitochondrial function.

  16. 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...

  17. Exendin-4 ameliorates motor neuron degeneration in cellular and animal models of amyotrophic lateral sclerosis.

    Directory of Open Access Journals (Sweden)

    Yazhou Li

    Full Text Available Amyotrophic lateral sclerosis (ALS is a devastating neurodegenerative disease characterized by a progressive loss of lower motor neurons in the spinal cord. The incretin hormone, glucagon-like peptide-1 (GLP-1, facilitates insulin signaling, and the long acting GLP-1 receptor agonist exendin-4 (Ex-4 is currently used as an anti-diabetic drug. GLP-1 receptors are widely expressed in the brain and spinal cord, and our prior studies have shown that Ex-4 is neuroprotective in several neurodegenerative disease rodent models, including stroke, Parkinson's disease and Alzheimer's disease. Here we hypothesized that Ex-4 may provide neuroprotective activity in ALS, and hence characterized Ex-4 actions in both cell culture (NSC-19 neuroblastoma cells and in vivo (SOD1 G93A mutant mice models of ALS. Ex-4 proved to be neurotrophic in NSC-19 cells, elevating choline acetyltransferase (ChAT activity, as well as neuroprotective, protecting cells from hydrogen peroxide-induced oxidative stress and staurosporine-induced apoptosis. Additionally, in both wild-type SOD1 and mutant SOD1 (G37R stably transfected NSC-19 cell lines, Ex-4 protected against trophic factor withdrawal-induced toxicity. To assess in vivo translation, SOD1 mutant mice were administered vehicle or Ex-4 at 6-weeks of age onwards to end-stage disease via subcutaneous osmotic pump to provide steady-state infusion. ALS mice treated with Ex-4 showed improved glucose tolerance and normalization of behavior, as assessed by running wheel, compared to control ALS mice. Furthermore, Ex-4 treatment attenuated neuronal cell death in the lumbar spinal cord; immunohistochemical analysis demonstrated the rescue of neuronal markers, such as ChAT, associated with motor neurons. Together, our results suggest that GLP-1 receptor agonists warrant further evaluation to assess whether their neuroprotective potential is of therapeutic relevance in ALS.

  18. 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

  19. 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

  20. 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

  1. Impulsive control and synchronization of chaotic Hindmarsh-Rose models for neuronal activity

    International Nuclear Information System (INIS)

    Wu Quanjun; Zhou Jin; Xiang Lan; Liu Zengrong

    2009-01-01

    The issues of impulsive control and synchronization of chaotic Hindmarsh-Rose model are investigated in this paper. Based on impulsive control theory of dynamical systems, some simple yet less conservative criteria ensuring impulsive stabilization and synchronization of the Hindmarsh-Rose models are derived analytically. Furthermore, two numerical results are presented to demonstrate the effectiveness of the proposed control techniques. It is shown that the obtained results should be helpful to understand dynamical mechanism of signal encoding and transduction from information processing of real neuronal activity.

  2. 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.

  3. 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

  4. Whole-Brain Mapping of Neuronal Activity in the Learned Helplessness Model of Depression.

    Science.gov (United States)

    Kim, Yongsoo; Perova, Zinaida; Mirrione, Martine M; Pradhan, Kith; Henn, Fritz A; Shea, Stephen; Osten, Pavel; Li, Bo

    2016-01-01

    Some individuals are resilient, whereas others succumb to despair in repeated stressful situations. The neurobiological mechanisms underlying such divergent behavioral responses remain unclear. Here, we employed an automated method for mapping neuronal activity in search of signatures of stress responses in the entire mouse brain. We used serial two-photon tomography to detect expression of c-FosGFP - a marker of neuronal activation - in c-fosGFP transgenic mice subjected to the learned helplessness (LH) procedure, a widely used model of stress-induced depression-like phenotype in laboratory animals. We found that mice showing "helpless" behavior had an overall brain-wide reduction in the level of neuronal activation compared with mice showing "resilient" behavior, with the exception of a few brain areas, including the locus coeruleus, that were more activated in the helpless mice. In addition, the helpless mice showed a strong trend of having higher similarity in whole-brain activity profile among individuals, suggesting that helplessness is represented by a more stereotypic brain-wide activation pattern. This latter effect was confirmed in rats subjected to the LH procedure, using 2-deoxy-2[18F]fluoro-D-glucose positron emission tomography to assess neural activity. Our findings reveal distinct brain activity markings that correlate with adaptive and maladaptive behavioral responses to stress, and provide a framework for further studies investigating the contribution of specific brain regions to maladaptive stress responses.

  5. Whole-brain mapping of neuronal activity in the learned helplessness model of depression

    Directory of Open Access Journals (Sweden)

    Yongsoo eKim

    2016-02-01

    Full Text Available Some individuals are resilient, whereas others succumb to despair in repeated stressful situations. The neurobiological mechanisms underlying such divergent behavioral responses remain unclear. Here, we employed an automated method for mapping neuronal activity in search of signatures of stress responses in the entire mouse brain. We used serial two-photon tomography to detect expression of c-FosGFP – a marker of neuronal activation – in c-fosGFP transgenic mice subjected to the learned helplessness (LH procedure, a widely used model of stress-induced depression-like phenotype in laboratory animals. We found that mice showing helpless behavior had an overall brain-wide reduction in the level of neuronal activation compared with mice showing resilient behavior, with the exception of a few brain areas, including the locus coeruleus, that were more activated in the helpless mice. In addition, the helpless mice showed a strong trend of having higher similarity in whole brain activity profile among individuals, suggesting that helplessness is represented by a more stereotypic brain-wide activation pattern. This latter effect was confirmed in rats subjected to the LH procedure, using 2-deoxy-2[18F]fluoro-D-glucose positron emission tomography to assess neural activity. Our findings reveal distinct brain activity markings that correlate with adaptive and maladaptive behavioral responses to stress, and provide a framework for further studies investigating the contribution of specific brain regions to maladaptive stress responses.

  6. Biophysical properties and computational modeling of calcium spikes in serotonergic neurons of the dorsal raphe nucleus.

    Science.gov (United States)

    Tuckwell, Henry C

    2013-06-01

    Serotonergic neurons of the dorsal raphe nuclei, with their extensive innervation of nearly the whole brain have important modulatory effects on many cognitive and physiological processes. They play important roles in clinical depression and other psychiatric disorders. In order to quantify the effects of serotonergic transmission on target cells it is desirable to construct computational models and to this end these it is necessary to have details of the biophysical and spike properties of the serotonergic neurons. Here several basic properties are reviewed with data from several studies since the 1960s to the present. The quantities included are input resistance, resting membrane potential, membrane time constant, firing rate, spike duration, spike and afterhyperpolarization (AHP) amplitude, spike threshold, cell capacitance, soma and somadendritic areas. The action potentials of these cells are normally triggered by a combination of sodium and calcium currents which may result in autonomous pacemaker activity. We here analyse the mechanisms of high-threshold calcium spikes which have been demonstrated in these cells the presence of TTX (tetrodotoxin). The parameters for calcium dynamics required to give calcium spikes are quite different from those for regular spiking which suggests the involvement of restricted parts of the soma-dendritic surface as has been found, for example, in hippocampal neurons. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

  7. Model System for Live Imaging of Neuronal Responses to Injury and Repair

    Directory of Open Access Journals (Sweden)

    Mathieu Gravel

    2011-11-01

    Full Text Available Although it has been well established that induction of growth-associated protein-43 (GAP-43 during development coincides with axonal outgrowth and early synapse formation, the existence of neuronal plasticity and neurite outgrowth in the adult central nervous system after injuries is more controversial. To visualize the processes of neuronal injury and repair in living animals, we generated reporter mice for bioluminescence and fluorescence imaging bearing the luc (luciferase and gfp (green fluorescent protein reporter genes under the control of the murine GAP-43 promoter. Reporter functionality was first observed during the development of transgenic embryos. Using in vivo bioluminescence and fluorescence imaging, we visualized induction of the GAP-43 signals from live embryos starting at E10.5, as well as neuronal responses to brain and peripheral nerve injuries (the signals peaked at 14 days postinjury. Moreover, three-dimensional analysis of the GAP-43 bioluminescent signal confirmed that it originated from brain structures affected by ischemic injury. The analysis of fluorescence signal at cellular level revealed colocalization between endogenous protein and the GAP-43-driven gfp transgene. Taken together, our results suggest that the GAP-43-luc/gfp reporter mouse represents a valid model system for real-time analysis of neurite outgrowth and the capacity of the adult nervous system to regenerate after injuries.

  8. iPSC-Based Models to Unravel Key Pathogenetic Processes Underlying Motor Neuron Disease Development

    Directory of Open Access Journals (Sweden)

    Irene Faravelli

    2014-10-01

    Full Text Available Motor neuron diseases (MNDs are neuromuscular disorders affecting rather exclusively upper motor neurons (UMNs and/or lower motor neurons (LMNs. The clinical phenotype is characterized by muscular weakness and atrophy leading to paralysis and almost invariably death due to respiratory failure. Adult MNDs include sporadic and familial amyotrophic lateral sclerosis (sALS-fALS, while the most common infantile MND is represented by spinal muscular atrophy (SMA. No effective treatment is ccurrently available for MNDs, as for the vast majority of neurodegenerative disorders, and cures are limited to supportive care and symptom relief. The lack of a deep understanding of MND pathogenesis accounts for the difficulties in finding a cure, together with the scarcity of reliable in vitro models. Recent progresses in stem cell field, in particular in the generation of induced Pluripotent Stem Cells (iPSCs has made possible for the first time obtaining substantial amounts of human cells to recapitulate in vitro some of the key pathogenetic processes underlying MNDs. In the present review, recently published studies involving the use of iPSCs to unravel aspects of ALS and SMA pathogenesis are discussed with an overview of their implications in the process of finding a cure for these still orphan disorders.

  9. Enhanced neuronal glucose transporter expression reveals metabolic choice in a HD Drosophila model.

    Science.gov (United States)

    Besson, Marie Thérèse; Alegría, Karin; Garrido-Gerter, Pamela; Barros, Luis Felipe; Liévens, Jean-Charles

    2015-01-01

    Huntington's disease is a neurodegenerative disorder caused by toxic insertions of polyglutamine residues in the Huntingtin protein and characterized by progressive deterioration of cognitive and motor functions. Altered brain glucose metabolism has long been suggested and a possible link has been proposed in HD. However, the precise function of glucose transporters was not yet determined. Here, we report the effects of the specifically-neuronal human glucose transporter expression in neurons of a Drosophila model carrying the exon 1 of the human huntingtin gene with 93 glutamine repeats (HQ93). We demonstrated that overexpression of the human glucose transporter in neurons ameliorated significantly the status of HD flies by increasing their lifespan, reducing their locomotor deficits and rescuing eye neurodegeneration. Then, we investigated whether increasing the major pathways of glucose catabolism, glycolysis and pentose-phosphate pathway (PPP) impacts HD. To mimic increased glycolytic flux, we overexpressed phosphofructokinase (PFK) which catalyzes an irreversible step in glycolysis. Overexpression of PFK did not affect HQ93 fly survival, but protected from photoreceptor loss. Overexpression of glucose-6-phosphate dehydrogenase (G6PD), the key enzyme of the PPP, extended significantly the lifespan of HD flies and rescued eye neurodegeneration. Since G6PD is able to synthesize NADPH involved in cell survival by maintenance of the redox state, we showed that tolerance to experimental oxidative stress was enhanced in flies co-expressing HQ93 and G6PD. Additionally overexpressions of hGluT3, G6PD or PFK were able to circumvent mitochondrial deficits induced by specific silencing of genes necessary for mitochondrial homeostasis. Our study confirms the involvement of bioenergetic deficits in HD course; they can be rescued by specific expression of a glucose transporter in neurons. Finally, the PPP and, to a lesser extent, the glycolysis seem to mediate the hGluT3

  10. Enhanced neuronal glucose transporter expression reveals metabolic choice in a HD Drosophila model.

    Directory of Open Access Journals (Sweden)

    Marie Thérèse Besson

    Full Text Available Huntington's disease is a neurodegenerative disorder caused by toxic insertions of polyglutamine residues in the Huntingtin protein and characterized by progressive deterioration of cognitive and motor functions. Altered brain glucose metabolism has long been suggested and a possible link has been proposed in HD. However, the precise function of glucose transporters was not yet determined. Here, we report the effects of the specifically-neuronal human glucose transporter expression in neurons of a Drosophila model carrying the exon 1 of the human huntingtin gene with 93 glutamine repeats (HQ93. We demonstrated that overexpression of the human glucose transporter in neurons ameliorated significantly the status of HD flies by increasing their lifespan, reducing their locomotor deficits and rescuing eye neurodegeneration. Then, we investigated whether increasing the major pathways of glucose catabolism, glycolysis and pentose-phosphate pathway (PPP impacts HD. To mimic increased glycolytic flux, we overexpressed phosphofructokinase (PFK which catalyzes an irreversible step in glycolysis. Overexpression of PFK did not affect HQ93 fly survival, but protected from photoreceptor loss. Overexpression of glucose-6-phosphate dehydrogenase (G6PD, the key enzyme of the PPP, extended significantly the lifespan of HD flies and rescued eye neurodegeneration. Since G6PD is able to synthesize NADPH involved in cell survival by maintenance of the redox state, we showed that tolerance to experimental oxidative stress was enhanced in flies co-expressing HQ93 and G6PD. Additionally overexpressions of hGluT3, G6PD or PFK were able to circumvent mitochondrial deficits induced by specific silencing of genes necessary for mitochondrial homeostasis. Our study confirms the involvement of bioenergetic deficits in HD course; they can be rescued by specific expression of a glucose transporter in neurons. Finally, the PPP and, to a lesser extent, the glycolysis seem to

  11. 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)

  12. 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.

  13. 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.

  14. 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

  15. Structural and functional properties of a probabilistic model of neuronal connectivity in a simple locomotor network

    Science.gov (United States)

    Merrison-Hort, Robert; Soffe, Stephen R; Borisyuk, Roman

    2018-01-01

    Although, in most animals, brain connectivity varies between individuals, behaviour is often similar across a species. What fundamental structural properties are shared across individual networks that define this behaviour? We describe a probabilistic model of connectivity in the hatchling Xenopus tadpole spinal cord which, when combined with a spiking model, reliably produces rhythmic activity corresponding to swimming. The probabilistic model allows calculation of structural characteristics that reflect common network properties, independent of individual network realisations. We use the structural characteristics to study examples of neuronal dynamics, in the complete network and various sub-networks, and this allows us to explain the basis for key experimental findings, and make predictions for experiments. We also study how structural and functional features differ between detailed anatomical connectomes and those generated by our new, simpler, model (meta-model). PMID:29589828

  16. Patient iPSC-derived neurons for disease modeling of frontotemporal dementia with mutation in CHMP2B

    DEFF Research Database (Denmark)

    Zhang, Yu; Schmid, Benjamin; Nikolaisen, Nanett Kvist

    2017-01-01

    -to-lysosome trafficking and substrate degradation. To understand the underlying molecular pathology, FTD3 patient induced pluripotent stem cells (iPSCs) were differentiated into forebrain-type cortical neurons. FTD3 neurons exhibited abnormal endosomes, as previously shown in patients. Moreover, mitochondria of FTD3...... in neurodegenerative diseases. All phenotypes observed in FTD3 neurons were rescued in CRISPR/Cas9-edited isogenic controls. These findings illustrate the relevance of our patient-specific in vitro models and open up possibilities for drug target development....

  17. A quantitative overview of biophysical forces impinging on neural function

    International Nuclear Information System (INIS)

    Mueller, Jerel K; Tyler, William J

    2014-01-01

    The fundamentals of neuronal membrane excitability are globally described using the Hodgkin-Huxley (HH) model. The HH model, however, does not account for a number of biophysical phenomena associated with action potentials or propagating nerve impulses. Physical mechanisms underlying these processes, such as reversible heat transfer and axonal swelling, have been compartmentalized and separately investigated to reveal neuronal activity is not solely influenced by electrical or biochemical factors. Instead, mechanical forces and thermodynamics also govern neuronal excitability and signaling. To advance our understanding of neuronal function and dysfunction, compartmentalized analyses of electrical, chemical, and mechanical processes need to be revaluated and integrated into more comprehensive theories. The present perspective is intended to provide a broad overview of biophysical forces that can influence neural function, but which have been traditionally underappreciated in neuroscience. Further, several examples where mechanical forces have been shown to exert their actions on nervous system development, signaling, and plasticity are highlighted to underscore their importance in sculpting neural function. By considering the collective actions of biophysical forces influencing neuronal activity, our working models can be expanded and new paradigms can be applied to the investigation and characterization of brain function and dysfunction. (topical review)

  18. 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.

  19. 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)

  20. Functional integration of grafted neural stem cell-derived dopaminergic neurons monitored by optogenetics in an in vitro Parkinson model

    DEFF Research Database (Denmark)

    Tønnesen, Jan; Parish, Clare L; Sørensen, Andreas T

    2011-01-01

    Intrastriatal grafts of stem cell-derived dopamine (DA) neurons induce behavioral recovery in animal models of Parkinson's disease (PD), but how they functionally integrate in host neural circuitries is poorly understood. Here, Wnt5a-overexpressing neural stem cells derived from embryonic ventral...... of post-synaptic currents, and functional expression of DA D₂ autoreceptors. These properties resembled those recorded from identical cells in acute slices of intrastriatal grafts in the 6-hydroxy-DA-induced mouse PD model and from DA neurons in intact substantia nigra. Optogenetic activation...... using optogenetics that ectopically grafted stem cell-derived DA neurons become functionally integrated in the DA-denervated striatum. Further optogenetic dissection of the synaptic wiring between grafted and host neurons will be crucial to clarify the cellular and synaptic mechanisms underlying...

  1. Sexually dimorphic distribution of Prokr2 neurons revealed by the Prokr2-Cre mouse model.

    Science.gov (United States)

    Mohsen, Zaid; Sim, Hosung; Garcia-Galiano, David; Han, Xingfa; Bellefontaine, Nicole; Saunders, Thomas L; Elias, Carol F

    2017-12-01

    Prokineticin receptor 2 (PROKR2) is predominantly expressed in the mammalian central nervous system. Loss-of-function mutations of PROKR2 in humans are associated with Kallmann syndrome due to the disruption of gonadotropin releasing hormone neuronal migration and deficient olfactory bulb morphogenesis. PROKR2 has been also implicated in the neuroendocrine control of GnRH neurons post-migration and other physiological systems. However, the brain circuitry and mechanisms associated with these actions have been difficult to investigate mainly due to the widespread distribution of Prokr2-expressing cells, and the lack of animal models and molecular tools. Here, we describe the generation, validation and characterization of a new mouse model that expresses Cre recombinase driven by the Prokr2 promoter, using CRISPR-Cas9 technology. Cre expression was visualized using reporter genes, tdTomato and GFP, in males and females. Expression of Cre-induced reporter genes was found in brain sites previously described to express Prokr2, e.g., the paraventricular and the suprachiasmatic nuclei, and the area postrema. The Prokr2-Cre mouse model was further validated by colocalization of Cre-induced GFP and Prokr2 mRNA. No disruption of Prokr2 expression, GnRH neuronal migration or fertility was observed. Comparative analysis of Prokr2-Cre expression in male and female brains revealed a sexually dimorphic distribution confirmed by in situ hybridization. In females, higher Cre activity was found in the medial preoptic area, ventromedial nucleus of the hypothalamus, arcuate nucleus, medial amygdala and lateral parabrachial nucleus. In males, Cre was higher in the amygdalo-hippocampal area. The sexually dimorphic pattern of Prokr2 expression indicates differential roles in reproductive function and, potentially, in other physiological systems.

  2. Novel model of neuronal bioenergetics: postsynaptic utilization of glucose but not lactate correlates positively with Ca2+ signalling in cultured mouse glutamatergic neurons.

    Science.gov (United States)

    Bak, Lasse K; Obel, Linea F; Walls, Anne B; Schousboe, Arne; Faek, Sevan A A; Jajo, Farah S; Waagepetersen, Helle S

    2012-04-05

    We have previously investigated the relative roles of extracellular glucose and lactate as fuels for glutamatergic neurons during synaptic activity. The conclusion from these studies was that cultured glutamatergic neurons utilize glucose rather than lactate during NMDA (N-methyl-d-aspartate)-induced synaptic activity and that lactate alone is not able to support neurotransmitter glutamate homoeostasis. Subsequently, a model was proposed to explain these results at the cellular level. In brief, the intermittent rises in intracellular Ca2+ during activation cause influx of Ca2+ into the mitochondrial matrix thus activating the tricarboxylic acid cycle dehydrogenases. This will lead to a lower activity of the MASH (malate-aspartate shuttle), which in turn will result in anaerobic glycolysis and lactate production rather than lactate utilization. In the present work, we have investigated the effect of an ionomycin-induced increase in intracellular Ca2+ (i.e. independent of synaptic activity) on neuronal energy metabolism employing 13C-labelled glucose and lactate and subsequent mass spectrometric analysis of labelling in glutamate, alanine and lactate. The results demonstrate that glucose utilization is positively correlated with intracellular Ca2+ whereas lactate utilization is not. This result lends further support for a significant role of glucose in neuronal bioenergetics and that Ca2+ signalling may control the switch between glucose and lactate utilization during synaptic activity. Based on the results, we propose a compartmentalized CiMASH (Ca2+-induced limitation of the MASH) model that includes intracellular compartmentation of glucose and lactate metabolism. We define pre- and post-synaptic compartments metabolizing glucose and glucose plus lactate respectively in which the latter displays a positive correlation between oxidative metabolism of glucose and Ca2+ signalling.

  3. Dynamics of a neuron model in different two-dimensional parameter-spaces

    Science.gov (United States)

    Rech, Paulo C.

    2011-03-01

    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.

  4. Oxidative Stress Associated with Neuronal Apoptosis in Experimental Models of Epilepsy

    Directory of Open Access Journals (Sweden)

    Marisela Méndez-Armenta

    2014-01-01

    Full Text Available Epilepsy is considered one of the most common neurological disorders worldwide. Oxidative stress produced by free radicals may play a role in the initiation and progression of epilepsy; the changes in the mitochondrial and the oxidative stress state can lead mechanism associated with neuronal death pathway. Bioenergetics state failure and impaired mitochondrial function include excessive free radical production with impaired synthesis of antioxidants. This review summarizes evidence that suggest what is the role of oxidative stress on induction of apoptosis in experimental models of epilepsy.

  5. Adaptive Neuron Model: An architecture for the rapid learning of nonlinear topological transformations

    Science.gov (United States)

    Tawel, Raoul (Inventor)

    1994-01-01

    A method for the rapid learning of nonlinear mappings and topological transformations using a dynamically reconfigurable artificial neural network is presented. This fully-recurrent Adaptive Neuron Model (ANM) network was applied to the highly degenerate inverse kinematics problem in robotics, and its performance evaluation is bench-marked. Once trained, the resulting neuromorphic architecture was implemented in custom analog neural network hardware and the parameters capturing the functional transformation downloaded onto the system. This neuroprocessor, capable of 10(exp 9) ops/sec, was interfaced directly to a three degree of freedom Heathkit robotic manipulator. Calculation of the hardware feed-forward pass for this mapping was benchmarked at approximately 10 microsec.

  6. 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.

  7. 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.

  8. 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.

  9. eGFP expression under the Uchl1 promoter labels corticospinal motor neurons and a subpopulation of degeneration resistant spinal motor neurons in ALS mouse models

    Science.gov (United States)

    Yasvoina, Marina V.

    Current understanding of basic cellular and molecular mechanisms for motor neuron vulnerability during motor neuron disease initiation and progression is incomplete. The complex cytoarchitecture and cellular heterogeneity of the cortex and spinal cord greatly impedes our ability to visualize, isolate, and study specific neuron populations in both healthy and diseased states. We generated a novel reporter line, the Uchl1-eGFP mouse, in which cortical and spinal components of motor neuron circuitry are genetically labeled with eGFP under the Uchl1 promoter. A series of cellular and anatomical analyses combined with retrograde labeling, molecular marker expression, and electrophysiology were employed to determine identity of eGFP expressing cells in the motor cortex and the spinal cord of novel Uchl1-eGFP reporter mice. We conclude that eGFP is expressed in corticospinal motor neurons (CSMN) in the motor cortex and a subset of S-type alpha and gamma spinal motor neurons (SMN) in the spinal cord. hSOD1G93A and Alsin-/- mice, mouse models for amyotrophic lateral sclerosis (ALS), were bred to Uchl1-eGFP reporter mouse line to investigate the pathophysiology and underlying mechanisms of CSMN degeneration in vivo. Evidence suggests early and progressive degeneration of CSMN and SMN in the hSOD1G93A transgenic mice. We show an early increase of autophagosome formation in the apical dendrites of vulnerable CSMN in hSOD1G93A-UeGFP mice, which is localized to the apical dendrites. In addition, labeling S-type alpha and gamma SMN in the hSOD1G93A-UeGFP mice provide a unique opportunity to study basis of their resistance to degeneration. Mice lacking alsin show moderate clinical phenotype and mild CSMN axon degeneration in the spinal cord, which suggests vulnerability of CSMN. Therefore, we investigated the CSMN cellular and axon defects in aged Alsin-/- mice bred to Uchl1-eGFP reporter mouse line. We show that while CSMN are preserved and lack signs of degeneration, CSMN axons

  10. Gastrodin Protects Apoptotic Dopaminergic Neurons in a Toxin-Induced Parkinson’s Disease Model

    Directory of Open Access Journals (Sweden)

    Hemant Kumar

    2013-01-01

    Full Text Available Gastrodia elata (GE Blume is one of the most important traditional plants in Oriental countries and has been used for centuries to improve various conditions. The phenolic glucoside gastrodin is an active constituent of GE. The aim of this study was to investigate the neuroprotective role of gastrodin in 1-methyl-4-phenylpyridinium (MPP+/1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine- (MPTP induced human dopaminergic SH-SY5Y cells and mouse model of Parkinson’s disease (PD, respectively. Gastrodin significantly and dose dependently protected dopaminergic neurons against neurotoxicity through regulating free radicals, Bax/Bcl-2 mRNA, caspase-3, and cleaved poly(ADP-ribose polymerase (PARP in SH-SY5Y cells stressed with MPP+. Gastrodin also showed neuroprotective effects in the subchronic MPTP mouse PD model by ameliorating bradykinesia and motor impairment in the pole and rotarod tests, respectively. Consistent with this finding, gastrodin prevented dopamine depletion and reduced reactive astrogliosis caused by MPTP as assessed by immunohistochemistry and immunoblotting in the substantiae nigrae and striatata of mice. Moreover, gastrodin was also effective in preventing neuronal apoptosis by attenuating antioxidant and antiapoptotic activities in these brain areas. These results strongly suggest that gastrodin has protective effects in experimental PD models and that it may be developed as a clinical candidate to ameliorate PD symptoms.

  11. A model-based spike sorting algorithm for removing correlation artifacts in multi-neuron recordings.

    Science.gov (United States)

    Pillow, Jonathan W; Shlens, Jonathon; Chichilnisky, E J; Simoncelli, Eero P

    2013-01-01

    We examine the problem of estimating the spike trains of multiple neurons from voltage traces recorded on one or more extracellular electrodes. Traditional spike-sorting methods rely on thresholding or clustering of recorded signals to identify spikes. While these methods can detect a large fraction of the spikes from a recording, they generally fail to identify synchronous or near-synchronous spikes: cases in which multiple spikes overlap. Here we investigate the geometry of failures in traditional sorting algorithms, and document the prevalence of such errors in multi-electrode recordings from primate retina. We then develop a method for multi-neuron spike sorting using a model that explicitly accounts for the superposition of spike waveforms. We model the recorded voltage traces as a linear combination of spike waveforms plus a stochastic background component of correlated Gaussian noise. Combining this measurement model with a Bernoulli prior over binary spike trains yields a posterior distribution for spikes given the recorded data. We introduce a greedy algorithm to maximize this posterior that we call "binary pursuit". The algorithm allows modest variability in spike waveforms and recovers spike times with higher precision than the voltage sampling rate. This method substantially corrects cross-correlation artifacts that arise with conventional methods, and substantially outperforms clustering methods on both real and simulated data. Finally, we develop diagnostic tools that can be used to assess errors in spike sorting in the absence of ground truth.

  12. Optimization behavior of brainstem respiratory neurons. A cerebral neural network model.

    Science.gov (United States)

    Poon, C S

    1991-01-01

    A recent model of respiratory control suggested that the steady-state respiratory responses to CO2 and exercise may be governed by an optimal control law in the brainstem respiratory neurons. It was not certain, however, whether such complex optimization behavior could be accomplished by a realistic biological neural network. To test this hypothesis, we developed a hybrid computer-neural model in which the dynamics of the lung, brain and other tissue compartments were simulated on a digital computer. Mimicking the "controller" was a human subject who pedalled on a bicycle with varying speed (analog of ventilatory output) with a view to minimize an analog signal of the total cost of breathing (chemical and mechanical) which was computed interactively and displayed on an oscilloscope. In this manner, the visuomotor cortex served as a proxy (homolog) of the brainstem respiratory neurons in the model. Results in 4 subjects showed a linear steady-state ventilatory CO2 response to arterial PCO2 during simulated CO2 inhalation and a nearly isocapnic steady-state response during simulated exercise. Thus, neural optimization is a plausible mechanism for respiratory control during exercise and can be achieved by a neural network with cognitive computational ability without the need for an exercise stimulus.

  13. Memory-induced resonancelike suppression of spike generation in a resonate-and-fire neuron model

    Science.gov (United States)

    Mankin, Romi; Paekivi, Sander

    2018-01-01

    The behavior of a stochastic resonate-and-fire neuron model based on a reduction of a fractional noise-driven generalized Langevin equation (GLE) with a power-law memory kernel is considered. The effect of temporally correlated random activity of synaptic inputs, which arise from other neurons forming local and distant networks, is modeled as an additive fractional Gaussian noise in the GLE. Using a first-passage-time formulation, in certain system parameter domains exact expressions for the output interspike interval (ISI) density and for the survival probability (the probability that a spike is not generated) are derived and their dependence on input parameters, especially on the memory exponent, is analyzed. In the case of external white noise, it is shown that at intermediate values of the memory exponent the survival probability is significantly enhanced in comparison with the cases of strong and weak memory, which causes a resonancelike suppression of the probability of spike generation as a function of the memory exponent. Moreover, an examination of the dependence of multimodality in the ISI distribution on input parameters shows that there exists a critical memory exponent αc≈0.402 , which marks a dynamical transition in the behavior of the system. That phenomenon is illustrated by a phase diagram describing the emergence of three qualitatively different structures of the ISI distribution. Similarities and differences between the behavior of the model at internal and external noises are also discussed.

  14. MicroRNA Profiling Reveals Marker of Motor Neuron Disease in ALS Models.

    Science.gov (United States)

    Hoye, Mariah L; Koval, Erica D; Wegener, Amy J; Hyman, Theodore S; Yang, Chengran; O'Brien, David R; Miller, Rebecca L; Cole, Tracy; Schoch, Kathleen M; Shen, Tao; Kunikata, Tomonori; Richard, Jean-Philippe; Gutmann, David H; Maragakis, Nicholas J; Kordasiewicz, Holly B; Dougherty, Joseph D; Miller, Timothy M

    2017-05-31

    Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder marked by the loss of motor neurons (MNs) in the brain and spinal cord, leading to fatally debilitating weakness. Because this disease predominantly affects MNs, we aimed to characterize the distinct expression profile of that cell type to elucidate underlying disease mechanisms and to identify novel targets that inform on MN health during ALS disease time course. microRNAs (miRNAs) are short, noncoding RNAs that can shape the expression profile of a cell and thus often exhibit cell-type-enriched expression. To determine MN-enriched miRNA expression, we used Cre recombinase-dependent miRNA tagging and affinity purification in mice. By defining the in vivo miRNA expression of MNs, all neurons, astrocytes, and microglia, we then focused on MN-enriched miRNAs via a comparative analysis and found that they may functionally distinguish MNs postnatally from other spinal neurons. Characterizing the levels of the MN-enriched miRNAs in CSF harvested from ALS models of MN disease demonstrated that one miRNA (miR-218) tracked with MN loss and was responsive to an ALS therapy in rodent models. Therefore, we have used cellular expression profiling tools to define the distinct miRNA expression of MNs, which is likely to enrich future studies of MN disease. This approach enabled the development of a novel, drug-responsive marker of MN disease in ALS rodents. SIGNIFICANCE STATEMENT Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease in which motor neurons (MNs) in the brain and spinal cord are selectively lost. To develop tools to aid in our understanding of the distinct expression profiles of MNs and, ultimately, to monitor MN disease progression, we identified small regulatory microRNAs (miRNAs) that were highly enriched or exclusive in MNs. The signal for one of these MN-enriched miRNAs is detectable in spinal tap biofluid from an ALS rat model, where its levels change as disease

  15. Protein carbonylation, protein aggregation and neuronal cell death in a murine model of multiple sclerosis

    Science.gov (United States)

    Dasgupta, Anushka

    Many studies have suggested that oxidative stress plays an important role in the pathophysiology of both multiple sclerosis (MS) and its animal model experimental autoimmune encephalomyelitis (EAE). Yet, the mechanism by which oxidative stress leads to tissue damage in these disorders is unclear. Recent work from our laboratory has revealed that protein carbonylation, a major oxidative modification caused by severe and/or chronic oxidative stress conditions, is elevated in MS and EAE. Furthermore, protein carbonylation has been shown to alter protein structure leading to misfolding/aggregation. These findings prompted me to hypothesize that carbonylated proteins, formed as a consequence of oxidative stress and/or decreased proteasomal activity, promote protein aggregation to mediate neuronal apoptosis in vitro and in EAE. To test this novel hypothesis, I first characterized protein carbonylation, protein aggregation and apoptosis along the spinal cord during the course of myelin-oligodendrocyte glycoprotein (MOG)35-55 peptide-induced EAE in C57BL/6 mice [Chapter 2]. The results show that carbonylated proteins accumulate throughout the course of the disease, albeit by different mechanisms: increased oxidative stress in acute EAE and decreased proteasomal activity in chronic EAE. I discovered not only that there is a temporal correlation between protein carbonylation and apoptosis but also that carbonyl levels are significantly higher in apoptotic cells. A high number of juxta-nuclear and cytoplasmic protein aggregates containing the majority of the oxidized proteins are also present during the course of EAE, which seems to be due to reduced autophagy. In chapter 3, I show that when gluthathione levels are reduced to those in EAE spinal cord, both neuron-like PC12 (nPC12) cells and primary neuronal cultures accumulate carbonylated proteins and undergo cell death (both by necrosis and apoptosis). Immunocytochemical and biochemical studies also revealed a temporal

  16. Automatic Generation of Connectivity for Large-Scale Neuronal Network Models through Structural Plasticity.

    Science.gov (United States)

    Diaz-Pier, Sandra; Naveau, Mikaël; Butz-Ostendorf, Markus; Morrison, Abigail

    2016-01-01

    With the emergence of new high performance computation technology in the last decade, the simulation of large scale neural networks which are able to reproduce the behavior and structure of the brain has finally become an achievable target of neuroscience. Due to the number of synaptic connections between neurons and the complexity of biological networks, most contemporary models have manually defined or static connectivity. However, it is expected that modeling the dynamic generation and deletion of the links among neurons, locally and between different regions of the brain, is crucial to unravel important mechanisms associated with learning, memory and healing. Moreover, for many neural circuits that could potentially be modeled, activity data is more readily and reliably available than connectivity data. Thus, a framework that enables networks to wire themselves on the basis of specified activity targets can be of great value in specifying network models where connectivity data is incomplete or has large error margins. To address these issues, in the present work we present an implementation of a model of structural plasticity in the neural network simulator NEST. In this model, synapses consist of two parts, a pre- and a post-synaptic element. Synapses are created and deleted during the execution of the simulation following local homeostatic rules until a mean level of electrical activity is reached in the network. We assess the scalability of the implementation in order to evaluate its potential usage in the self generation of connectivity of large scale networks. We show and discuss the results of simulations on simple two population networks and more complex models of the cortical microcircuit involving 8 populations and 4 layers using the new framework.

  17. Arginine vasopressin neuronal loss results from autophagy-associated cell death in a mouse model for familial neurohypophysial diabetes insipidus

    Science.gov (United States)

    Hagiwara, D; Arima, H; Morishita, Y; Wenjun, L; Azuma, Y; Ito, Y; Suga, H; Goto, M; Banno, R; Sugimura, Y; Shiota, A; Asai, N; Takahashi, M; Oiso, Y

    2014-01-01

    Familial neurohypophysial diabetes insipidus (FNDI) characterized by progressive polyuria is mostly caused by mutations in the gene encoding neurophysin II (NPII), which is the carrier protein of the antidiuretic hormone, arginine vasopressin (AVP). Although accumulation of mutant NPII in the endoplasmic reticulum (ER) could be toxic for AVP neurons, the precise mechanisms of cell death of AVP neurons, reported in autopsy studies, remain unclear. Here, we subjected FNDI model mice to intermittent water deprivation (WD) in order to promote the phenotypes. Electron microscopic analyses demonstrated that, while aggregates are confined to a certain compartment of the ER in the AVP neurons of FNDI mice with water access ad libitum, they were scattered throughout the dilated ER lumen in the FNDI mice subjected to WD for 4 weeks. It is also demonstrated that phagophores, the autophagosome precursors, emerged in the vicinity of aggregates and engulfed the ER containing scattered aggregates. Immunohistochemical analyses revealed that expression of p62, an adapter protein between ubiquitin and autophagosome, was elicited on autophagosomal membranes in the AVP neurons, suggesting selective autophagy induction at this time point. Treatment of hypothalamic explants of green fluorescent protein (GFP)-microtubule-associated protein 1 light chain 3 (LC3) transgenic mice with an ER stressor thapsigargin increased the number of GFP-LC3 puncta, suggesting that ER stress could induce autophagosome formation in the hypothalamus of wild-type mice as well. The cytoplasm of AVP neurons in FNDI mice was occupied with vacuoles in the mice subjected to WD for 12 weeks, when 30–40% of AVP neurons are lost. Our data thus demonstrated that autophagy was induced in the AVP neurons subjected to ER stress in FNDI mice. Although autophagy should primarily be protective for neurons, it is suggested that the organelles including ER were lost over time through autophagy, leading to autophagy

  18. 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.

  19. 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.

  20. Enhancement of a robust arcuate GABAergic input to gonadotropin-releasing hormone neurons in a model of polycystic ovarian syndrome.

    Science.gov (United States)

    Moore, Aleisha M; Prescott, Mel; Marshall, Christopher J; Yip, Siew Hoong; Campbell, Rebecca E

    2015-01-13

    Polycystic ovarian syndrome (PCOS), the leading cause of female infertility, is associated with an increase in luteinizing hormone (LH) pulse frequency, implicating abnormal steroid hormone feedback to gonadotropin-releasing hormone (GnRH) neurons. This study investigated whether modifications in the synaptically connected neuronal network of GnRH neurons could account for this pathology. The PCOS phenotype was induced in mice following prenatal androgen (PNA) exposure. Serial blood sampling confirmed that PNA elicits increased LH pulse frequency and impaired progesterone negative feedback in adult females, mimicking the neuroendocrine abnormalities of the clinical syndrome. Imaging of GnRH neurons revealed greater dendritic spine density that correlated with increased putative GABAergic but not glutamatergic inputs in PNA mice. Mapping of steroid hormone receptor expression revealed that PNA mice had 59% fewer progesterone receptor-expressing cells in the arcuate nucleus of the hypothalamus (ARN). To address whether increased GABA innervation to GnRH neurons originates in the ARN, a viral-mediated Cre-lox approach was taken to trace the projections of ARN GABA neurons in vivo. Remarkably, projections from ARN GABAergic neurons heavily contacted and even bundled with GnRH neuron dendrites, and the density of fibers apposing GnRH neurons was even greater in PNA mice (56%). Additionally, this ARN GABA population showed significantly less colocalization with progesterone receptor in PNA animals compared with controls. Together, these data describe a robust GABAergic circuit originating in the ARN that is enhanced in a model of PCOS and may underpin the neuroendocrine pathophysiology of the syndrome.

  1. Lychee Seed Saponins Improve Cognitive Function and Prevent Neuronal Injury via Inhibiting Neuronal Apoptosis in a Rat Model of Alzheimer’s Disease

    Directory of Open Access Journals (Sweden)

    Xiuling Wang

    2017-02-01

    Full Text Available Lychee seed is a traditional Chinese medicine and possesses many activities, including hypoglycemia, liver protection, antioxidation, antivirus, and antitumor. However, its effect on neuroprotection is still unclear. The present study investigated the effects of lychee seed saponins (LSS on neuroprotection and associated mechanisms. We established a rat model of Alzheimer’s disease (AD by injecting Aβ25–35 into the lateral ventricle of rats and evaluated the effect of LSS on spatial learning and memory ability via the Morris water maze. Neuronal apoptosis was analyzed by hematoxylin and eosin stain and terminal deoxynucleotidyl transferase (Tdt-mediated dUTP nick-end labeling analysis, and mRNA expression of caspase-3 and protein expressions of Bax and Bcl-2 by reverse transcription-polymerase chain reaction (RT-PCR and Western blotting, respectively. The results showed that LSS remarkably improved cognitive function and alleviated neuronal injury by inhibiting apoptosis in the hippocampus of AD rats. Furthermore, the mRNA expression of caspase-3 and the protein expression of Bax were downregulated, while the protein expression of Bcl-2 and the ratio of Bcl-2/Bax were increased by LSS. We demonstrate that LSS significantly improves cognitive function and prevent neuronal injury in the AD rats via regulation of the apoptosis pathway. Therefore, LSS may be developed as a nutritional supplement and sold as a drug for AD prevention and/or treatment.

  2. Chimera States in Neural Oscillators

    Science.gov (United States)

    Bahar, Sonya; Glaze, Tera

    2014-03-01

    Chimera states have recently been explored both theoretically and experimentally, in various coupled nonlinear oscillators, ranging from phase-oscillator models to coupled chemical reactions. In a chimera state, both coherent and incoherent (or synchronized and desynchronized) states occur simultaneously in populations of identical oscillators. We investigate chimera behavior in a population of neural oscillators using the Huber-Braun model, a Hodgkin-Huxley-like model originally developed to characterize the temperature-dependent bursting behavior of mammalian cold receptors. One population of neurons is allowed to synchronize, with each neuron receiving input from all the others in its group (global within-group coupling). Subsequently, a second population of identical neurons is placed under an identical global within-group coupling, and the two populations are also coupled to each other (between-group coupling). For certain values of the coupling constants, the neurons in the two populations exhibit radically different synchronization behavior. We will discuss the range of chimera activity in the model, and discuss its implications for actual neural activity, such as unihemispheric sleep.

  3. Irrelevant sensory stimuli interfere with working memory storage: evidence from a computational model of prefrontal neurons.

    Science.gov (United States)

    Bancroft, Tyler D; Hockley, William E; Servos, Philip

    2013-03-01

    The encoding of irrelevant stimuli into the memory store has previously been suggested as a mechanism of interference in working memory (e.g., Lange & Oberauer, Memory, 13, 333-339, 2005; Nairne, Memory & Cognition, 18, 251-269, 1990). Recently, Bancroft and Servos (Experimental Brain Research, 208, 529-532, 2011) used a tactile working memory task to provide experimental evidence that irrelevant stimuli were, in fact, encoded into working memory. In the present study, we replicated Bancroft and Servos's experimental findings using a biologically based computational model of prefrontal neurons, providing a neurocomputational model of overwriting in working memory. Furthermore, our modeling results show that inhibition acts to protect the contents of working memory, and they suggest a need for further experimental research into the capacity of vibrotactile working memory.

  4. Dynamic neuronal ensembles: Issues in representing structure change in object-oriented, biologically-based brain models

    Energy Technology Data Exchange (ETDEWEB)

    Vahie, S.; Zeigler, B.P.; Cho, H. [Univ. of Arizona, Tucson, AZ (United States)

    1996-12-31

    This paper describes the structure of dynamic neuronal ensembles (DNEs). DNEs represent a new paradigm for learning, based on biological neural networks that use variable structures. We present a computational neural element that demonstrates biological neuron functionality such as neurotransmitter feedback absolute refractory period and multiple output potentials. More specifically, we will develop a network of neural elements that have the ability to dynamically strengthen, weaken, add and remove interconnections. We demonstrate that the DNE is capable of performing dynamic modifications to neuron connections and exhibiting biological neuron functionality. In addition to its applications for learning, DNEs provide an excellent environment for testing and analysis of biological neural systems. An example of habituation and hyper-sensitization in biological systems, using a neural circuit from a snail is presented and discussed. This paper provides an insight into the DNE paradigm using models developed and simulated in DEVS.

  5. Inhibition of apoptosis blocks human motor neuron cell death in a stem cell model of spinal muscular atrophy.

    Directory of Open Access Journals (Sweden)

    Dhruv Sareen

    Full Text Available Spinal muscular atrophy (SMA is a genetic disorder caused by a deletion of the survival motor neuron 1 gene leading to motor neuron loss, muscle atrophy, paralysis, and death. We show here that induced pluripotent stem cell (iPSC lines generated from two Type I SMA subjects-one produced with lentiviral constructs and the second using a virus-free plasmid-based approach-recapitulate the disease phenotype and generate significantly fewer motor neurons at later developmental time periods in culture compared to two separate control subject iPSC lines. During motor neuron development, both SMA lines showed an increase in Fas ligand-mediated apoptosis and increased caspase-8 and-3 activation. Importantly, this could be mitigated by addition of either a Fas blocking antibody or a caspase-3 inhibitor. Together, these data further validate this human stem cell model of SMA, suggesting that specific inhibitors of apoptotic pathways may be beneficial for patients.

  6. Model-based iterative learning control of Parkinsonian state in thalamic relay neuron

    Science.gov (United States)

    Liu, Chen; Wang, Jiang; Li, Huiyan; Xue, Zhiqin; Deng, Bin; Wei, Xile

    2014-09-01

    Although the beneficial effects of chronic deep brain stimulation on Parkinson's disease motor symptoms are now largely confirmed, the underlying mechanisms behind deep brain stimulation remain unclear and under debate. Hence, the selection of stimulation parameters is full of challenges. Additionally, due to the complexity of neural system, together with omnipresent noises, the accurate model of thalamic relay neuron is unknown. Thus, the iterative learning control of the thalamic relay neuron's Parkinsonian state based on various variables is presented. Combining the iterative learning control with typical proportional-integral control algorithm, a novel and efficient control strategy is proposed, which does not require any particular knowledge on the detailed physiological characteristics of cortico-basal ganglia-thalamocortical loop and can automatically adjust the stimulation parameters. Simulation results demonstrate the feasibility of the proposed control strategy to restore the fidelity of thalamic relay in the Parkinsonian condition. Furthermore, through changing the important parameter—the maximum ionic conductance densities of low-threshold calcium current, the dominant characteristic of the proposed method which is independent of the accurate model can be further verified.

  7. Numerical simulation of coherent resonance in a model network of Rulkov neurons

    Science.gov (United States)

    Andreev, Andrey V.; Runnova, Anastasia E.; Pisarchik, Alexander N.

    2018-04-01

    In this paper we study the spiking behaviour of a neuronal network consisting of Rulkov elements. We find that the regularity of this behaviour maximizes at a certain level of environment noise. This effect referred to as coherence resonance is demonstrated in a random complex network of Rulkov neurons. An external stimulus added to some of neurons excites them, and then activates other neurons in the network. The network coherence is also maximized at the certain stimulus amplitude.

  8. Multiphasic on/off pheromone signalling in moths as neural correlates of a search strategy.

    Directory of Open Access Journals (Sweden)

    Dominique Martinez

    Full Text Available Insects and robots searching for odour sources in turbulent plumes face the same problem: the random nature of mixing causes fluctuations and intermittency in perception. Pheromone-tracking male moths appear to deal with discontinuous flows of information by surging upwind, upon sensing a pheromone patch, and casting crosswind, upon losing the plume. Using a combination of neurophysiological recordings, computational modelling and experiments with a cyborg, we propose a neuronal mechanism that promotes a behavioural switch between surge and casting. We show how multiphasic On/Off pheromone-sensitive neurons may guide action selection based on signalling presence or loss of the pheromone. A Hodgkin-Huxley-type neuron model with a small-conductance calcium-activated potassium (SK channel reproduces physiological On/Off responses. Using this model as a command neuron and the antennae of tethered moths as pheromone sensors, we demonstrate the efficiency of multiphasic patterning in driving a robotic searcher toward the source. Taken together, our results suggest that multiphasic On/Off responses may mediate olfactory navigation and that SK channels may account for these responses.

  9. Multiphasic on/off pheromone signalling in moths as neural correlates of a search strategy.

    Science.gov (United States)

    Martinez, Dominique; Chaffiol, Antoine; Voges, Nicole; Gu, Yuqiao; Anton, Sylvia; Rospars, Jean-Pierre; Lucas, Philippe

    2013-01-01

    Insects and robots searching for odour sources in turbulent plumes face the same problem: the random nature of mixing causes fluctuations and intermittency in perception. Pheromone-tracking male moths appear to deal with discontinuous flows of information by surging upwind, upon sensing a pheromone patch, and casting crosswind, upon losing the plume. Using a combination of neurophysiological recordings, computational modelling and experiments with a cyborg, we propose a neuronal mechanism that promotes a behavioural switch between surge and casting. We show how multiphasic On/Off pheromone-sensitive neurons may guide action selection based on signalling presence or loss of the pheromone. A Hodgkin-Huxley-type neuron model with a small-conductance calcium-activated potassium (SK) channel reproduces physiological On/Off responses. Using this model as a command neuron and the antennae of tethered moths as pheromone sensors, we demonstrate the efficiency of multiphasic patterning in driving a robotic searcher toward the source. Taken together, our results suggest that multiphasic On/Off responses may mediate olfactory navigation and that SK channels may account for these responses.

  10. Transient exposure to ethanol during zebrafish embryogenesis results in defects in neuronal differentiation: an alternative model system to study FASD.

    Directory of Open Access Journals (Sweden)

    Xavier Joya

    Full Text Available The exposure of the human embryo to ethanol results in a spectrum of disorders involving multiple organ systems, including the impairment of the development of the central nervous system (CNS. In spite of the importance for human health, the molecular basis of prenatal ethanol exposure remains poorly understood, mainly to the difficulty of sample collection. Zebrafish is now emerging as a powerful organism for the modeling and the study of human diseases. In this work, we have assessed the sensitivity of specific subsets of neurons to ethanol exposure during embryogenesis and we have visualized the sensitive embryonic developmental periods for specific neuronal groups by the use of different transgenic zebrafish lines.In order to evaluate the teratogenic effects of acute ethanol exposure, we exposed zebrafish embryos to ethanol in a given time window and analyzed the effects in neurogenesis, neuronal differentiation and brain patterning. Zebrafish larvae exposed to ethanol displayed small eyes and/or a reduction of the body length, phenotypical features similar to the observed in children with prenatal exposure to ethanol. When neuronal populations were analyzed, we observed a clear reduction in the number of differentiated neurons in the spinal cord upon ethanol exposure. There was a decrease in the population of sensory neurons mainly due to a decrease in cell proliferation and subsequent apoptosis during neuronal differentiation, with no effect in motoneuron specification.Our investigation highlights that transient exposure to ethanol during early embryonic development affects neuronal differentiation although does not result in defects in early neurogenesis. These results establish the use of zebrafish embryos as an alternative research model to elucidate the molecular mechanism(s of ethanol-induced developmental toxicity at very early stages of embryonic development.

  11. Lemon Odor Reduces Stress-induced Neuronal Activation in the Emotion Expression System: An Animal Model Study

    Science.gov (United States)

    Sanada, Kazue; Sugimoto, Koji; Shutoh, Fumihiro; Hisano, Setsuji

    Perception of particular sensory stimuli from the surroundings can influence emotion in individuals. In an uncomfortable situation, humans protect themselves from some aversive stimulus by acutely evoking a stress response. Animal model studies have contributed to an understanding of neuronal mechanisms underlying the stress response in humans. To study a possible anti-stressful effect of lemon odor, an excitation of neurons secreting corticotropin-releasing hormone (CRH) as a primary factor of the hypothalamic-pituitary-adrenal axis (HPA) was analyzed in animal model experiments, in which rats are restrained in the presence or absence of the odor. The effect was evaluated by measuring expression of c-Fos (an excited neuron marker) in the hypothalamic paraventricular nucleus (PVN), a key structure of the HPA in the brain. We prepared 3 animal groups: Groups S, L and I. Groups S and L were restrained for 30 minutes while being blown by air and being exposed to the lemon odor, respectively. Group I was intact without any treatment. Two hours later of the onset of experiments, brains of all groups were sampled and processed for microscopic examination. Brain sections were processed for c-Fos immunostaining and/or in situ hybridization for CRH. In Group S but not in Group I, c-Fos expression was found in the PVN. A combined in situ hybridization-immunohistochemical dual labeling revealed that CRH mRNA-expressing neurons express c-Fos. In computer-assisted automatic counting, the incidence of c-Fos-expressing neurons in the entire PVN was statistically lower in Group L than in Group S. Detailed analysis of PVN subregions demonstrated that c-Fos-expressing neurons are fewer in Group L than in Group S in the dorsal part of the medial parvocellular subregion. These results may suggest that lemon odor attenuates the restraint stress-induced neuronal activation including CRH neurons, presumably mimicking an aspect of stress responses in humans.

  12. Transient Exposure to Ethanol during Zebrafish Embryogenesis Results in Defects in Neuronal Differentiation: An Alternative Model System to Study FASD

    Science.gov (United States)

    Joya, Xavier; Garcia-Algar, Oscar; Vall, Oriol; Pujades, Cristina

    2014-01-01

    Background The exposure of the human embryo to ethanol results in a spectrum of disorders involving multiple organ systems, including the impairment of the development of the central nervous system (CNS). In spite of the importance for human health, the molecular basis of prenatal ethanol exposure remains poorly understood, mainly to the difficulty of sample collection. Zebrafish is now emerging as a powerful organism for the modeling and the study of human diseases. In this work, we have assessed the sensitivity of specific subsets of neurons to ethanol exposure during embryogenesis and we have visualized the sensitive embryonic developmental periods for specific neuronal groups by the use of different transgenic zebrafish lines. Methodology/Principal Findings In order to evaluate the teratogenic effects of acute ethanol exposure, we exposed zebrafish embryos to ethanol in a given time window and analyzed the effects in neurogenesis, neuronal differentiation and brain patterning. Zebrafish larvae exposed to ethanol displayed small eyes and/or a reduction of the body length, phenotypical features similar to the observed in children with prenatal exposure to ethanol. When neuronal populations were analyzed, we observed a clear reduction in the number of differentiated neurons in the spinal cord upon ethanol exposure. There was a decrease in the population of sensory neurons mainly due to a decrease in cell proliferation and subsequent apoptosis during neuronal differentiation, with no effect in motoneuron specification. Conclusion Our investigation highlights that transient exposure to ethanol during early embryonic development affects neuronal differentiation although does not result in defects in early neurogenesis. These results establish the use of zebrafish embryos as an alternative research model to elucidate the molecular mechanism(s) of ethanol-induced developmental toxicity at very early stages of embryonic development. PMID:25383948

  13. 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.

  14. 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.

  15. Phase transition approach to bursting in neuronal cultures: quorum percolation models

    Science.gov (United States)

    Monceau, P.; Renault, R.; Métens, S.; Bottani, S.; Fardet, T.

    2017-10-01

    The Quorum Percolation model has been designed in the context of neurobiology to describe bursts of activity occurring in neuronal cultures from the point of view of statistical physics rather than from a dynamical synchronization approach. It is based upon information propagation on a directed graph with a threshold activation rule; this leads to a phase diagram which exhibits a giant percolation cluster below some critical value mC of the excitability. We describe the main characteristics of the original model and derive extensions according to additional relevant biological features. Firstly, we investigate the effects of an excitability variability on the phase diagram and show that the percolation transition can be destroyed by a sufficient amount of such a disorder; we stress the weakly averaging character of the order parameter and show that connectivity and excitability can be seen as two overlapping aspects of the same reality. Secondly, we elaborate a discrete time stochastic model taking into account the decay originating from ionic leakage through the membrane of neurons and synaptic depression; we give evidence that the decay softens and shifts the transition, and conjecture than decay destroys the transition in the thermodynamical limit. We were able to develop mean-field theories associated with each of the two effects; we discuss the framework of their agreement with Monte Carlo simulations. It turns out that the the critical point mC from which information on the connectivity of the network can be inferred is affected by each of these additional effects. Lastly, we show how dynamical simulations of bursts with an adaptive exponential integrateand- fire model can be interpreted in terms of Quorum Percolation. Moreover, the usefulness of the percolation model including the set of sophistication we investigated can be extended to many scientific fields involving information propagation, such as the spread of rumors in sociology, ethology, ecology.

  16. 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

  17. 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.

  18. Bifurcation of synchronous oscillations into torus in a system of two reciprocally inhibitory silicon neurons: Experimental observation and modeling

    International Nuclear Information System (INIS)

    Bondarenko, Vladimir E.; Cymbalyuk, Gennady S.; Patel, Girish; DeWeerth, Stephen P.; Calabrese, Ronald L.

    2004-01-01

    Oscillatory activity in the central nervous system is associated with various functions, like motor control, memory formation, binding, and attention. Quasiperiodic oscillations are rarely discussed in the neurophysiological literature yet they may play a role in the nervous system both during normal function and disease. Here we use a physical system and a model to explore scenarios for how quasiperiodic oscillations might arise in neuronal networks. An oscillatory system of two mutually inhibitory neuronal units is a ubiquitous network module found in nervous systems and is called a half-center oscillator. Previously we created a half-center oscillator of two identical oscillatory silicon (analog Very Large Scale Integration) neurons and developed a mathematical model describing its dynamics. In the mathematical model, we have shown that an in-phase limit cycle becomes unstable through a subcritical torus bifurcation. However, the existence of this torus bifurcation in experimental silicon two-neuron system was not rigorously demonstrated or investigated. Here we demonstrate the torus predicted by the model for the silicon implementation of a half-center oscillator using complex time series analysis, including bifurcation diagrams, mapping techniques, correlation functions, amplitude spectra, and correlation dimensions, and we investigate how the properties of the quasiperiodic oscillations depend on the strengths of coupling between the silicon neurons. The potential advantages and disadvantages of quasiperiodic oscillations (torus) for biological neural systems and artificial neural networks are discussed

  19. Dream interpretation, affect, and the theory of neuronal group selection: Freud, Winnicott, Bion, and Modell.

    Science.gov (United States)

    Shields, Walker

    2006-12-01

    The author uses a dream specimen as interpreted during psychoanalysis to illustrate Modell's hypothesis that Edelman's theory of neuronal group selection (TNGS) may provide a valuable neurobiological model for Freud's dynamic unconscious, imaginative processes in the mind, the retranscription of memory in psychoanalysis, and intersubjective processes in the analytic relationship. He draws parallels between the interpretation of the dream material with keen attention to affect-laden meanings in the evolving analytic relationship in the domain of psychoanalysis and the principles of Edelman's TNGS in the domain of neurobiology. The author notes how this correlation may underscore the importance of dream interpretation in psychoanalysis. He also suggests areas for further investigation in both realms based on study of their interplay.

  20. Edaravone guards dopamine neurons in a rotenone model for Parkinson's disease.

    Directory of Open Access Journals (Sweden)

    Nian Xiong

    Full Text Available 3-methyl-1-phenyl-2-pyrazolin-5-one (edaravone, an effective free radical scavenger, provides neuroprotection in stroke models and patients. In this study, we investigated its neuroprotective effects in a chronic rotenone rat model for Parkinson's disease. Here we showed that a five-week treatment with edaravone abolished rotenone's activity to induce catalepsy, damage mitochondria and degenerate dopamine neurons in the midbrain of rotenone-treated rats. This abolishment was attributable at least partly to edaravone's inhibition of rotenone-induced reactive oxygen species production or apoptotic promoter Bax expression and its up-regulation of the vesicular monoamine transporter 2 (VMAT2 expression. Collectively, edaravone may provide novel clinical therapeutics for PD.

  1. Edaravone Guards Dopamine Neurons in a Rotenone Model for Parkinson's Disease

    Science.gov (United States)

    Chen, Chunnuan; Huang, Jinsha; Zhao, Ying; Zhang, Zhentao; Qiao, Xian; Feng, Yuan; Reesaul, Harrish; Zhang, Yongxue; Sun, Shenggang; Lin, Zhicheng; Wang, Tao

    2011-01-01

    3-methyl-1-phenyl-2-pyrazolin-5-one (edaravone), an effective free radical scavenger, provides neuroprotection in stroke models and patients. In this study, we investigated its neuroprotective effects in a chronic rotenone rat model for Parkinson's disease. Here we showed that a five-week treatment with edaravone abolished rotenone's activity to induce catalepsy, damage mitochondria and degenerate dopamine neurons in the midbrain of rotenone-treated rats. This abolishment was attributable at least partly to edaravone's inhibition of rotenone-induced reactive oxygen species production or apoptotic promoter Bax expression and its up-regulation of the vesicular monoamine transporter 2 (VMAT2) expression. Collectively, edaravone may provide novel clinical therapeutics for PD. PMID:21677777

  2. Dipeptide Piracetam Analogue Noopept Improves Viability of Hippocampal HT-22 Neurons in the Glutamate Toxicity Model.

    Science.gov (United States)

    Antipova, T A; Nikolaev, S V; Ostrovskaya, P U; Gudasheva, T A; Seredenin, S B

    2016-05-01

    Effect of noopept (N-phenylacetyl-prolylglycine ethyl ester) on viability of neurons exposed to neurotoxic action of glutamic acid (5 mM) was studied in vitro in immortalized mouse hippocampal HT-22 neurons. Noopept added to the medium before or after glutamic acid improved neuronal survival in a concentration range of 10-11-10-5 M. Comparison of the effective noopept concentrations determined in previous studies on cultured cortical and cerebellar neurons showed that hippocampal neurons are more sensitive to the protective effect of noopept.

  3. 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.

  4. 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.

  5. Computational modeling reveals dendritic origins of GABA(A-mediated excitation in CA1 pyramidal neurons.

    Directory of Open Access Journals (Sweden)

    Naomi Lewin

    Full Text Available GABA is the key inhibitory neurotransmitter in the adult central nervous system, but in some circumstances can lead to a paradoxical excitation that has been causally implicated in diverse pathologies from endocrine stress responses to diseases of excitability including neuropathic pain and temporal lobe epilepsy. We undertook a computational modeling approach to determine plausible ionic mechanisms of GABA(A-dependent excitation in isolated post-synaptic CA1 hippocampal neurons because it may constitute a trigger for pathological synchronous epileptiform discharge. In particular, the interplay intracellular chloride accumulation via the GABA(A receptor and extracellular potassium accumulation via the K/Cl co-transporter KCC2 in promoting GABA(A-mediated excitation is complex. Experimentally it is difficult to determine the ionic mechanisms of depolarizing current since potassium transients are challenging to isolate pharmacologically and much GABA signaling occurs in small, difficult to measure, dendritic compartments. To address this problem and determine plausible ionic mechanisms of GABA(A-mediated excitation, we built a detailed biophysically realistic model of the CA1 pyramidal neuron that includes processes critical for ion homeostasis. Our results suggest that in dendritic compartments, but not in the somatic compartments, chloride buildup is sufficient to cause dramatic depolarization of the GABA(A reversal potential and dominating bicarbonate currents that provide a substantial current source to drive whole-cell depolarization. The model simulations predict that extracellular K(+ transients can augment GABA(A-mediated excitation, but not cause it. Our model also suggests the potential for GABA(A-mediated excitation to promote network synchrony depending on interneuron synapse location - excitatory positive-feedback can occur when interneurons synapse onto distal dendritic compartments, while interneurons projecting to the perisomatic

  6. Ongoing spontaneous activity controls access to consciousness: a neuronal model for inattentional blindness.

    Directory of Open Access Journals (Sweden)

    Stanislas Dehaene

    2005-05-01

    Full Text Available Even in the absence of sensory inputs, cortical and thalamic neurons can show structured patterns of ongoing spontaneous activity, whose origins and functional significance are not well understood. We use computer simulations to explore the conditions under which spontaneous activity emerges from a simplified model of multiple interconnected thalamocortical columns linked by long-range, top-down excitatory axons, and to examine its interactions with stimulus-induced activation. Simulations help characterize two main states of activity. First, spontaneous gamma-band oscillations emerge at a precise threshold controlled by ascending neuromodulator systems. Second, within a spontaneously active network, we observe the sudden "ignition" of one out of many possible coherent states of high-level activity amidst cortical neurons with long-distance projections. During such an ignited state, spontaneous activity can block external sensory processing. We relate those properties to experimental observations on the neural bases of endogenous states of consciousness, and particularly the blocking of access to consciousness that occurs in the psychophysical phenomenon of "inattentional blindness," in which normal subjects intensely engaged in mental activity fail to notice salient but irrelevant sensory stimuli. Although highly simplified, the generic properties of a minimal network may help clarify some of the basic cerebral phenomena underlying the autonomy of consciousness.

  7. Ongoing spontaneous activity controls access to consciousness: a neuronal model for inattentional blindness.

    Science.gov (United States)

    Dehaene, Stanislas; Changeux, Jean-Pierre

    2005-05-01

    Even in the absence of sensory inputs, cortical and thalamic neurons can show structured patterns of ongoing spontaneous activity, whose origins and functional significance are not well understood. We use computer simulations to explore the conditions under which spontaneous activity emerges from a simplified model of multiple interconnected thalamocortical columns linked by long-range, top-down excitatory axons, and to examine its interactions with stimulus-induced activation. Simulations help characterize two main states of activity. First, spontaneous gamma-band oscillations emerge at a precise threshold controlled by ascending neuromodulator systems. Second, within a spontaneously active network, we observe the sudden "ignition" of one out of many possible coherent states of high-level activity amidst cortical neurons with long-distance projections. During such an ignited state, spontaneous activity can block external sensory processing. We relate those properties to experimental observations on the neural bases of endogenous states of consciousness, and particularly the blocking of access to consciousness that occurs in the psychophysical phenomenon of "inattentional blindness," in which normal subjects intensely engaged in mental activity fail to notice salient but irrelevant sensory stimuli. Although highly simplified, the generic properties of a minimal network may help clarify some of the basic cerebral phenomena underlying the autonomy of consciousness.

  8. Presynaptic learning and memory with a persistent firing neuron and a habituating synapse: a model of short term persistent habituation.

    Science.gov (United States)

    Ramanathan, Kiruthika; Ning, Ning; Dhanasekar, Dhiviya; Li, Guoqi; Shi, Luping; Vadakkepat, Prahlad

    2012-08-01

    Our paper explores the interaction of persistent firing axonal and presynaptic processes in the generation of short term memory for habituation. We first propose a model of a sensory neuron whose axon is able to switch between passive conduction and persistent firing states, thereby triggering short term retention to the stimulus. Then we propose a model of a habituating synapse and explore all nine of the behavioral characteristics of short term habituation in a two neuron circuit. We couple the persistent firing neuron to the habituation synapse and investigate the behavior of short term retention of habituating response. Simulations show that, depending on the amount of synaptic resources, persistent firing either results in continued habituation or maintains the response, both leading to longer recovery times. The effectiveness of the model as an element in a bio-inspired memory system is discussed.

  9. A spatially resolved network spike in model neuronal cultures reveals nucleation centers, circular traveling waves and drifting spiral waves.

    Science.gov (United States)

    Paraskevov, A V; Zendrikov, D K

    2017-03-23

    We show that in model neuronal cultures, where the probability of interneuronal connection formation decreases exponentially with increasing distance between the neurons, there exists a small number of spatial nucleation centers of a network spike, from where the synchronous spiking activity starts propagating in the network typically in the form of circular traveling waves. The number of nucleation centers and their spatial locations are unique and unchanged for a given realization of neuronal network but are different for different networks. In contrast, if the probability of interneuronal connection formation is independent of the distance between neurons, then the nucleation centers do not arise and the synchronization of spiking activity during a network spike occurs spatially uniform throughout the network. Therefore one can conclude that spatial proximity of connections between neurons is important for the formation of nucleation centers. It is also shown that fluctuations of the spatial density of neurons at their random homogeneous distribution typical for the experiments in vitro do not determine the locations of the nucleation centers. The simulation results are qualitatively consistent with the experimental observations.

  10. Ablating ErbB4 in PV neurons attenuates synaptic and cognitive deficits in an animal model of Alzheimer's disease.

    Science.gov (United States)

    Zhang, Heng; Zhang, Ling; Zhou, Dongming; He, Xiao; Wang, Dongpi; Pan, Hongyu; Zhang, Xiaoqin; Mei, Yufei; Qian, Qi; Zheng, Tingting; Jones, Frank E; Sun, Binggui

    2017-10-01

    Accumulation of amyloid β (Aβ) induces neuronal, synaptic, and cognitive deficits in patients and animal models of Alzheimer's disease (AD). The underlying mechanisms, however, remain to be fully elucidated. In the present study, we found that Aβ interacted with ErbB4, a member of the receptor tyrosine kinase family and mainly expressed in GABAergic interneurons. Deleting ErbB4 in parvalbumin-expressing neurons (PV neurons) significantly attenuated oligomeric Aβ-induced suppression of long term potentiation (LTP). Furthermore, specific ablation of ErbB4 in PV neurons via Cre/loxP system greatly improved spatial memory and synaptic plasticity in the hippocampus of hAPP-J20 mice. The deposition of Aβ detected by 3D6 and Thioflavin S staining and the proteolytic processing of hAPP analyzed by western blotting were not affected in the hippocampus of hAPP-J20 mice by deleting ErbB4 in PV neurons. Our data suggested that ErbB4 in PV neurons mediated Aβ-induced synaptic and cognitive dysfunctions without affecting Aβ levels. Copyright © 2017 Elsevier Inc. All rights reserved.

  11. 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

  12. Th17 Cells Induce Dopaminergic Neuronal Death via LFA-1/ICAM-1 Interaction in a Mouse Model of Parkinson's Disease.

    Science.gov (United States)

    Liu, Zhan; Huang, Yan; Cao, Bei-Bei; Qiu, Yi-Hua; Peng, Yu-Ping

    2017-12-01

    T helper (Th)17 cells, a subset of CD4 + T lymphocytes, have strong pro-inflammatory property and appear to be essential in the pathogenesis of many inflammatory diseases. However, the involvement of Th17 cells in Parkinson's disease (PD) that is characterized by a progressive degeneration of dopaminergic (DAergic) neurons in the nigrostriatal system is unclear. Here, we aimed to demonstrate that Th17 cells infiltrate into the brain parenchyma and induce neuroinflammation and DAergic neuronal death in 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)- or 1-methyl-4-phenylpyridinium (MPP + )-induced PD models. Blood-brain barrier (BBB) disruption in the substantia nigra (SN) was assessed by the signal of FITC-labeled albumin that was injected into blood circulation via the ascending aorta. Live cell imaging system was used to observe a direct contact of Th17 cells with neurons by staining these cells using the two adhesion molecules, leukocyte function-associated antigen (LFA)-1 and intercellular adhesion molecule (ICAM)-1, respectively. Th17 cells invaded into the SN where BBB was disrupted in MPTP-induced PD mice. Th17 cells exacerbated DAergic neuronal loss and pro-inflammatory/neurotrophic factor disorders in MPP + -treated ventral mesencephalic (VM) cell cultures. A direct contact of LFA-1-stained Th17 cells with ICAM-1-stained VM neurons was dynamically captured. Either blocking LFA-1 in Th17 cells or blocking ICAM-1 in VM neurons with neutralizing antibodies abolished Th17-induced DAergic neuronal death. These results establish that Th17 cells infiltrate into the brain parenchyma of PD mice through lesioned BBB and exert neurotoxic property by promoting glial activation and importantly by a direct damage to neurons depending on LFA-1/ICAM-1 interaction.

  13. 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

  14. 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.

  15. Conditional ablation of orexin/hypocretin neurons: a new mouse model for the study of narcolepsy and orexin system function.

    Science.gov (United States)

    Tabuchi, Sawako; Tsunematsu, Tomomi; Black, Sarah W; Tominaga, Makoto; Maruyama, Megumi; Takagi, Kazuyo; Minokoshi, Yasuhiko; Sakurai, Takeshi; Kilduff, Thomas S; Yamanaka, Akihiro

    2014-05-07

    The sleep disorder narcolepsy results from loss of hypothalamic orexin/hypocretin neurons. Although narcolepsy onset is usually postpubertal, current mouse models involve loss of either orexin peptides or orexin neurons from birth. To create a model of orexin/hypocretin deficiency with closer fidelity to human narcolepsy, diphtheria toxin A (DTA) was expressed in orexin neurons under control of the Tet-off system. Upon doxycycline removal from the diet of postpubertal orexin-tTA;TetO DTA mice, orexin neurodegeneration was rapid, with 80% cell loss within 7 d, and resulted in disrupted sleep architecture. Cataplexy, the pathognomic symptom of narcolepsy, occurred by 14 d when ∼5% of the orexin neurons remained. Cataplexy frequency increased for at least 11 weeks after doxycycline. Temporary doxycycline removal followed by reintroduction after several days enabled partial lesion of orexin neurons. DTA-induced orexin neurodegeneration caused a body weight increase without a change in food consumption, mimicking metabolic aspects of human narcolepsy. Because the orexin/hypocretin system has been implicated in the control of metabolism and addiction as well as sleep/wake regulation, orexin-tTA; TetO DTA mice are a novel model in which to study these functions, for pharmacological studies of cataplexy, and to study network reorganization as orexin input is lost.

  16. 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.

  17. The advantage of flexible neuronal tunings in neural network models for motor learning

    Directory of Open Access Journals (Sweden)

    Ellisha N Marongelli

    2013-07-01

    Full Text Available Human motor adaptation to novel environments is often modeled by a basis function network that transforms desired movement properties into estimated forces. This network employs a layer of nodes that have fixed broad tunings that generalize across the input domain. Learning is achieved by updating the weights of these nodes in response to training experience. This conventional model is unable to account for rapid flexibility observed in human spatial generalization during motor adaptation. However, added plasticity in the breadths of the basis function tunings can achieve this flexibility, and several neurophysiological experiments have revealed flexibility in tunings of sensorimotor neurons. We found a model, Locally Weighted Projection Regression (LWPR, which uniquely possesses the structure of a basis function network in which both the weights and tuning widths of the nodes are updated incrementally during adaptation. We presented this LWPR model with training functions of different spatial complexities and monitored incremental updates to receptive field sizes. An inverse pattern of dependence of receptive field adaptation on experienced error became evident, underlying both a relationship between generalization and complexity, and a unique behavior in which generalization always narrows after a sudden switch in environmental complexity. These results implicate a model with a flexible structure, like LWPR, as a viable alternative model for human motor adaptation that can account for previously observed plasticity in spatial generalization. This theory can be tested by using the behaviors observed in our experiments as novel hypotheses in human studies.

  18. The advantage of flexible neuronal tunings in neural network models for motor learning

    Science.gov (United States)

    Marongelli, Ellisha N.; Thoroughman, Kurt A.

    2013-01-01

    Human motor adaptation to novel environments is often modeled by a basis function network that transforms desired movement properties into estimated forces. This network employs a layer of nodes that have fixed broad tunings that generalize across the input domain. Learning is achieved by updating the weights of these nodes in response to training experience. This conventional model is unable to account for rapid flexibility observed in human spatial generalization during motor adaptation. However, added plasticity in the widths of the basis function tunings can achieve this flexibility, and several neurophysiological experiments have revealed flexibility in tunings of sensorimotor neurons. We found a model, Locally Weighted Projection Regression (LWPR), which uniquely possesses the structure of a basis function network in which both the weights and tuning widths of the nodes are updated incrementally during adaptation. We presented this LWPR model with training functions of different spatial complexities and monitored incremental updates to receptive field widths. An inverse pattern of dependence of receptive field adaptation on experienced error became evident, underlying both a relationship between generalization and complexity, and a unique behavior in which generalization always narrows after a sudden switch in environmental complexity. These results implicate a model that is flexible in both basis function widths and weights, like LWPR, as a viable alternative model for human motor adaptation that can account for previously observed plasticity in spatial generalization. This theory can be tested by using the behaviors observed in our experiments as novel hypotheses in human studies. PMID:23888141

  19. 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.

  20. An approximation to the adaptive exponential integrate-and-fire neuron model allows fast and predictive fitting to physiological data

    Directory of Open Access Journals (Sweden)

    Loreen eHertäg

    2012-09-01

    Full Text Available For large-scale network simulations, it is often desirable to have computationally tractable, yet in a defined sense still physiologically valid neuron models. In particular, these models should be able to reproduce physiological measurements, ideally in a predictive sense, and under different input regimes in which neurons may operate in vivo. Here we present an approach to parameter estimation for a simple spiking neuron model mainly based on standard f-I curves obtained from in vitro recordings. Such recordings are routinely obtained in standard protocols and assess a neuron's response under a wide range of mean input currents. Our fitting procedure makes use of closed-form expressions for the firing rate derived from an approximation to the adaptive exponential integrate-and-fire (AdEx model. The resulting fitting process is simple and about two orders of magnitude faster compared to methods based on numerical integration of the differential equations. We probe this method on different cell types recorded from rodent prefrontal cortex. After fitting to the f-I current-clamp data, the model cells are tested on completely different sets of recordings obtained by fluctuating ('in-vivo-like' input currents. For a wide range of different input regimes, cell types, and cortical layers, the model could predict spike times on these test traces quite accurately within the bounds of physiological reliability, although no information from these distinct test sets was used for model fitting. Further analyses delineated some of the empirical factors constraining model fitting and the model's generalization performance. An even simpler adaptive LIF neuron was also examined in this context. Hence, we have developed a 'high-throughput' model fitting procedure which is simple and fast, with good prediction performance, and which relies only on firing rate information and standard physiological data widely and easily available.

  1. GeNN: a code generation framework for accelerated brain simulations

    Science.gov (United States)

    Yavuz, Esin; Turner, James; Nowotny, Thomas

    2016-01-01

    Large-scale numerical simulations of detailed brain circuit models are important for identifying hypotheses on brain functions and testing their consistency and plausibility. An ongoing challenge for simulating realistic models is, however, computational speed. In this paper, we present the GeNN (GPU-enhanced Neuronal Networks) framework, which aims to facilitate the use of graphics accelerators for computational models of large-scale neuronal networks to address this challenge. GeNN is an open source library that generates code to accelerate the execution of network simulations on NVIDIA GPUs, through a flexible and extensible interface, which does not require in-depth technical knowledge from the users. We present performance benchmarks showing that 200-fold speedup compared to a single core of a CPU can be achieved for a network of one million conductance based Hodgkin-Huxley neurons but that for other models the speedup can differ. GeNN is available for Linux, Mac OS X and Windows platforms. The source code, user manual, tutorials, Wiki, in-depth example projects and all other related information can be found on the project website http://genn-team.github.io/genn/.

  2. Motor neurons and glia exhibit specific individualized responses to TDP-43 expression in a Drosophila model of amyotrophic lateral sclerosis.

    Science.gov (United States)

    Estes, Patricia S; Daniel, Scott G; McCallum, Abigail P; Boehringer, Ashley V; Sukhina, Alona S; Zwick, Rebecca A; Zarnescu, Daniela C

    2013-05-01

    Amyotrophic lateral sclerosis (ALS) is a fatal disease characterized by complex neuronal and glial phenotypes. Recently, RNA-based mechanisms have been linked to ALS via RNA-binding proteins such as TDP-43, which has been studied in vivo using models ranging from yeast to rodents. We have developed a Drosophila model of ALS based on TDP-43 that recapitulates several aspects of pathology, including motor neuron loss, locomotor dysfunction and reduced survival. Here we report the phenotypic consequences of expressing wild-type and four different ALS-linked TDP-43 mutations in neurons and glia. We show that TDP-43-driven neurodegeneration phenotypes are dose- and age-dependent. In motor neurons, TDP-43 appears restricted to nuclei, which are significantly misshapen due to mutant but not wild-type protein expression. In glia and in the developing neuroepithelium, TDP-43 associates with cytoplasmic puncta. TDP-43-containing RNA granules are motile in cultured motor neurons, although wild-type and mutant variants exhibit different kinetic properties. At the neuromuscular junction, the expression of TDP-43 in motor neurons versus glia leads to seemingly opposite synaptic phenotypes that, surprisingly, translate into comparable locomotor defects. Finally, we explore sleep as a behavioral readout of TDP-43 expression and find evidence of sleep fragmentation consistent with hyperexcitability, a suggested mechanism in ALS. These findings support the notion that although motor neurons and glia are both involved in ALS pathology, at the cellular level they can exhibit different responses to TDP-43. In addition, our data suggest that individual TDP-43 alleles utilize distinct molecular mechanisms, which will be important for developing therapeutic strategies.

  3. Motor neurons and glia exhibit specific individualized responses to TDP-43 expression in a Drosophila model of amyotrophic lateral sclerosis

    Directory of Open Access Journals (Sweden)

    Patricia S. Estes

    2013-05-01

    Amyotrophic lateral sclerosis (ALS is a fatal disease characterized by complex neuronal and glial phenotypes. Recently, RNA-based mechanisms have been linked to ALS via RNA-binding proteins such as TDP-43, which has been studied in vivo using models ranging from yeast to rodents. We have developed a Drosophila model of ALS based on TDP-43 that recapitulates several aspects of pathology, including motor neuron loss, locomotor dysfunction and reduced survival. Here we report the phenotypic consequences of expressing wild-type and four different ALS-linked TDP-43 mutations in neurons and glia. We show that TDP-43-driven neurodegeneration phenotypes are dose- and age-dependent. In motor neurons, TDP-43 appears restricted to nuclei, which are significantly misshapen due to mutant but not wild-type protein expression. In glia and in the developing neuroepithelium, TDP-43 associates with cytoplasmic puncta. TDP-43-containing RNA granules are motile in cultured motor neurons, although wild-type and mutant variants exhibit different kinetic properties. At the neuromuscular junction, the expression of TDP-43 in motor neurons versus glia leads to seemingly opposite synaptic phenotypes that, surprisingly, translate into comparable locomotor defects. Finally, we explore sleep as a behavioral readout of TDP-43 expression and find evidence of sleep fragmentation consistent with hyperexcitability, a suggested mechanism in ALS. These findings support the notion that although motor neurons and glia are both involved in ALS pathology, at the cellular level they can exhibit different responses to TDP-43. In addition, our data suggest that individual TDP-43 alleles utilize distinct molecular mechanisms, which will be important for developing therapeutic strategies.

  4. Neuronal models for evaluation of proliferation in vitro using high content screening

    International Nuclear Information System (INIS)

    Mundy, William R.; Radio, Nicholas M.; Freudenrich, Theresa M.

    2010-01-01

    In vitro test methods can provide a rapid approach for the screening of large numbers of chemicals for their potential to produce toxicity (hazard identification). In order to identify potential developmental neurotoxicants, a battery of in vitro tests for neurodevelopmental processes such as cell proliferation, differentiation, growth, and synaptogenesis has been proposed. The development of in vitro approaches for toxicity testing will require choosing a model system that is appropriate to the endpoint of concern. This study compared several cell lines as models for neuronal proliferation. The sensitivities of neuronal cell lines derived from three species (PC12, rat; N1E-115, mouse; SH-SY5Y, human) to chemicals known to affect cell proliferation were assessed using a high content screening system. After optimizing conditions for cell growth in 96-well plates, proliferation was measured as the incorporation of 5-bromo-2'-deoxyuridine (BrdU) into replicating DNA during S phase. BrdU-labeled cells were detected by immunocytochemistry and cell counts were obtained using automated image acquisition and analysis. The three cell lines showed approximately 30-40% of the population in S phase after a 4 h pulse of BrdU. Exposure to the DNA polymerase inhibitor aphidicolin for 20 h prior to the 4 h pulse of BrdU significantly decreased proliferation in all three cell lines. The sensitivities of the cell lines were compared by exposure to eight chemicals known to affect proliferation (positive controls) and determination of the concentration inhibiting proliferation by 50% of control (I 50 ). PC12 cells were the most sensitive to chemicals; 6 out of 8 chemicals (aphidicolin, cadmium, cytosine arabinoside, dexamethasone, 5-fluorouracil, and methylmercury) inhibited proliferation at the concentrations tested. SH-SY5Y cells were somewhat less sensitive to chemical effects, with five out of eight chemicals inhibiting proliferation; dexamethasone had no effect, and cadmium

  5. Fractional Gaussian noise-enhanced information capacity of a nonlinear neuron model with binary signal input

    Science.gov (United States)

    Gao, Feng-Yin; Kang, Yan-Mei; Chen, Xi; Chen, Guanrong

    2018-05-01

    This paper reveals the effect of fractional Gaussian noise with Hurst exponent H ∈(1 /2 ,1 ) on the information capacity of a general nonlinear neuron model with binary signal input. The fGn and its corresponding fractional Brownian motion exhibit long-range, strong-dependent increments. It extends standard Brownian motion to many types of fractional processes found in nature, such as the synaptic noise. In the paper, for the subthreshold binary signal, sufficient conditions are given based on the "forbidden interval" theorem to guarantee the occurrence of stochastic resonance, while for the suprathreshold binary signal, the simulated results show that additive fGn with Hurst exponent H ∈(1 /2 ,1 ) could increase the mutual information or bits count. The investigation indicated that the synaptic noise with the characters of long-range dependence and self-similarity might be the driving factor for the efficient encoding and decoding of the nervous system.

  6. Mode locking and quasiperiodicity in a discrete-time Chialvo neuron model

    Science.gov (United States)

    Wang, Fengjuan; Cao, Hongjun

    2018-03-01

    The two-dimensional parameter spaces of a discrete-time Chialvo neuron model are investigated. Our studies demonstrate that for all our choice of two parameters (i) the fixed point is destabilized via Neimark-Sacker bifurcation; (ii) there exist mode locking structures like Arnold tongues and shrimps, with periods organized in a Farey tree sequence, embedded in quasiperiodic/chaotic region. We determine analytically the location of the parameter sets where Neimark-Sacker bifurcation occurs, and the location on this curve where Arnold tongues of arbitrary period are born. Properties of the transition that follows the so-called two-torus from quasiperiodicity to chaos are presented clearly and proved strictly by using numerical simulations such as bifurcation diagrams, the largest Lyapunov exponent diagram on MATLAB and C++.

  7. Macro- and micro-chaotic structures in the Hindmarsh-Rose model of bursting neurons

    Energy Technology Data Exchange (ETDEWEB)

    Barrio, Roberto, E-mail: rbarrio@unizar.es; Serrano, Sergio [Computational Dynamics Group, Departamento de Matemática Aplicada, GME and IUMA, Universidad de Zaragoza, E-50009 Zaragoza (Spain); Angeles Martínez, M. [Computational Dynamics Group, GME, Universidad de Zaragoza, E-50009 Zaragoza (Spain); Shilnikov, Andrey [Neuroscience Institute and Department of Mathematics and Statistics, Georgia State University, Atlanta, Georgia 30078 (United States); Department of Computational Mathematics and Cybernetics, Lobachevsky State University of Nizhni Novgorod, 603950 Nizhni Novgorod (Russian Federation)

    2014-06-01

    We study a plethora of chaotic phenomena in the Hindmarsh-Rose neuron model with the use of several computational techniques including the bifurcation parameter continuation, spike-quantification, and evaluation of Lyapunov exponents in bi-parameter diagrams. Such an aggregated approach allows for detecting regions of simple and chaotic dynamics, and demarcating borderlines—exact bifurcation curves. We demonstrate how the organizing centers—points corresponding to codimension-two homoclinic bifurcations—along with fold and period-doubling bifurcation curves structure the biparametric plane, thus forming macro-chaotic regions of onion bulb shapes and revealing spike-adding cascades that generate micro-chaotic structures due to the hysteresis.

  8. Neurocognitive Poetics: methods and models for investigating the neuronal and cognitive- affective bases of literature reception

    Directory of Open Access Journals (Sweden)

    Arthur M Jacobs

    2015-04-01

    Full Text Available A long tradition of research including classical rhetoric, aesthetics and poetics theory, formalism and structuralism, as well as current perspectives in (neurocognitive poetics has investigated structural and functional aspects of literature reception. Despite a wealth of literature published in specialised journals like Poetics, however, still little is known about how the brain processes and creates literary and poetic texts. Still, such stimulus material might be suited better than other genres for demonstrating the complexities with which our brain constructs the world in and around us, because it unifies thought and language, music and imagery in a clear, manageable way, most often with play, pleasure, and emotion (Schrott & Jacobs, 2011. In this paper, I discuss methods and models for investigating the neuronal and cognitive-affective bases of literary reading together with pertinent results from studies on poetics, text processing, emotion, or neuroaesthetics, and outline current challenges and future perspectives.

  9. Macro- and micro-chaotic structures in the Hindmarsh-Rose model of bursting neurons

    International Nuclear Information System (INIS)

    Barrio, Roberto; Serrano, Sergio; Angeles Martínez, M.; Shilnikov, Andrey

    2014-01-01

    We study a plethora of chaotic phenomena in the Hindmarsh-Rose neuron model with the use of several computational techniques including the bifurcation parameter continuation, spike-quantification, and evaluation of Lyapunov exponents in bi-parameter diagrams. Such an aggregated approach allows for detecting regions of simple and chaotic dynamics, and demarcating borderlines—exact bifurcation curves. We demonstrate how the organizing centers—points corresponding to codimension-two homoclinic bifurcations—along with fold and period-doubling bifurcation curves structure the biparametric plane, thus forming macro-chaotic regions of onion bulb shapes and revealing spike-adding cascades that generate micro-chaotic structures due to the hysteresis

  10. Cognitive phase transitions in the cerebral cortex enhancing the neuron doctrine by modeling neural fields

    CERN Document Server

    Kozma, Robert

    2016-01-01

    This intriguing book was born out of the many discussions the authors had in the past 10 years about the role of scale-free structure and dynamics in producing intelligent behavior in brains. The microscopic dynamics of neural networks is well described by the prevailing paradigm based in a narrow interpretation of the neuron doctrine. This book broadens the doctrine by incorporating the dynamics of neural fields, as first revealed by modeling with differential equations (K-sets).  The book broadens that approach by application of random graph theory (neuropercolation). The book concludes with diverse commentaries that exemplify the wide range of mathematical/conceptual approaches to neural fields. This book is intended for researchers, postdocs, and graduate students, who see the limitations of network theory and seek a beachhead from which to embark on mesoscopic and macroscopic neurodynamics.

  11. 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.

  12. Numerical investigation of action potential transmission in plants

    Directory of Open Access Journals (Sweden)

    Mariusz Pietruszka

    2014-01-01

    Full Text Available In context of a fairly concise review of recent literature and well established experimental results we reconsider the problem of action potential propagating steadily down the plant cell(s. Having adopted slightly modified Hodgkin-Huxley set of differential equations for the action potential we carried out the numerical investigation of these equations in the course of time. We argue that the Hodgkin-Huxley-Katz model for the nerve impulse can be used to describe the phenomena which take place in plants - this point of view seems to be plausible since the mechanisms involving active ionic transport across membranes from the mathematical point of view are similar. Besides, we compare in a qualitative way our theoretical outcomes with typical experimental results for the action potentials which arise as the reaction of plants to electrical, mechanical and light stimuli. Moreover, we point out the relevance of the sequence of events during the pulse with the appropriate ionic fluxes.

  13. Modeling the electrode-neuron interface of cochlear implants: effects of neural survival, electrode placement, and the partial tripolar configuration.

    Science.gov (United States)

    Goldwyn, Joshua H; Bierer, Steven M; Bierer, Julie Arenberg

    2010-09-01

    The partial tripolar electrode configuration is a relatively novel stimulation strategy that can generate more spatially focused electric fields than the commonly used monopolar configuration. Focused stimulation strategies should improve spectral resolution in cochlear implant users, but may also be more sensitive to local irregularities in the electrode-neuron interface. In this study, we develop a practical computer model of cochlear implant stimulation that can simulate neural activation in a simplified cochlear geometry and we relate the resulting patterns of neural activity to basic psychophysical measures. We examine how two types of local irregularities in the electrode-neuron interface, variations in spiral ganglion nerve density and electrode position within the scala tympani, affect the simulated neural activation patterns and how these patterns change with electrode configuration. The model shows that higher partial tripolar fractions activate more spatially restricted populations of neurons at all current levels and require higher current levels to excite a given number of neurons. We find that threshold levels are more sensitive at high partial tripolar fractions to both types of irregularities, but these effects are not independent. In particular, at close electrode-neuron distances, activation is typically more spatially localized which leads to a greater influence of neural dead regions. Copyright (c) 2010 Elsevier B.V. All rights reserved.

  14. 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.

  15. Input dependent cell assembly dynamics in a model of the striatal medium spiny neuron network

    Directory of Open Access Journals (Sweden)

    Adam ePonzi

    2012-03-01

    Full Text Available The striatal medium spiny neuron (MSNs network is sparsely connected with fairly weak GABAergic collaterals receiving an excitatory glutamatergic cortical projection. Peri stimulus time histograms (PSTH of MSN population response investigated in various experimental studies display strong firing rate modulations distributed throughout behavioural task epochs. In previous work we have shown by numerical simulation that sparse random networks of inhibitory spiking neurons with characteristics appropriate for UP state MSNs form cell assemblies which fire together coherently in sequences on long behaviourally relevant timescales when the network receives a fixed pattern of constant input excitation. Here we first extend that model to the case where cortical excitation is composed of many independent noisy Poisson processes and demonstrate that cell assembly dynamics is still observed when the input is sufficiently weak. However if cortical excitation strength is increased more regularly firing and completely quiescent cells are found, which depend on the cortical stimulation. Subsequently we further extend previous work to consider what happens when the excitatory input varies as it would in when the animal is engaged in behavior. We investigate how sudden switches in excitation interact with network generated patterned activity. We show that sequences of cell assembly activations can be locked to the excitatory input sequence and delineate the range of parameters where this behaviour is shown. Model cell population PSTH display both stimulus and temporal specificity, with large population firing rate modulations locked to elapsed time from task events. Thus the random network can generate a large diversity of temporally evolving stimulus dependent responses even though the input is fixed between switches. We suggest the MSN network is well suited to the generation of such slow coherent task dependent response

  16. Input dependent cell assembly dynamics in a model of the striatal medium spiny neuron network.

    Science.gov (United States)

    Ponzi, Adam; Wickens, Jeff

    2012-01-01

    The striatal medium spiny neuron (MSN) network is sparsely connected with fairly weak GABAergic collaterals receiving an excitatory glutamatergic cortical projection. Peri-stimulus time histograms (PSTH) of MSN population response investigated in various experimental studies display strong firing rate modulations distributed throughout behavioral task epochs. In previous work we have shown by numerical simulation that sparse random networks of inhibitory spiking neurons with characteristics appropriate for UP state MSNs form cell assemblies which fire together coherently in sequences on long behaviorally relevant timescales when the network receives a fixed pattern of constant input excitation. Here we first extend that model to the case where cortical excitation is composed of many independent noisy Poisson processes and demonstrate that cell assembly dynamics is still observed when the input is sufficiently weak. However if cortical excitation strength is increased more regularly firing and completely quiescent cells are found, which depend on the cortical stimulation. Subsequently we further extend previous work to consider what happens when the excitatory input varies as it would when the animal is engaged in behavior. We investigate how sudden switches in excitation interact with network generated patterned activity. We show that sequences of cell assembly activations can be locked to the excitatory input sequence and outline the range of parameters where this behavior is shown. Model cell population PSTH display both stimulus and temporal specificity, with large population firing rate modulations locked to elapsed time from task events. Thus the random network can generate a large diversity of temporally evolving stimulus dependent responses even though the input is fixed between switches. We suggest the MSN network is well suited to the generation of such slow coherent task dependent response which could be utilized by the animal in behavior.

  17. Human neuronal cell based assay: A new in vitro model for toxicity evaluation of ciguatoxin.

    Science.gov (United States)

    Coccini, Teresa; Caloni, Francesca; De Simone, Uliana

    2017-06-01

    Ciguatoxins (CTXs) are emerging marine neurotoxins representing the main cause of ciguatera fish poisoning, an intoxication syndrome which configures a health emergency and constitutes an evolving issue constantly changing due to new vectors and derivatives of CTXs, as well as their presence in new non-endemic areas. The study applied the neuroblastoma cell model of human origin (SH-SY5Y) to evaluate species-specific mechanistic information on CTX toxicity. Metabolic functionality, cell morphology, cytosolic Ca 2+ i responses, neuronal cell growth and proliferation were assessed after short- (4-24h) and long-term exposure (10days) to P-CTX-3C. In SH-SY5Y, P-CTX-3C displayed a powerful cytotoxicity requiring the presence of both Veratridine and Ouabain. SH-SY5Y were very sensitive to Ouabain: 10 and 0.25nM appeared the optimal concentrations, for short- and long-term toxicity studies, respectively, to be used in co-incubation with Veratridine (25μM), simulating the physiological and pathological endogenous Ouabain levels in humans. P-CTX-3C cytotoxic effect, on human neurons co-incubated with OV (Ouabain+Veratridine) mix, was expressed starting from 100pM after short- and 25pM after long-term exposure. Notably, P-CTX-3C alone at 25nM induced cytotoxicity after 24h and prolonged exposure. This human brain-derived cell line appears a suitable cell-based-model to evaluate cytotoxicity of CTX present in marine food contaminated at low toxic levels and to characterize the toxicological profile of other/new congeners. Copyright © 2017 Elsevier B.V. All rights reserved.

  18. Biophysical Neural Spiking, Bursting, and Excitability Dynamics in Reconfigurable Analog VLSI.

    Science.gov (United States)

    Yu, T; Sejnowski, T J; Cauwenberghs, G

    2011-10-01

    We study a range of neural dynamics under variations in biophysical parameters underlying extended Morris-Lecar and Hodgkin-Huxley models in three gating variables. The extended models are implemented in NeuroDyn, a four neuron, twelve synapse continuous-time analog VLSI programmable neural emulation platform with generalized channel kinetics and biophysical membrane dynamics. The dynamics exhibit a wide range of time scales extending beyond 100 ms neglected in typical silicon models of tonic spiking neurons. Circuit simulations and measurements show transition from tonic spiking to tonic bursting dynamics through variation of a single conductance parameter governing calcium recovery. We similarly demonstrate transition from graded to all-or-none neural excitability in the onset of spiking dynamics through the variation of channel kinetic parameters governing the speed of potassium activation. Other combinations of variations in conductance and channel kinetic parameters give rise to phasic spiking and spike frequency adaptation dynamics. The NeuroDyn chip consumes 1.29 mW and occupies 3 mm × 3 mm in 0.5 μm CMOS, supporting emerging developments in neuromorphic silicon-neuron interfaces.

  19. 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

  20. Genetically engineered cardiac pacemaker: Stem cells transfected with HCN2 gene and myocytes-A model

    Energy Technology Data Exchange (ETDEWEB)

    Kanani, S. [Institut Genomique Fonctionelle, 141 Rue de la Cardonille, 34396 Montpellier (France); Institut Non Lineaire de Nice, CNRS and Universite de Nice, 1361 route des Lucioles, 06560 Valbonne (France); Pumir, A. [Institut Non Lineaire de Nice, CNRS and Universite de Nice, 1361 route des Lucioles, 06560 Valbonne (France); Laboratoire J.A. Dieudonne, CNRS and Universite de Nice, Parc Valrose, 06108 Nice (France)], E-mail: alain.pumir@unice.fr; Krinsky, V. [Institut Non Lineaire de Nice, CNRS and Universite de Nice, 1361 route des Lucioles, 06560 Valbonne (France)

    2008-01-07

    One of the successfully tested methods to design genetically engineered cardiac pacemaker cells consists in transfecting a human mesenchymal stem cell (hMSC) with a HCN2 gene and connecting it to a myocyte. We develop and study a mathematical model, describing a myocyte connected to a hMSC transfected with a HCN2 gene. The cardiac action potential is described both with the simple Beeler-Reuter model, as well as with the elaborate dynamic Luo-Rudy model. The HCN2 channel is described by fitting electrophysiological records, in the spirit of Hodgkin-Huxley. The model shows that oscillations can occur in a pair myocyte-stem cell, that was not observed in the experiments yet. The model predicted that: (1) HCN pacemaker channels can induce oscillations only if the number of expressed I{sub K1} channels is low enough. At too high an expression level of I{sub K1} channels, oscillations cannot be induced, no matter how many pacemaker channels are expressed. (2) At low expression levels of I{sub K1} channels, a large domain of values in the parameter space (n, N) exists, where oscillations should be observed. We denote N the number of expressed pacemaker channels in the stem cell, and n the number of gap junction channels coupling the stem cell and the myocyte. (3) The expression levels of I{sub K1} channels observed in ventricular myocytes, both in the Beeler-Reuter and in the dynamic Luo-Rudy models are too high to allow to observe oscillations. With expression levels below {approx}1/4 of the original value, oscillations can be observed. The main consequence of this work is that in order to obtain oscillations in an experiment with a myocyte-stem cell pair, increasing the values of n, N is unlikely to be helpful, unless the expression level of I{sub K1} has been reduced enough. The model also allows us to explore levels of gene expression not yet achieved in experiments, and could be useful to plan new experiments, aimed at improving the robustness of the oscillations.

  1. Genetically engineered cardiac pacemaker: Stem cells transfected with HCN2 gene and myocytes—A model

    Science.gov (United States)

    Kanani, S.; Pumir, A.; Krinsky, V.

    2008-01-01

    One of the successfully tested methods to design genetically engineered cardiac pacemaker cells consists in transfecting a human mesenchymal stem cell (hMSC) with a HCN2 gene and connecting it to a myocyte. We develop and study a mathematical model, describing a myocyte connected to a hMSC transfected with a HCN2 gene. The cardiac action potential is described both with the simple Beeler Reuter model, as well as with the elaborate dynamic Luo Rudy model. The HCN2 channel is described by fitting electrophysiological records, in the spirit of Hodgkin Huxley. The model shows that oscillations can occur in a pair myocyte-stem cell, that was not observed in the experiments yet. The model predicted that: (1) HCN pacemaker channels can induce oscillations only if the number of expressed I channels is low enough. At too high an expression level of I channels, oscillations cannot be induced, no matter how many pacemaker channels are expressed. (2) At low expression levels of I channels, a large domain of values in the parameter space (n, N) exists, where oscillations should be observed. We denote N the number of expressed pacemaker channels in the stem cell, and n the number of gap juncti