Integrated neuron circuit for implementing neuromorphic system with synaptic device

Science.gov (United States)

Lee, Jeong-Jun; Park, Jungjin; Kwon, Min-Woo; Hwang, Sungmin; Kim, Hyungjin; Park, Byung-Gook

2018-02-01

In this paper, we propose and fabricate Integrate & Fire neuron circuit for implementing neuromorphic system. Overall operation of the circuit is verified by measuring discrete devices and the output characteristics of the circuit. Since the neuron circuit shows asymmetric output characteristic that can drive synaptic device with Spike-Timing-Dependent-Plasticity (STDP) characteristic, the autonomous weight update process is also verified by connecting the synaptic device and the neuron circuit. The timing difference of the pre-neuron and the post-neuron induce autonomous weight change of the synaptic device. Unlike 2-terminal devices, which is frequently used to implement neuromorphic system, proposed scheme of the system enables autonomous weight update and simple configuration by using 4-terminal synapse device and appropriate neuron circuit. Weight update process in the multi-layer neuron-synapse connection ensures implementation of the hardware-based artificial intelligence, based on Spiking-Neural- Network (SNN).

Does spike-timing-dependent synaptic plasticity couple or decouple neurons firing in synchrony?

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Andreas eKnoblauch

2012-08-01

Full Text Available Spike synchronization is thought to have a constructive role for feature integration, attention, associativelearning, and the formation of bidirectionally connected Hebbian cell assemblies. By contrast, theoreticalstudies on spike-timing-dependent plasticity (STDP report an inherently decoupling influence of spikesynchronization on synaptic connections of coactivated neurons. For example, bidirectional synapticconnections as found in cortical areas could be reproduced only by assuming realistic models of STDP andrate coding. We resolve this conflict by theoretical analysis and simulation of various simple and realisticSTDP models that provide a more complete characterization of conditions when STDP leads to eithercoupling or decoupling of neurons firing in synchrony. In particular, we show that STDP consistentlycouples synchronized neurons if key model parameters are matched to physiological data: First, synapticpotentiation must be significantly stronger than synaptic depression for small (positive or negative timelags between presynaptic and postsynaptic spikes. Second, spike synchronization must be sufficientlyimprecise, for example, within a time window of 5-10msec instead of 1msec. Third, axonal propagationdelays should not be much larger than dendritic delays. Under these assumptions synchronized neuronswill be strongly coupled leading to a dominance of bidirectional synaptic connections even for simpleSTDP models and low mean firing rates at the level of spontaneous activity.

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.

The Gamma renewal process as an output of the diffusion leaky integrate-and-fire neuronal model

Czech Academy of Sciences Publication Activity Database

Lánský, Petr; Sacerdote, L.; Zucca, C.

2016-01-01

Roč. 110, 2-3 (2016), s. 193-200 ISSN 0340-1200 R&D Projects: GA ČR(CZ) GA15-08066S Institutional support: RVO:67985823 Keywords : first-passage-time problem * leaky integrate-and-fire * Stein's neuronal model Subject RIV: BD - Theory of Information Impact factor: 1.716, year: 2016

Contribution of synchronized GABAergic neurons to dopaminergic neuron firing and bursting.

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Morozova, Ekaterina O; Myroshnychenko, Maxym; Zakharov, Denis; di Volo, Matteo; Gutkin, Boris; Lapish, Christopher C; Kuznetsov, Alexey

2016-10-01

In the ventral tegmental area (VTA), interactions between dopamine (DA) and γ-aminobutyric acid (GABA) neurons are critical for regulating DA neuron activity and thus DA efflux. To provide a mechanistic explanation of how GABA neurons influence DA neuron firing, we developed a circuit model of the VTA. The model is based on feed-forward inhibition and recreates canonical features of the VTA neurons. Simulations revealed that γ-aminobutyric acid (GABA) receptor (GABAR) stimulation can differentially influence the firing pattern of the DA neuron, depending on the level of synchronization among GABA neurons. Asynchronous activity of GABA neurons provides a constant level of inhibition to the DA neuron and, when removed, produces a classical disinhibition burst. In contrast, when GABA neurons are synchronized by common synaptic input, their influence evokes additional spikes in the DA neuron, resulting in increased measures of firing and bursting. Distinct from previous mechanisms, the increases were not based on lowered firing rate of the GABA neurons or weaker hyperpolarization by the GABAR synaptic current. This phenomenon was induced by GABA-mediated hyperpolarization of the DA neuron that leads to decreases in intracellular calcium (Ca 2+ ) concentration, thus reducing the Ca 2+ -dependent potassium (K + ) current. In this way, the GABA-mediated hyperpolarization replaces Ca 2+ -dependent K + current; however, this inhibition is pulsatile, which allows the DA neuron to fire during the rhythmic pauses in inhibition. Our results emphasize the importance of inhibition in the VTA, which has been discussed in many studies, and suggest a novel mechanism whereby computations can occur locally. Copyright © 2016 the American Physiological Society.

How well do mean field theories of spiking quadratic-integrate-and-fire networks work in realistic parameter regimes?

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Grabska-Barwińska, Agnieszka; Latham, Peter E

2014-06-01

We use mean field techniques to compute the distribution of excitatory and inhibitory firing rates in large networks of randomly connected spiking quadratic integrate and fire neurons. These techniques are based on the assumption that activity is asynchronous and Poisson. For most parameter settings these assumptions are strongly violated; nevertheless, so long as the networks are not too synchronous, we find good agreement between mean field prediction and network simulations. Thus, much of the intuition developed for randomly connected networks in the asynchronous regime applies to mildly synchronous networks.

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

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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.

Recurrently connected and localized neuronal communities initiate coordinated spontaneous activity in neuronal networks

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Amin, Hayder; Maccione, Alessandro; Nieus, Thierry

2017-01-01

Developing neuronal systems intrinsically generate coordinated spontaneous activity that propagates by involving a large number of synchronously firing neurons. In vivo, waves of spikes transiently characterize the activity of developing brain circuits and are fundamental for activity-dependent circuit formation. In vitro, coordinated spontaneous spiking activity, or network bursts (NBs), interleaved within periods of asynchronous spikes emerge during the development of 2D and 3D neuronal cultures. Several studies have investigated this type of activity and its dynamics, but how a neuronal system generates these coordinated events remains unclear. Here, we investigate at a cellular level the generation of network bursts in spontaneously active neuronal cultures by exploiting high-resolution multielectrode array recordings and computational network modelling. Our analysis reveals that NBs are generated in specialized regions of the network (functional neuronal communities) that feature neuronal links with high cross-correlation peak values, sub-millisecond lags and that share very similar structural connectivity motifs providing recurrent interactions. We show that the particular properties of these local structures enable locally amplifying spontaneous asynchronous spikes and that this mechanism can lead to the initiation of NBs. Through the analysis of simulated and experimental data, we also show that AMPA currents drive the coordinated activity, while NMDA and GABA currents are only involved in shaping the dynamics of NBs. Overall, our results suggest that the presence of functional neuronal communities with recurrent local connections allows a neuronal system to generate spontaneous coordinated spiking activity events. As suggested by the rules used for implementing our computational model, such functional communities might naturally emerge during network development by following simple constraints on distance-based connectivity. PMID:28749937

Recurrently connected and localized neuronal communities initiate coordinated spontaneous activity in neuronal networks.

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Davide Lonardoni

2017-07-01

Full Text Available Developing neuronal systems intrinsically generate coordinated spontaneous activity that propagates by involving a large number of synchronously firing neurons. In vivo, waves of spikes transiently characterize the activity of developing brain circuits and are fundamental for activity-dependent circuit formation. In vitro, coordinated spontaneous spiking activity, or network bursts (NBs, interleaved within periods of asynchronous spikes emerge during the development of 2D and 3D neuronal cultures. Several studies have investigated this type of activity and its dynamics, but how a neuronal system generates these coordinated events remains unclear. Here, we investigate at a cellular level the generation of network bursts in spontaneously active neuronal cultures by exploiting high-resolution multielectrode array recordings and computational network modelling. Our analysis reveals that NBs are generated in specialized regions of the network (functional neuronal communities that feature neuronal links with high cross-correlation peak values, sub-millisecond lags and that share very similar structural connectivity motifs providing recurrent interactions. We show that the particular properties of these local structures enable locally amplifying spontaneous asynchronous spikes and that this mechanism can lead to the initiation of NBs. Through the analysis of simulated and experimental data, we also show that AMPA currents drive the coordinated activity, while NMDA and GABA currents are only involved in shaping the dynamics of NBs. Overall, our results suggest that the presence of functional neuronal communities with recurrent local connections allows a neuronal system to generate spontaneous coordinated spiking activity events. As suggested by the rules used for implementing our computational model, such functional communities might naturally emerge during network development by following simple constraints on distance-based connectivity.

Simulating synchronization in neuronal networks

Science.gov (United States)

Fink, Christian G.

2016-06-01

We discuss several techniques used in simulating neuronal networks by exploring how a network's connectivity structure affects its propensity for synchronous spiking. Network connectivity is generated using the Watts-Strogatz small-world algorithm, and two key measures of network structure are described. These measures quantify structural characteristics that influence collective neuronal spiking, which is simulated using the leaky integrate-and-fire model. Simulations show that adding a small number of random connections to an otherwise lattice-like connectivity structure leads to a dramatic increase in neuronal synchronization.

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)

Delay-induced diversity of firing behavior and ordered chaotic firing in adaptive neuronal networks

International Nuclear Information System (INIS)

Gong Yubing; Wang Li; Xu Bo

2012-01-01

In this paper, we study the effect of time delay on the firing behavior and temporal coherence and synchronization in Newman–Watts thermosensitive neuron networks with adaptive coupling. At beginning, the firing exhibit disordered spiking in absence of time delay. As time delay is increased, the neurons exhibit diversity of firing behaviors including bursting with multiple spikes in a burst, spiking, bursting with four, three and two spikes, firing death, and bursting with increasing amplitude. The spiking is the most ordered, exhibiting coherence resonance (CR)-like behavior, and the firing synchronization becomes enhanced with the increase of time delay. As growth rate of coupling strength or network randomness increases, CR-like behavior shifts to smaller time delay and the synchronization of firing increases. These results show that time delay can induce diversity of firing behaviors in adaptive neuronal networks, and can order the chaotic firing by enhancing and optimizing the temporal coherence and enhancing the synchronization of firing. However, the phenomenon of firing death shows that time delay may inhibit the firing of adaptive neuronal networks. These findings provide new insight into the role of time delay in the firing activity of adaptive neuronal networks, and can help to better understand the complex firing phenomena in neural networks.

Random Sampling with Interspike-Intervals of the Exponential Integrate and Fire Neuron: A Computational Interpretation of UP-States.

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Andreas Steimer

Full Text Available Oscillations between high and low values of the membrane potential (UP and DOWN states respectively are an ubiquitous feature of cortical neurons during slow wave sleep and anesthesia. Nevertheless, a surprisingly small number of quantitative studies have been conducted only that deal with this phenomenon's implications for computation. Here we present a novel theory that explains on a detailed mathematical level the computational benefits of UP states. The theory is based on random sampling by means of interspike intervals (ISIs of the exponential integrate and fire (EIF model neuron, such that each spike is considered a sample, whose analog value corresponds to the spike's preceding ISI. As we show, the EIF's exponential sodium current, that kicks in when balancing a noisy membrane potential around values close to the firing threshold, leads to a particularly simple, approximative relationship between the neuron's ISI distribution and input current. Approximation quality depends on the frequency spectrum of the current and is improved upon increasing the voltage baseline towards threshold. Thus, the conceptually simpler leaky integrate and fire neuron that is missing such an additional current boost performs consistently worse than the EIF and does not improve when voltage baseline is increased. For the EIF in contrast, the presented mechanism is particularly effective in the high-conductance regime, which is a hallmark feature of UP-states. Our theoretical results are confirmed by accompanying simulations, which were conducted for input currents of varying spectral composition. Moreover, we provide analytical estimations of the range of ISI distributions the EIF neuron can sample from at a given approximation level. Such samples may be considered by any algorithmic procedure that is based on random sampling, such as Markov Chain Monte Carlo or message-passing methods. Finally, we explain how spike-based random sampling relates to existing

Random Sampling with Interspike-Intervals of the Exponential Integrate and Fire Neuron: A Computational Interpretation of UP-States.

Science.gov (United States)

Steimer, Andreas; Schindler, Kaspar

2015-01-01

Oscillations between high and low values of the membrane potential (UP and DOWN states respectively) are an ubiquitous feature of cortical neurons during slow wave sleep and anesthesia. Nevertheless, a surprisingly small number of quantitative studies have been conducted only that deal with this phenomenon's implications for computation. Here we present a novel theory that explains on a detailed mathematical level the computational benefits of UP states. The theory is based on random sampling by means of interspike intervals (ISIs) of the exponential integrate and fire (EIF) model neuron, such that each spike is considered a sample, whose analog value corresponds to the spike's preceding ISI. As we show, the EIF's exponential sodium current, that kicks in when balancing a noisy membrane potential around values close to the firing threshold, leads to a particularly simple, approximative relationship between the neuron's ISI distribution and input current. Approximation quality depends on the frequency spectrum of the current and is improved upon increasing the voltage baseline towards threshold. Thus, the conceptually simpler leaky integrate and fire neuron that is missing such an additional current boost performs consistently worse than the EIF and does not improve when voltage baseline is increased. For the EIF in contrast, the presented mechanism is particularly effective in the high-conductance regime, which is a hallmark feature of UP-states. Our theoretical results are confirmed by accompanying simulations, which were conducted for input currents of varying spectral composition. Moreover, we provide analytical estimations of the range of ISI distributions the EIF neuron can sample from at a given approximation level. Such samples may be considered by any algorithmic procedure that is based on random sampling, such as Markov Chain Monte Carlo or message-passing methods. Finally, we explain how spike-based random sampling relates to existing computational

Reconstructing stimuli from the spike-times of leaky integrate and fire neurons

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Sebastian eGerwinn

2011-02-01

Full Text Available Reconstructing stimuli from the spike-trains of neurons is an important approach for understanding the neural code. One of the difficulties associated with this task is that signals which are varying continuously in time are encoded into sequences of discrete events or spikes. An important problem is to determine how much information about the continuously varying stimulus can be extracted from the time-points at which spikes were observed, especially if these time-points are subject to some sort of randomness. For the special case of spike trains generated by leaky integrate and fire neurons, noise can be introduced by allowing variations in the threshold every time a spike is released. A simple decoding algorithm previously derived for the noiseless case can be extended to the stochastic case, but turns out to be biased. Here, we review a solution to this problem, by presenting a simple yet efficient algorithm which greatly reduces the bias, and therefore leads to better decoding performance in the stochastic case.

KV7 Channels Regulate Firing during Synaptic Integration in GABAergic Striatal Neurons

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M. Belén Pérez-Ramírez

2015-01-01

Full Text Available Striatal projection neurons (SPNs process motor and cognitive information. Their activity is affected by Parkinson’s disease, in which dopamine concentration is decreased and acetylcholine concentration is increased. Acetylcholine activates muscarinic receptors in SPNs. Its main source is the cholinergic interneuron that responds with a briefer latency than SPNs during a cortical command. Therefore, an important question is whether muscarinic G-protein coupled receptors and their signaling cascades are fast enough to intervene during synaptic responses to regulate synaptic integration and firing. One of the most known voltage dependent channels regulated by muscarinic receptors is the KV7/KCNQ channel. It is not known whether these channels regulate the integration of suprathreshold corticostriatal responses. Here, we study the impact of cholinergic muscarinic modulation on the synaptic response of SPNs by regulating KV7 channels. We found that KV7 channels regulate corticostriatal synaptic integration and that this modulation occurs in the dendritic/spines compartment. In contrast, it is negligible in the somatic compartment. This modulation occurs on sub- and suprathreshold responses and lasts during the whole duration of the responses, hundreds of milliseconds, greatly altering SPNs firing properties. This modulation affected the behavior of the striatal microcircuit.

Generalization of the event-based Carnevale-Hines integration scheme for integrate-and-fire models

NARCIS (Netherlands)

van Elburg, R.A.J.; van Ooyen, A.

2009-01-01

An event-based integration scheme for an integrate-and-fire neuron model with exponentially decaying excitatory synaptic currents and double exponential inhibitory synaptic currents has been introduced by Carnevale and Hines. However, the integration scheme imposes nonphysiological constraints on

Generalization of the Event-Based Carnevale-Hines Integration Scheme for Integrate-and-Fire Models

NARCIS (Netherlands)

van Elburg, Ronald A. J.; van Ooyen, Arjen

An event-based integration scheme for an integrate-and-fire neuron model with exponentially decaying excitatory synaptic currents and double exponential inhibitory synaptic currents has been introduced by Carnevale and Hines. However, the integration scheme imposes nonphysiological constraints on

Rhythmic Firing of Pedunculopontine Tegmental Nucleus Neurons in Monkeys during Eye Movement Task.

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Ken-Ichi Okada

Full Text Available The pedunculopontine tegmental nucleus (PPTN has been thought to be involved in the control of behavioral state. Projections to the entire thalamus and reciprocal connections with the basal ganglia nuclei suggest a potential role for the PPTN in the control of various rhythmic behaviors, including waking/sleeping and locomotion. Recently, rhythmic activity in the local field potentials was recorded from the PPTN of patients with Parkinson's disease who were treated with levodopa, suggesting that rhythmic firing is a feature of the functioning PPTN and might change with the behaving conditions even within waking. However, it remains unclear whether and how single PPTN neurons exhibit rhythmic firing patterns during various behaving conditions, including executing conditioned eye movement behaviors, seeking reward, or during resting. We previously recorded from PPTN neurons in healthy monkeys during visually guided saccade tasks and reported task-related changes in firing rate, and in this paper, we reanalyzed these data and focused on their firing patterns. A population of PPTN neurons demonstrated a regular firing pattern in that the coefficient of variation of interspike intervals was lower than what would be expected of theoretical random and irregular spike trains. Furthermore, a group of PPTN neurons exhibited a clear periodic single spike firing that changed with the context of the behavioral task. Many of these neurons exhibited a periodic firing pattern during highly active conditions, either the fixation condition during the saccade task or the free-viewing condition during the intertrial interval. We speculate that these task context-related changes in rhythmic firing of PPTN neurons might regulate the monkey's attentional and vigilance state to perform the task.

Pattern formation and firing synchronization in networks of map neurons

International Nuclear Information System (INIS)

Wang Qingyun; Duan Zhisheng; Huang Lin; Chen Guanrong; Lu Qishao

2007-01-01

Patterns and collective phenomena such as firing synchronization are studied in networks of nonhomogeneous oscillatory neurons and mixtures of oscillatory and excitable neurons, with dynamics of each neuron described by a two-dimensional (2D) Rulkov map neuron. It is shown that as the coupling strength is increased, typical patterns emerge spatially, which propagate through the networks in the form of beautiful target waves or parallel ones depending on the size of networks. Furthermore, we investigate the transitions of firing synchronization characterized by the rate of firing when the coupling strength is increased. It is found that there exists an intermediate coupling strength; firing synchronization is minimal simultaneously irrespective of the size of networks. For further increasing the coupling strength, synchronization is enhanced. Since noise is inevitable in real neurons, we also investigate the effects of white noise on firing synchronization for different networks. For the networks of oscillatory neurons, it is shown that firing synchronization decreases when the noise level increases. For the missed networks, firing synchronization is robust under the noise conditions considered in this paper. Results presented in this paper should prove to be valuable for understanding the properties of collective dynamics in real neuronal networks

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

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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

The chronotron: a neuron that learns to fire temporally precise spike patterns.

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Răzvan V Florian

Full Text Available In many cases, neurons process information carried by the precise timings of spikes. Here we show how neurons can learn to generate specific temporally precise output spikes in response to input patterns of spikes having precise timings, thus processing and memorizing information that is entirely temporally coded, both as input and as output. We introduce two new supervised learning rules for spiking neurons with temporal coding of information (chronotrons, one that provides high memory capacity (E-learning, and one that has a higher biological plausibility (I-learning. With I-learning, the neuron learns to fire the target spike trains through synaptic changes that are proportional to the synaptic currents at the timings of real and target output spikes. We study these learning rules in computer simulations where we train integrate-and-fire neurons. Both learning rules allow neurons to fire at the desired timings, with sub-millisecond precision. We show how chronotrons can learn to classify their inputs, by firing identical, temporally precise spike trains for different inputs belonging to the same class. When the input is noisy, the classification also leads to noise reduction. We compute lower bounds for the memory capacity of chronotrons and explore the influence of various parameters on chronotrons' performance. The chronotrons can model neurons that encode information in the time of the first spike relative to the onset of salient stimuli or neurons in oscillatory networks that encode information in the phases of spikes relative to the background oscillation. Our results show that firing one spike per cycle optimizes memory capacity in neurons encoding information in the phase of firing relative to a background rhythm.

Intrinsic and integrative properties of substantia nigra pars reticulata neurons

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Zhou, Fu-Ming; Lee, Christian R.

2011-01-01

The GABA projection neurons of the substantia nigra pars reticulata (SNr) are output neurons for the basal ganglia and thus critical for movement control. Their most striking neurophysiological feature is sustained, spontaneous high frequency spike firing. A fundamental question is: what are the key ion channels supporting the remarkable firing capability in these neurons? Recent studies indicate that these neurons express tonically active TRPC3 channels that conduct a Na-dependent inward current even at hyperpolarized membrane potentials. When the membrane potential reaches −60 mV, a voltage-gated persistent sodium current (INaP) starts to activate, further depolarizing the membrane potential. At or slightly below −50 mV, the large transient voltage-activated sodium current (INaT) starts to activate and eventually triggers the rapid rising phase of action potentials. SNr GABA neurons have a higher density of (INaT), contributing to the faster rise and larger amplitude of action potentials, compared with the slow-spiking dopamine neurons. INaT also recovers from inactivation more quickly in SNr GABA neurons than in nigral dopamine neurons. In SNr GABA neurons, the rising phase of the action potential triggers the activation of high-threshold, inactivation-resistant Kv3-like channels that can rapidly repolarize the membrane. These intrinsic ion channels provide SNr GABA neurons with the ability to fire spontaneous and sustained high frequency spikes. Additionally, robust GABA inputs from direct pathway medium spiny neurons in the striatum and GABA neurons in the globus pallidus may inhibit and silence SNr GABA neurons, whereas glutamate synaptic input from the subthalamic nucleus may induce burst firing in SNr GABA neurons. Thus, afferent GABA and glutamate synaptic inputs sculpt the tonic high frequency firing of SNr GABA neurons and the consequent inhibition of their targets into an integrated motor control signal that is further fine-tuned by neuromodulators

Differential Activation of Fast-Spiking and Regular-Firing Neuron Populations During Movement and Reward in the Dorsal Medial Frontal Cortex

Science.gov (United States)

Insel, Nathan; Barnes, Carol A.

2015-01-01

The medial prefrontal cortex is thought to be important for guiding behavior according to an animal's expectations. Efforts to decode the region have focused not only on the question of what information it computes, but also how distinct circuit components become engaged during behavior. We find that the activity of regular-firing, putative projection neurons contains rich information about behavioral context and firing fields cluster around reward sites, while activity among putative inhibitory and fast-spiking neurons is most associated with movement and accompanying sensory stimulation. These dissociations were observed even between adjacent neurons with apparently reciprocal, inhibitory–excitatory connections. A smaller population of projection neurons with burst-firing patterns did not show clustered firing fields around rewards; these neurons, although heterogeneous, were generally less selective for behavioral context than regular-firing cells. The data suggest a network that tracks an animal's behavioral situation while, at the same time, regulating excitation levels to emphasize high valued positions. In this scenario, the function of fast-spiking inhibitory neurons is to constrain network output relative to incoming sensory flow. This scheme could serve as a bridge between abstract sensorimotor information and single-dimensional codes for value, providing a neural framework to generate expectations from behavioral state. PMID:24700585

Firing probability and mean firing rates of human muscle vasoconstrictor neurones are elevated during chronic asphyxia

DEFF Research Database (Denmark)

Ashley, Cynthia; Burton, Danielle; Sverrisdottir, Yrsa B

2010-01-01

in the obstructive sleep apnoea syndrome (OSAS) is associated with an increase in firing probability and mean firing rate, and an increase in multiple within-burst firing. Here we characterize the firing properties of muscle vasoconstrictor neurones in patients with chronic obstructive pulmonary disease (COPD), who...... are chronically asphyxic. We tested the hypothesis that this elevated chemical drive would shift the firing pattern from that seen in healthy subjects to that seen in OSAS. The mean firing probability (52%) and mean firing rate (0.92 Hz) of 17 muscle vasoconstrictor neurones recorded in COPD were comparable...

Estradiol-Dependent Stimulation and Suppression of Gonadotropin-Releasing Hormone Neuron Firing Activity by Corticotropin-Releasing Hormone in Female Mice.

Science.gov (United States)

Phumsatitpong, Chayarndorn; Moenter, Suzanne M

2018-01-01

Gonadotropin-releasing hormone (GnRH) neurons are the final central regulators of reproduction, integrating various inputs that modulate fertility. Stress typically inhibits reproduction but can be stimulatory; stress effects can also be modulated by steroid milieu. Corticotropin-releasing hormone (CRH) released during the stress response may suppress reproduction independent of downstream glucocorticoids. We hypothesized CRH suppresses fertility by decreasing GnRH neuron firing activity. To test this, mice were ovariectomized (OVX) and either implanted with an estradiol capsule (OVX+E) or not treated further to examine the influence of estradiol on GnRH neuron response to CRH. Targeted extracellular recordings were used to record firing activity from green fluorescent protein-identified GnRH neurons in brain slices before and during CRH treatment; recordings were done in the afternoon when estradiol has a positive feedback effect to increase GnRH neuron firing. In OVX mice, CRH did not affect the firing rate of GnRH neurons. In contrast, CRH exhibited dose-dependent stimulatory (30 nM) or inhibitory (100 nM) effects on GnRH neuron firing activity in OVX+E mice; both effects were reversible. The dose-dependent effects of CRH appear to result from activation of different receptor populations; a CRH receptor type-1 agonist increased firing activity in GnRH neurons, whereas a CRH receptor type-2 agonist decreased firing activity. CRH and specific agonists also differentially regulated short-term burst frequency and burst properties, including burst duration, spikes/burst, and/or intraburst interval. These results indicate that CRH alters GnRH neuron activity and that estradiol is required for CRH to exert both stimulatory and inhibitory effects on GnRH neurons. Copyright © 2018 Endocrine Society.

Increased neuronal firing in resting and sleep in areas of the macaque medial prefrontal cortex.

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Gabbott, Paul L; Rolls, Edmund T

2013-06-01

The medial prefrontal cortex (mPFC) of humans and macaques is an integral part of the default mode network and is a brain region that shows increased activation in the resting state. A previous paper from our laboratory reported significantly increased firing rates of neurons in the macaque subgenual cingulate cortex, Brodmann area (BA) 25, during disengagement from a task and also during slow wave sleep [E.T. Rolls et al. (2003) J. Neurophysiology, 90, 134-142]. Here we report the finding that there are neurons in other areas of mPFC that also increase their firing rates during disengagement from a task, drowsiness and eye-closure. During the neurophysiological recording of single mPFC cells (n = 249) in BAs 9, 10, 13 m, 14c, 24b and especially pregenual area 32, populations of neurons were identified whose firing rates altered significantly with eye-closure compared with eye-opening. Three types of neuron were identified: Type 1 cells (28.1% of the total population) significantly increased (mean + 329%; P ≪ 0.01) their average firing rate with eye-closure, from 3.1 spikes/s when awake to 10.2 spikes/s when asleep; Type 2 cells (6.0%) significantly decreased (mean -68%; P areas of mPFC, implicated in the anterior default mode network, there is a substantial population of neurons that significantly increase their firing rates during periods of eye-closure. Such neurons may be part of an interconnected network of distributed brain regions that are more active during periods of relaxed wakefulness than during attention-demanding tasks. © 2013 Federation of European Neuroscience Societies and Blackwell Publishing Ltd.

Dynamical behaviour of the firing in coupled neuronal system

International Nuclear Information System (INIS)

Wei Wang; Perez, G.; Cerdeira, H.A.

1993-03-01

The time interval sequences and the spatio-temporal patterns of the firings of a coupled neuronal network are investigated in this paper. For a single neuron stimulated by an external stimulus I, the time interval sequences show a low frequency firing of bursts of spikes, and reversed period-doubling cascade to a high frequency repetitive firing state as the stimulus I is increased. For two neurons coupled to each other through the firing of the spikes, the complexity of the time interval sequences becomes simple as the coupling strength increases. A network with large numbers of neurons shows a complex spatio-temporal pattern structure. As the coupling strength increases, the numbers of phase locked neurons increase and the time interval diagram shows temporal chaos and a bifurcation in the space. The dynamical behaviour is also verified by the Lyapunov exponent. (author). 17 refs, 6 figs

Pseudorabies virus infection alters neuronal activity and connectivity in vitro.

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Kelly M McCarthy

2009-10-01

Full Text Available Alpha-herpesviruses, including human herpes simplex virus 1 & 2, varicella zoster virus and the swine pseudorabies virus (PRV, infect the peripheral nervous system of their hosts. Symptoms of infection often include itching, numbness, or pain indicative of altered neurological function. To determine if there is an in vitro electrophysiological correlate to these characteristic in vivo symptoms, we infected cultured rat sympathetic neurons with well-characterized strains of PRV known to produce virulent or attenuated symptoms in animals. Whole-cell patch clamp recordings were made at various times after infection. By 8 hours of infection with virulent PRV, action potential (AP firing rates increased substantially and were accompanied by hyperpolarized resting membrane potentials and spikelet-like events. Coincident with the increase in AP firing rate, adjacent neurons exhibited coupled firing events, first with AP-spikelets and later with near identical resting membrane potentials and AP firing. Small fusion pores between adjacent cell bodies formed early after infection as demonstrated by transfer of the low molecular weight dye, Lucifer Yellow. Later, larger pores formed as demonstrated by transfer of high molecular weight Texas red-dextran conjugates between infected cells. Further evidence for viral-induced fusion pores was obtained by infecting neurons with a viral mutant defective for glycoprotein B, a component of the viral membrane fusion complex. These infected neurons were essentially identical to mock infected neurons: no increased AP firing, no spikelet-like events, and no electrical or dye transfer. Infection with PRV Bartha, an attenuated circuit-tracing strain delayed, but did not eliminate the increased neuronal activity and coupling events. We suggest that formation of fusion pores between infected neurons results in electrical coupling and elevated firing rates, and that these processes may contribute to the altered neural

Visualizing neuronal network connectivity with connectivity pattern tables

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Eilen Nordlie

2010-01-01

Full Text Available Complex ideas are best conveyed through well-designed illustrations. Up to now, computational neuroscientists have mostly relied on box-and-arrow diagrams of even complex neuronal networks, often using ad hoc notations with conflicting use of symbols from paper to paper. This significantly impedes the communication of ideas in neuronal network modeling. We present here Connectivity Pattern Tables (CPTs as a clutter-free visualization of connectivity in large neuronal networks containing two-dimensional populations of neurons. CPTs can be generated automatically from the same script code used to create the actual network in the NEST simulator. Through aggregation, CPTs can be viewed at different levels, providing either full detail or summary information. We also provide the open source ConnPlotter tool as a means to create connectivity pattern tables.

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

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Sadeh, Sadra; Rotter, Stefan

2015-01-01

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

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

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Sadra Sadeh

2015-01-01

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

Comparison of the dynamics of neural interactions in integrate-and-fire networks with current-based and conductance-based synapses

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Stefano eCavallari

2014-03-01

Full Text Available Models of networks of Leaky Integrate-and-Fire neurons (LIF are a widely used tool for theoretical investigations of brain function. These models have been used both with current- and conductance-based synapses. However, the differences in the dynamics expressed by these two approaches have been so far mainly studied at the single neuron level. To investigate how these synaptic models affect network activity, we compared the single-neuron and neural population dynamics of conductance-based networks (COBN and current-based networks (CUBN of LIF neurons. These networks were endowed with sparse excitatory and inhibitory recurrent connections, and were tested in conditions including both low- and high-conductance states. We developed a novel procedure to obtain comparable networks by properly tuning the synaptic parameters not shared by the models. The so defined comparable networks displayed an excellent and robust match of first order statistics (average single neuron firing rates and average frequency spectrum of network activity. However, these comparable networks showed profound differences in the second order statistics of neural population interactions and in the modulation of these properties by external inputs. The correlation between inhibitory and excitatory synaptic currents and the cross-neuron correlation between synaptic inputs, membrane potentials and spike trains were stronger and more stimulus-sensitive in the COBN. Because of these properties, the spike train correlation carried more information about the strength of the input in the COBN, although the firing rates were equally informative in both network models. Moreover, COBN showed stronger neuronal population synchronization in the gamma band, and their spectral information about the network input was higher and spread over a broader range of frequencies. These results suggest that second order properties of network dynamics depend strongly on the choice of synaptic model.

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

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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

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

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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.

Firing Patterns and Transitions in Coupled Neurons Controlled by a Pacemaker

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Mei-Sheng, Li; Qi-Shao, Lu; Li-Xia, Duan; Qing-Yun, Wang

2008-01-01

To reveal the dynamics of neuronal networks with pacemakers, the firing patterns and their transitions are investigated in a ring HR neuronal network with gap junctions under the control of a pacemaker. Compared with the situation without pacemaker, the neurons in the network can exhibit various firing patterns as the external current is applied or the coupling strength of pacemaker varies. The results are beneficial for understanding the complex cooperative behaviour of large neural assemblies with pacemaker control

Cerebellar Nuclear Neurons Use Time and Rate Coding to Transmit Purkinje Neuron Pauses.

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Sudhakar, Shyam Kumar; Torben-Nielsen, Benjamin; De Schutter, Erik

2015-12-01

Neurons of the cerebellar nuclei convey the final output of the cerebellum to their targets in various parts of the brain. Within the cerebellum their direct upstream connections originate from inhibitory Purkinje neurons. Purkinje neurons have a complex firing pattern of regular spikes interrupted by intermittent pauses of variable length. How can the cerebellar nucleus process this complex input pattern? In this modeling study, we investigate different forms of Purkinje neuron simple spike pause synchrony and its influence on candidate coding strategies in the cerebellar nuclei. That is, we investigate how different alignments of synchronous pauses in synthetic Purkinje neuron spike trains affect either time-locking or rate-changes in the downstream nuclei. We find that Purkinje neuron synchrony is mainly represented by changes in the firing rate of cerebellar nuclei neurons. Pause beginning synchronization produced a unique effect on nuclei neuron firing, while the effect of pause ending and pause overlapping synchronization could not be distinguished from each other. Pause beginning synchronization produced better time-locking of nuclear neurons for short length pauses. We also characterize the effect of pause length and spike jitter on the nuclear neuron firing. Additionally, we find that the rate of rebound responses in nuclear neurons after a synchronous pause is controlled by the firing rate of Purkinje neurons preceding it.

Cerebellar Nuclear Neurons Use Time and Rate Coding to Transmit Purkinje Neuron Pauses

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Sudhakar, Shyam Kumar; Torben-Nielsen, Benjamin; De Schutter, Erik

2015-01-01

Neurons of the cerebellar nuclei convey the final output of the cerebellum to their targets in various parts of the brain. Within the cerebellum their direct upstream connections originate from inhibitory Purkinje neurons. Purkinje neurons have a complex firing pattern of regular spikes interrupted by intermittent pauses of variable length. How can the cerebellar nucleus process this complex input pattern? In this modeling study, we investigate different forms of Purkinje neuron simple spike pause synchrony and its influence on candidate coding strategies in the cerebellar nuclei. That is, we investigate how different alignments of synchronous pauses in synthetic Purkinje neuron spike trains affect either time-locking or rate-changes in the downstream nuclei. We find that Purkinje neuron synchrony is mainly represented by changes in the firing rate of cerebellar nuclei neurons. Pause beginning synchronization produced a unique effect on nuclei neuron firing, while the effect of pause ending and pause overlapping synchronization could not be distinguished from each other. Pause beginning synchronization produced better time-locking of nuclear neurons for short length pauses. We also characterize the effect of pause length and spike jitter on the nuclear neuron firing. Additionally, we find that the rate of rebound responses in nuclear neurons after a synchronous pause is controlled by the firing rate of Purkinje neurons preceding it. PMID:26630202

Effects of dendritic load on the firing frequency of oscillating neurons.

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Schwemmer, Michael A; Lewis, Timothy J

2011-03-01

We study the effects of passive dendritic properties on the dynamics of neuronal oscillators. We find that the addition of a passive dendrite can sometimes have counterintuitive effects on firing frequency. Specifically, the addition of a hyperpolarized passive dendritic load can either increase, decrease, or have negligible effects on firing frequency. We use the theory of weak coupling to derive phase equations for "ball-and-stick" model neurons and two-compartment model neurons. We then develop a framework for understanding how the addition of passive dendrites modulates the frequency of neuronal oscillators. We show that the average value of the neuronal oscillator's phase response curves measures the sensitivity of the neuron's firing rate to the dendritic load, including whether the addition of the dendrite causes an increase or decrease in firing frequency. We interpret this finding in terms of to the slope of the neuronal oscillator's frequency-applied current curve. We also show that equivalent results exist for constant and noisy point-source input to the dendrite. We note that the results are not specific to neurons but are applicable to any oscillator subject to a passive load.

Fire, humans and landscape. Is there a connection?

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Valese, Eva; Ascoli, Davide; Conedera, Marco; Held, Alex

2013-04-01

Fire evolved on the earth under the direct influence of climate and the accumulation of burnable biomass at various times and spatial scales. As a result, fire regimes depend not only on climatic and biological factors, but also greatly reflect the cultural background of how people do manage ecosystems and fire. A new awareness among scientists and managers has been rising about the ecological role of fire and the necessity to understand its past natural and cultural dynamics in different ecosystems, in order to preserve present ecosystem functionality and minimize management costs and negative impacts. As a consequence we assisted in the last decades to a general shift from the fire control to the fire management approach, where fire prevention, fire danger rating, fire ecology, fire pre-suppression and suppression strategies are fully integrated in the landscape management. Nowadays, a large number of authors recognize that a total suppression strategy, as the one adopted during last decades, leads to a fire paradox: the more we fight for putting out all fires, the more extreme events occur and cause long term damages. The aim of this review is to provide a state of art about the connection between fire, humans and landscape, along time and space. Negative and positive impacts on ecosystem services and values are put in evidence, as well as their incidence on human aptitude to fire use as to fire suppression. In order to capture a consistent fragment of fire history, palaeofires and related palynological studies are considered. They enable a valuable, even if partial, look at the millenary fire regime. Actual strategies and future directions are described in order to show what are the alternatives for living with fire, since removing completely this disturbance from earth is not a option, nor feasible neither advisable. Examples from the world, in particular from the Alps and the Mediterranean basin, are shown for better illustrating the signature of

Connection behaviour and the robustness of steel-framed structures in fire

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Burgess Ian

2018-01-01

Full Text Available The full-scale fire tests at Cardington in the 1990s, and the collapse of at least one of the WTC buildings in 2001, illustrated that connections are potentially the most vulnerable parts of a structure in fire. Fracture of connections causes structural discontinuities and reduces the robustness provided by alternative load paths. An understanding of connection performance is essential to the assessment of structural robustness, and so to structural design against progressive collapse. The forces and deformations to which connectionscan be subjected during a fire differ significantly from those assumed in general design. The internal forces i generally start with moment and shear at ambient temperature, then superposing compression in the initial stages of a fire, which finally changes to catenary tension at high temperatures. If a connection does not have sufficient resistance or ductility to accommodate simultaneous large rotations and normal forces, then connections may fracture, leading to extensive damage or progressive collapse of the structure. Practical assessment of the robustness of steel connections in fire will inevitably rely largely on numerical modelling, but this is unlikely to include general-purpose finite element modelling, because of the complexity of such models. The most promising alternative is the component method, a practical approach which can be included within global three-dimensional frame analysis. The connection is represented by an assembly of individual components with known mechanical properties. Component characterization must include high-deflection elevated-temperature behaviour, and represent it up to fracture.In reality a connection may either be able to regain its stability after the initial fracture of one (or a few components, or the first failure may trigger a cascade of failures of other components, leading to complete detachment of the supported member. Numerical modelling must be capable of

Chaos in integrate-and-fire dynamical systems

International Nuclear Information System (INIS)

Coombes, S.

2000-01-01

Integrate-and-fire (IF) mechanisms are often studied within the context of neural dynamics. From a mathematical perspective they represent a minimal yet biologically realistic model of a spiking neuron. The non-smooth nature of the dynamics leads to extremely rich spike train behavior capable of explaining a variety of biological phenomenon including phase-locked states, mode-locking, bursting and pattern formation. The conditions under which chaotic spike trains may be generated in synaptically interacting networks of neural oscillators is an important open question. Using techniques originally introduced for the study of impact oscillators we develop the notion of a Liapunov exponent for IF systems. In the strong coupling regime a network may undergo a discrete Turing-Hopf bifurcation of the firing times from a synchronous state to a state with periodic or quasiperiodic variations of the interspike intervals on closed orbits. Away from the bifurcation point these invariant circles may break up. We establish numerically that in this case the largest IF Liapunov exponent becomes positive. Hence, one route to chaos in networks of synaptically coupled IF neurons is via the breakup of invariant circles

Optimal and Local Connectivity Between Neuron and Synapse Array in the Quantum Dot/Silicon Brain

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Duong, Tuan A.; Assad, Christopher; Thakoor, Anikumar P.

2010-01-01

This innovation is used to connect between synapse and neuron arrays using nanowire in quantum dot and metal in CMOS (complementary metal oxide semiconductor) technology to enable the density of a brain-like connection in hardware. The hardware implementation combines three technologies: 1. Quantum dot and nanowire-based compact synaptic cell (50x50 sq nm) with inherently low parasitic capacitance (hence, low dynamic power approx.l0(exp -11) watts/synapse), 2. Neuron and learning circuits implemented in 50-nm CMOS technology, to be integrated with quantum dot and nanowire synapse, and 3. 3D stacking approach to achieve the overall numbers of high density O(10(exp 12)) synapses and O(10(exp 8)) neurons in the overall system. In a 1-sq cm of quantum dot layer sitting on a 50-nm CMOS layer, innovators were able to pack a 10(exp 6)-neuron and 10(exp 10)-synapse array; however, the constraint for the connection scheme is that each neuron will receive a non-identical 10(exp 4)-synapse set, including itself, via its efficacy of the connection. This is not a fully connected system where the 100x100 synapse array only has a 100-input data bus and 100-output data bus. Due to the data bus sharing, it poses a great challenge to have a complete connected system, and its constraint within the quantum dot and silicon wafer layer. For an effective connection scheme, there are three conditions to be met: 1. Local connection. 2. The nanowire should be connected locally, not globally from which it helps to maximize the data flow by sharing the same wire space location. 3. Each synapse can have an alternate summation line if needed (this option is doable based on the simple mask creation). The 10(exp 3)x10(exp 3)-neuron array was partitioned into a 10-block, 10(exp 2)x10(exp 3)-neuron array. This building block can be completely mapped within itself (10,000 synapses to a neuron).

Complementary responses to mean and variance modulations in the perfect integrate-and-fire model.

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Pressley, Joanna; Troyer, Todd W

2009-07-01

In the perfect integrate-and-fire model (PIF), the membrane voltage is proportional to the integral of the input current since the time of the previous spike. It has been shown that the firing rate within a noise free ensemble of PIF neurons responds instantaneously to dynamic changes in the input current, whereas in the presence of white noise, model neurons preferentially pass low frequency modulations of the mean current. Here, we prove that when the input variance is perturbed while holding the mean current constant, the PIF responds preferentially to high frequency modulations. Moreover, the linear filters for mean and variance modulations are complementary, adding exactly to one. Since changes in the rate of Poisson distributed inputs lead to proportional changes in the mean and variance, these results imply that an ensemble of PIF neurons transmits a perfect replica of the time-varying input rate for Poisson distributed input. A more general argument shows that this property holds for any signal leading to proportional changes in the mean and variance of the input current.

The calcium-binding protein parvalbumin modulates the firing 1 properties of the reticular thalamic nucleus bursting neurons.

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Albéri, Lavinia; Lintas, Alessandra; Kretz, Robert; Schwaller, Beat; Villa, Alessandro E P

2013-06-01

The reticular thalamic nucleus (RTN) of the mouse is characterized by an overwhelming majority of GABAergic neurons receiving afferences from both the thalamus and the cerebral cortex and sending projections mainly on thalamocortical neurons. The RTN neurons express high levels of the "slow Ca(2+) buffer" parvalbumin (PV) and are characterized by low-threshold Ca(2+) currents, I(T). We performed extracellular recordings in ketamine/xylazine anesthetized mice in the rostromedial portion of the RTN. In the RTN of wild-type and PV knockout (PVKO) mice we distinguished four types of neurons characterized on the basis of their firing pattern: irregular firing (type I), medium bursting (type II), long bursting (type III), and tonically firing (type IV). Compared with wild-type mice, we observed in the PVKOs the medium bursting (type II) more frequently than the long bursting type and longer interspike intervals within the burst without affecting the number of spikes. This suggests that PV may affect the firing properties of RTN neurons via a mechanism associated with the kinetics of burst discharges. Ca(v)3.2 channels, which mediate the I(T) currents, were more localized to the somatic plasma membrane of RTN neurons in PVKO mice, whereas Ca(v)3.3 expression was similar in both genotypes. The immunoelectron microscopy analysis showed that Ca(v)3.2 channels were localized at active axosomatic synapses, thus suggesting that the differential localization of Ca(v)3.2 in the PVKOs may affect bursting dynamics. Cross-correlation analysis of simultaneously recorded neurons from the same electrode tip showed that about one-third of the cell pairs tended to fire synchronously in both genotypes, independent of PV expression. In summary, PV deficiency does not affect the functional connectivity between RTN neurons but affects the distribution of Ca(v)3.2 channels and the dynamics of burst discharges of RTN cells, which in turn regulate the activity in the thalamocortical circuit.

GABAB-receptor activation alters the firing pattern of dopamine neurons in the rat substantia nigra.

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Engberg, G; Kling-Petersen, T; Nissbrandt, H

1993-11-01

Previous electrophysiological experiments have emphasized the importance of the firing pattern for the functioning of midbrain dopamine (DA) neurons. In this regard, excitatory amino acid receptors appear to constitute an important modulatory control mechanism. In the present study, extracellular recording techniques were used to investigate the significance of GABAB-receptor activation for the firing properties of DA neurons in the substantia nigra (SN) in the rat. Intravenous administration of the GABAB-receptor agonist baclofen (1-16 mg/kg) was associated with a dose-dependent regularization of the firing pattern, concomitant with a reduction in burst firing. At higher doses (16-32 mg/kg), the firing rate of the DA neurons was dose-dependently decreased. Also, microiontophoretic application of baclofen regularized the firing pattern of nigral DA neurons, including a reduction of burst firing. Both the regularization of the firing pattern and inhibition of firing rate produced by systemic baclofen administration was antagonized by the GABAB-receptor antagonist CGP 35348 (200 mg/kg, i.v.). The GABAA-receptor agonist muscimol produced effects on the firing properties of DA neurons that were opposite to those observed following baclofen, i.e., an increase in firing rate accompanied by a decreased regularity. The NMDA receptor antagonist MK 801 (0.4-3.2 mg/kg, i.v.) produced a moderate, dose-dependent increase in the firing rate of the nigral DA neurons as well as a slightly regularized firing pattern. Pretreatment with MK 801 (3.2 mg/kg, i.v., 3-10 min) did neither promote nor prevent the regularization of the firing pattern or inhibition of firing rate on the nigral DA neurons produced by baclofen. The present results clearly show that GABAB-receptors can alter the firing pattern of nigral DA neurons, hereby counterbalancing the previously described ability of glutamate to induce burst firing activity on these neurons.

Calcitonin gene-related peptide alters the firing rates of hypothalamic temperature sensitive and insensitive neurons

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Grimm Eleanor R

2008-07-01

Full Text Available Abstract Background Transient hyperthermic shifts in body temperature have been linked to the endogenous hormone calcitonin gene-related peptide (CGRP, which can increase sympathetic activation and metabolic heat production. Recent studies have demonstrated that these centrally mediated responses may result from CGRP dependent changes in the activity of thermoregulatory neurons in the preoptic and anterior regions of the hypothalamus (POAH. Results Using a tissue slice preparation, we recorded the single-unit activity of POAH neurons from the adult male rat, in response to temperature and CGRP (10 μM. Based on the slope of firing rate as a function of temperature, neurons were classified as either warm sensitive or temperature insensitive. All warm sensitive neurons responded to CGRP with a significant decrease in firing rate. While CGRP did not alter the firing rates of some temperature insensitive neurons, responsive neurons showed an increase in firing rate. Conclusion With respect to current models of thermoregulatory control, these CGRP dependent changes in firing rate would result in hyperthermia. This suggests that both warm sensitive and temperature insensitive neurons in the POAH may play a role in producing this hyperthermic shift in temperature.

Storage of phase-coded patterns via STDP in fully-connected and sparse network: a study of the network capacity

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Silvia Scarpetta

2010-08-01

Full Text Available We study the storage and retrieval of phase-coded patterns as stable dynamical attractors in recurrent neural networks, for both an analog and a integrate-and-fire spiking model. The synaptic strength is determined by a learning rule based on spike-time-dependent plasticity, with an asymmetric time window depending on the relative timing between pre- and post-synaptic activity. We store multiple patterns and study the network capacity. For the analog model, we find that the network capacity scales linearly with the network size, and that both capacity and the oscillation frequency of the retrieval state depend on the asymmetry of the learning time window. In addition to fully-connected networks, we study sparse networks, where each neuron is connected only to a small number $zll N$ of other neurons. Connections can be short range, between neighboring neurons placed on a regular lattice, or long range, between randomly chosen pairs of neurons. We find that a small fraction of long range connections is able to amplify the capacity of the network. This imply that a small-world-network topology is optimal, as a compromise between the cost of long range connections and the capacity increase. Also in the spiking integrate and fire model the crucial result of storing and retrieval of multiple phase-coded patterns is observed. The capacity of the fully-connected spiking network is investigated, together with the relation between oscillation frequency of retrieval state and window asymmetry.

[Patterns of action potential firing in cortical neurons of neonatal mice and their electrophysiological property].

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Furong, Liu; Shengtian, L I

2016-05-25

To investigate patterns of action potential firing in cortical heurons of neonatal mice and their electrophysiological properties. The passive and active membrane properties of cortical neurons from 3-d neonatal mice were observed by whole-cell patch clamp with different voltage and current mode. Three patterns of action potential firing were identified in response to depolarized current injection. The effects of action potential firing patterns on voltage-dependent inward and outward current were found. Neurons with three different firing patterns had different thresholds of depolarized current. In the morphology analysis of action potential, the three type neurons were different in rise time, duration, amplitude and threshold of the first action potential evoked by 80 pA current injection. The passive properties were similar in three patterns of action potential firing. These results indicate that newborn cortical neurons exhibit different patterns of action potential firing with different action potential parameters such as shape and threshold.

Three Types of Cortical L5 Neurons that Differ in Brain-Wide Connectivity and Function

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Kim, Euiseok J.; Juavinett, Ashley L.; Kyubwa, Espoir M.; Jacobs, Matthew W.; Callaway, Edward M.

2015-01-01

SUMMARY Cortical layer 5 (L5) pyramidal neurons integrate inputs from many sources and distribute outputs to cortical and subcortical structures. Previous studies demonstrate two L5 pyramid types: cortico-cortical (CC) and cortico-subcortical (CS). We characterize connectivity and function of these cell types in mouse primary visual cortex and reveal a new subtype. Unlike previously described L5 CC and CS neurons, this new subtype does not project to striatum [cortico-cortical, non-striatal (CC-NS)] and has distinct morphology, physiology and visual responses. Monosynaptic rabies tracing reveals that CC neurons preferentially receive input from higher visual areas, while CS neurons receive more input from structures implicated in top-down modulation of brain states. CS neurons are also more direction-selective and prefer faster stimuli than CC neurons. These differences suggest distinct roles as specialized output channels, with CS neurons integrating information and generating responses more relevant to movement control and CC neurons being more important in visual perception. PMID:26671462

A spiking neuron circuit based on a carbon nanotube transistor

International Nuclear Information System (INIS)

Chen, C-L; Kim, K; Truong, Q; Shen, A; Li, Z; Chen, Y

2012-01-01

A spiking neuron circuit based on a carbon nanotube (CNT) transistor is presented in this paper. The spiking neuron circuit has a crossbar architecture in which the transistor gates are connected to its row electrodes and the transistor sources are connected to its column electrodes. An electrochemical cell is incorporated in the gate of the transistor by sandwiching a hydrogen-doped poly(ethylene glycol)methyl ether (PEG) electrolyte between the CNT channel and the top gate electrode. An input spike applied to the gate triggers a dynamic drift of the hydrogen ions in the PEG electrolyte, resulting in a post-synaptic current (PSC) through the CNT channel. Spikes input into the rows trigger PSCs through multiple CNT transistors, and PSCs cumulate in the columns and integrate into a ‘soma’ circuit to trigger output spikes based on an integrate-and-fire mechanism. The spiking neuron circuit can potentially emulate biological neuron networks and their intelligent functions. (paper)

Rich-Club Organization in Effective Connectivity among Cortical Neurons

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Shimono, Masanori; Ito, Shinya; Yeh, Fang-Chin; Timme, Nicholas; Myroshnychenko, Maxym; Lapish, Christopher C.; Tosi, Zachary; Hottowy, Pawel; Smith, Wesley C.; Masmanidis, Sotiris C.; Litke, Alan M.; Sporns, Olaf; Beggs, John M.

2016-01-01

The performance of complex networks, like the brain, depends on how effectively their elements communicate. Despite the importance of communication, it is virtually unknown how information is transferred in local cortical networks, consisting of hundreds of closely spaced neurons. To address this, it is important to record simultaneously from hundreds of neurons at a spacing that matches typical axonal connection distances, and at a temporal resolution that matches synaptic delays. We used a 512-electrode array (60 μm spacing) to record spontaneous activity at 20 kHz from up to 500 neurons simultaneously in slice cultures of mouse somatosensory cortex for 1 h at a time. We applied a previously validated version of transfer entropy to quantify information transfer. Similar to in vivo reports, we found an approximately lognormal distribution of firing rates. Pairwise information transfer strengths also were nearly lognormally distributed, similar to reports of synaptic strengths. Some neurons transferred and received much more information than others, which is consistent with previous predictions. Neurons with the highest outgoing and incoming information transfer were more strongly connected to each other than chance, thus forming a “rich club.” We found similar results in networks recorded in vivo from rodent cortex, suggesting the generality of these findings. A rich-club structure has been found previously in large-scale human brain networks and is thought to facilitate communication between cortical regions. The discovery of a small, but information-rich, subset of neurons within cortical regions suggests that this population will play a vital role in communication, learning, and memory. SIGNIFICANCE STATEMENT Many studies have focused on communication networks between cortical brain regions. In contrast, very few studies have examined communication networks within a cortical region. This is the first study to combine such a large number of neurons (several

Rich-Club Organization in Effective Connectivity among Cortical Neurons.

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Nigam, Sunny; Shimono, Masanori; Ito, Shinya; Yeh, Fang-Chin; Timme, Nicholas; Myroshnychenko, Maxym; Lapish, Christopher C; Tosi, Zachary; Hottowy, Pawel; Smith, Wesley C; Masmanidis, Sotiris C; Litke, Alan M; Sporns, Olaf; Beggs, John M

2016-01-20

The performance of complex networks, like the brain, depends on how effectively their elements communicate. Despite the importance of communication, it is virtually unknown how information is transferred in local cortical networks, consisting of hundreds of closely spaced neurons. To address this, it is important to record simultaneously from hundreds of neurons at a spacing that matches typical axonal connection distances, and at a temporal resolution that matches synaptic delays. We used a 512-electrode array (60 μm spacing) to record spontaneous activity at 20 kHz from up to 500 neurons simultaneously in slice cultures of mouse somatosensory cortex for 1 h at a time. We applied a previously validated version of transfer entropy to quantify information transfer. Similar to in vivo reports, we found an approximately lognormal distribution of firing rates. Pairwise information transfer strengths also were nearly lognormally distributed, similar to reports of synaptic strengths. Some neurons transferred and received much more information than others, which is consistent with previous predictions. Neurons with the highest outgoing and incoming information transfer were more strongly connected to each other than chance, thus forming a "rich club." We found similar results in networks recorded in vivo from rodent cortex, suggesting the generality of these findings. A rich-club structure has been found previously in large-scale human brain networks and is thought to facilitate communication between cortical regions. The discovery of a small, but information-rich, subset of neurons within cortical regions suggests that this population will play a vital role in communication, learning, and memory. Significance statement: Many studies have focused on communication networks between cortical brain regions. In contrast, very few studies have examined communication networks within a cortical region. This is the first study to combine such a large number of neurons (several

Training the integrate-and-fire model with the informax principle: I

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Feng Jianfeng; Buxton, Hilary [COGS, Sussex University, Brighton (United Kingdom); Deng Yingchun [Department of Mathematics, Hunan Normal University, Changsha (China)

2002-03-15

In terms of the informax principle, and the input-output relationship of the integrate-and-fire (IF) model, IF neuron learning rules are developed. For supervised learning and with uniform weight of synapses (the theoretically tractable case), we show that the derived learning rule is stable and the stable state is unique. For unsupervised learning, within physiologically reasonable parameter regions, both long-term potentiation (LTP) and long-term depression (LTD) could happen when the inhibitory input is weak, but LTD cannot be observed when inhibitory input is strong enough. When both LTP and LTD occur, LTD is observable when the output of the postsynaptic neuron is faster than pre-synaptic inputs, otherwise LTP is observable, as observed in recent experiments. Learning rules of general cases are also studied and numerical examples show that the derived learning rule tends to equalize the contribution of different inputs to the output firing rates. (author)

Altering neuronal excitability to preserve network connectivity in a computational model of Alzheimer's disease.

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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.

The effects of prostaglandin E2 on the firing rate activity of thermosensitive and temperature insensitive neurons in the ventromedial preoptic area of the rat hypothalamus.

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Ranels, Heather J; Griffin, John D

2003-02-21

In response to an immune system challenge with lipopolysaccharide (LPS), recent work has shown that Fos immunoreactivity is displayed by neurons in the ventromedial preoptic area of the hypothalamus (VMPO). In addition, neurons in this region show distinct axonal projections to the anterior perifornical area (APFx) and the paraventricular nucleus (PVN). It has been hypothesized that neurons within the VMPO integrate their local responses to temperature with changes in firing activity that result from LPS induced production of prostaglandin E(2) (PGE(2)). This may be an important mechanism by which the set-point regulation of thermoeffector neurons in the APFx and PVN is altered, resulting in hyperthermia. To characterize the firing rate activity of VMPO neurons, single-unit recordings were made of neuronal extracellular activity in rat hypothalamic tissue slices. Based on the slope of firing rate as a function of tissue temperature, neurons were classified as either warm sensitive or temperature insensitive. Neurons were then treated with PGE(2) (200 nM) while tissue temperature was held at a constant level ( approximately 36 degrees C). The majority of temperature insensitive neurons responded to PGE(2) with an increase in firing rate activity, while warm sensitive neurons showed a reduction in firing rate. This suggests that both warm sensitive and temperature insensitive neurons in the VMPO may play critical and contrasting roles in the production of a fever during an acute phase response to infection.

Population coding in sparsely connected networks of noisy neurons.

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Tripp, Bryan P; Orchard, Jeff

2012-01-01

This study examines the relationship between population coding and spatial connection statistics in networks of noisy neurons. Encoding of sensory information in the neocortex is thought to require coordinated neural populations, because individual cortical neurons respond to a wide range of stimuli, and exhibit highly variable spiking in response to repeated stimuli. Population coding is rooted in network structure, because cortical neurons receive information only from other neurons, and because the information they encode must be decoded by other neurons, if it is to affect behavior. However, population coding theory has often ignored network structure, or assumed discrete, fully connected populations (in contrast with the sparsely connected, continuous sheet of the cortex). In this study, we modeled a sheet of cortical neurons with sparse, primarily local connections, and found that a network with this structure could encode multiple internal state variables with high signal-to-noise ratio. However, we were unable to create high-fidelity networks by instantiating connections at random according to spatial connection probabilities. In our models, high-fidelity networks required additional structure, with higher cluster factors and correlations between the inputs to nearby neurons.

Population Coding in Sparsely Connected Networks of Noisy Neurons

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Bryan Patrick Tripp

2012-05-01

Full Text Available This study examines the relationship between population coding and spatial connection statistics in networks of noisy neurons. Encoding of sensory information in the neocortex is thought to require coordinated neural populations, because individual cortical neurons respond to a wide range of stimuli, and exhibit highly variable spiking in response to repeated stimuli. Population coding is rooted in network structure, because cortical neurons receive information only from other neurons, and because the information they encode must be decoded by other neurons, if it is to affect behaviour. However, population coding theory has often ignored network structure, or assumed discrete, fully-connected populations (in contrast with the sparsely connected, continuous sheet of the cortex. In this study, we model a sheet of cortical neurons with sparse, primarily local connections, and find that a network with this structure can encode multiple internal state variables with high signal-to-noise ratio. However, in our model, although connection probability varies with the distance between neurons, we find that the connections cannot be instantiated at random according to these probabilities, but must have additional structure if information is to be encoded with high fidelity.

Dissociable effects of dopamine on neuronal firing rate and synchrony in the dorsal striatum

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John M Burkhardt

2009-10-01

Full Text Available Previous studies showed that dopamine depletion leads to both changes in firing rate and in neuronal synchrony in the basal ganglia. Since dopamine D1 and D2 receptors are preferentially expressed in striatonigral and striatopallidal medium spiny neurons, respectively, we investigated the relative contribution of lack of D1 and/or D2-type receptor activation to the changes in striatal firing rate and synchrony observed after dopamine depletion. Similar to what was observed after dopamine depletion, co-administration of D1 and D2 antagonists to mice chronically implanted with multielectrode arrays in the striatum caused significant changes in firing rate, power of the local field potential (LFP oscillations, and synchrony measured by the entrainment of neurons to striatal local field potentials. However, although blockade of either D1 or D2 type receptors produced similarly severe akinesia, the effects on neural activity differed. Blockade of D2 receptors affected the firing rate of medium spiny neurons and the power of the LFP oscillations substantially, but it did not affect synchrony to the same extent. In contrast, D1 blockade affected synchrony dramatically, but had less substantial effects on firing rate and LFP power. Furthermore, there was no consistent relation between neurons changing firing rate and changing LFP entrainment after dopamine blockade. Our results suggest that the changes in rate and entrainment to the LFP observed in medium spiny neurons after dopamine depletion are somewhat dissociable, and that lack of D1- or D2-type receptor activation can exert independent yet interactive pathological effects during the progression of Parkinson’s disease.

A scalable neural chip with synaptic electronics using CMOS integrated memristors

International Nuclear Information System (INIS)

Cruz-Albrecht, Jose M; Derosier, Timothy; Srinivasa, Narayan

2013-01-01

The design and simulation of a scalable neural chip with synaptic electronics using nanoscale memristors fully integrated with complementary metal–oxide–semiconductor (CMOS) is presented. The circuit consists of integrate-and-fire neurons and synapses with spike-timing dependent plasticity (STDP). The synaptic conductance values can be stored in memristors with eight levels, and the topology of connections between neurons is reconfigurable. The circuit has been designed using a 90 nm CMOS process with via connections to on-chip post-processed memristor arrays. The design has about 16 million CMOS transistors and 73 728 integrated memristors. We provide circuit level simulations of the entire chip performing neuronal and synaptic computations that result in biologically realistic functional behavior. (paper)

Examining Neuronal Connectivity and Its Role in Learning and Memory

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Gala, Rohan

Learning and long-term memory formation are accompanied with changes in the patterns and weights of synaptic connections in the underlying neuronal network. However, the fundamental rules that drive connectivity changes, and the precise structure-function relationships within neuronal networks remain elusive. Technological improvements over the last few decades have enabled the observation of large but specific subsets of neurons and their connections in unprecedented detail. Devising robust and automated computational methods is critical to distill information from ever-increasing volumes of raw experimental data. Moreover, statistical models and theoretical frameworks are required to interpret the data and assemble evidence into understanding of brain function. In this thesis, I first describe computational methods to reconstruct connectivity based on light microscopy imaging experiments. Next, I use these methods to quantify structural changes in connectivity based on in vivo time-lapse imaging experiments. Finally, I present a theoretical model of associative learning that can explain many stereotypical features of experimentally observed connectivity.

Three Types of Cortical Layer 5 Neurons That Differ in Brain-wide Connectivity and Function.

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Kim, Euiseok J; Juavinett, Ashley L; Kyubwa, Espoir M; Jacobs, Matthew W; Callaway, Edward M

2015-12-16

Cortical layer 5 (L5) pyramidal neurons integrate inputs from many sources and distribute outputs to cortical and subcortical structures. Previous studies demonstrate two L5 pyramid types: cortico-cortical (CC) and cortico-subcortical (CS). We characterize connectivity and function of these cell types in mouse primary visual cortex and reveal a new subtype. Unlike previously described L5 CC and CS neurons, this new subtype does not project to striatum [cortico-cortical, non-striatal (CC-NS)] and has distinct morphology, physiology, and visual responses. Monosynaptic rabies tracing reveals that CC neurons preferentially receive input from higher visual areas, while CS neurons receive more input from structures implicated in top-down modulation of brain states. CS neurons are also more direction-selective and prefer faster stimuli than CC neurons. These differences suggest distinct roles as specialized output channels, with CS neurons integrating information and generating responses more relevant to movement control and CC neurons being more important in visual perception. Copyright © 2015 Elsevier Inc. All rights reserved.

Bursting as a source of non-linear determinism in the firing patterns of nigral dopamine neurons.

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Jeong, Jaeseung; Shi, Wei-Xing; Hoffman, Ralph; Oh, Jihoon; Gore, John C; Bunney, Benjamin S; Peterson, Bradley S

2012-11-01

Nigral dopamine (DA) neurons in vivo exhibit complex firing patterns consisting of tonic single-spikes and phasic bursts that encode information for certain types of reward-related learning and behavior. Non-linear dynamical analysis has previously demonstrated the presence of a non-linear deterministic structure in complex firing patterns of DA neurons, yet the origin of this non-linear determinism remains unknown. In this study, we hypothesized that bursting activity is the primary source of non-linear determinism in the firing patterns of DA neurons. To test this hypothesis, we investigated the dimension complexity of inter-spike interval data recorded in vivo from bursting and non-bursting DA neurons in the chloral hydrate-anesthetized rat substantia nigra. We found that bursting DA neurons exhibited non-linear determinism in their firing patterns, whereas non-bursting DA neurons showed truly stochastic firing patterns. Determinism was also detected in the isolated burst and inter-burst interval data extracted from firing patterns of bursting neurons. Moreover, less bursting DA neurons in halothane-anesthetized rats exhibited higher dimensional spiking dynamics than do more bursting DA neurons in chloral hydrate-anesthetized rats. These results strongly indicate that bursting activity is the main source of low-dimensional, non-linear determinism in the firing patterns of DA neurons. This finding furthermore suggests that bursts are the likely carriers of meaningful information in the firing activities of DA neurons. © 2012 The Authors. European Journal of Neuroscience © 2012 Federation of European Neuroscience Societies and Blackwell Publishing Ltd.

Volitional enhancement of firing synchrony and oscillation by neuronal operant conditioning: interaction with neurorehabilitation and brain-machine interface.

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Sakurai, Yoshio; Song, Kichan; Tachibana, Shota; Takahashi, Susumu

2014-01-01

In this review, we focus on neuronal operant conditioning in which increments in neuronal activities are directly rewarded without behaviors. We discuss the potential of this approach to elucidate neuronal plasticity for enhancing specific brain functions and its interaction with the progress in neurorehabilitation and brain-machine interfaces. The key to-be-conditioned activities that this paper emphasizes are synchronous and oscillatory firings of multiple neurons that reflect activities of cell assemblies. First, we introduce certain well-known studies on neuronal operant conditioning in which conditioned enhancements of neuronal firing were reported in animals and humans. These studies demonstrated the feasibility of volitional control over neuronal activity. Second, we refer to the recent studies on operant conditioning of synchrony and oscillation of neuronal activities. In particular, we introduce a recent study showing volitional enhancement of oscillatory activity in monkey motor cortex and our study showing selective enhancement of firing synchrony of neighboring neurons in rat hippocampus. Third, we discuss the reasons for emphasizing firing synchrony and oscillation in neuronal operant conditioning, the main reason being that they reflect the activities of cell assemblies, which have been suggested to be basic neuronal codes representing information in the brain. Finally, we discuss the interaction of neuronal operant conditioning with neurorehabilitation and brain-machine interface (BMI). We argue that synchrony and oscillation of neuronal firing are the key activities required for developing both reliable neurorehabilitation and high-performance BMI. Further, we conclude that research of neuronal operant conditioning, neurorehabilitation, BMI, and system neuroscience will produce findings applicable to these interrelated fields, and neuronal synchrony and oscillation can be a common important bridge among all of them.

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

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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.

Effects of electrical stimulation of ventral septal area on firing rates of pyrogen-treated thermosensitive neurons in preoptic anterior hypothalamus from rabbits.

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Dong, Jun; Xie, Xin-Hua; Lu, Da-Xiang; Fu, Yong-Mei

2007-01-09

Although there is considerable evidence supporting that fever evolved as a host defense response, it is important that the rise in body temperature would not be too high. Many endogenous cryogens or antipyretics that limit the rise in body temperature have been identified. Endogenous antipyretics attenuate fever by influencing the thermoregulatory neurons in the preoptic anterior hypothalamus (POAH) and in adjacent septal areas including ventral septal area (VSA). Our previous study showed that intracerebroventricular (I.C.V.) injection of interleukin-1beta (IL-1beta) affected electrophysiological activities of thermosensitive neurons in VSA regions, and electrical stimulation of POAH reversed the effect of IL-1beta. To further investigate the functional electrophysiological connection between POAH and VSA and its mechanisms in thermoregulation, the firing rates of thermosensitive neurons in POAH of forty-seven unit discharge were recorded by using extracellular microelectrode technique in New Zealand white rabbits. Our results show that the firing rates of the warm-sensitive neurons decreased significantly and those of the cold-sensitive neurons increased in POAH when the pyrogen (IL-1beta) was injected I.C.V. The effects of IL-1beta on firing rates in thermosensitive neurons of POAH were reversed by electrical stimulation of VSA. An arginine vasopressin (AVP) V1 antagonist abolished the regulatory effects of VSA on the firing rates in thermosensitive neurons of POAH evoked by IL-1beta. However, an AVP V2 antagonist had no effects. These data indicated that VSA regulates the activities of the thermosensitive neurons of POAH through AVP V1 but not AVP V2 receptor.

Leaky Integrate and Fire Neuron by Charge-Discharge Dynamics in Floating-Body MOSFET.

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Dutta, Sangya; Kumar, Vinay; Shukla, Aditya; Mohapatra, Nihar R; Ganguly, Udayan

2017-08-15

Neuro-biology inspired Spiking Neural Network (SNN) enables efficient learning and recognition tasks. To achieve a large scale network akin to biology, a power and area efficient electronic neuron is essential. Earlier, we had demonstrated an LIF neuron by a novel 4-terminal impact ionization based n+/p/n+ with an extended gate (gated-INPN) device by physics simulation. Excellent improvement in area and power compared to conventional analog circuit implementations was observed. In this paper, we propose and experimentally demonstrate a compact conventional 3-terminal partially depleted (PD) SOI- MOSFET (100 nm gate length) to replace the 4-terminal gated-INPN device. Impact ionization (II) induced floating body effect in SOI-MOSFET is used to capture LIF neuron behavior to demonstrate spiking frequency dependence on input. MHz operation enables attractive hardware acceleration compared to biology. Overall, conventional PD-SOI-CMOS technology enables very-large-scale-integration (VLSI) which is essential for biology scale (~10 11 neuron based) large neural networks.

Functional Connectome Analysis of Dopamine Neuron Glutamatergic Connections in Forebrain Regions.

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Mingote, Susana; Chuhma, Nao; Kusnoor, Sheila V; Field, Bianca; Deutch, Ariel Y; Rayport, Stephen

2015-12-09

In the ventral tegmental area (VTA), a subpopulation of dopamine neurons express vesicular glutamate transporter 2 and make glutamatergic connections to nucleus accumbens (NAc) and olfactory tubercle (OT) neurons. However, their glutamatergic connections across the forebrain have not been explored systematically. To visualize dopamine neuron forebrain projections and to enable photostimulation of their axons independent of transmitter status, we virally transfected VTA neurons with channelrhodopsin-2 fused to enhanced yellow fluorescent protein (ChR2-EYFP) and used DAT(IREScre) mice to restrict expression to dopamine neurons. ChR2-EYFP-expressing neurons almost invariably stained for tyrosine hydroxylase, identifying them as dopaminergic. Dopamine neuron axons visualized by ChR2-EYFP fluorescence projected most densely to the striatum, moderately to the amygdala and entorhinal cortex (ERC), sparsely to prefrontal and cingulate cortices, and rarely to the hippocampus. Guided by ChR2-EYFP fluorescence, we recorded systematically from putative principal neurons in target areas and determined the incidence and strength of glutamatergic connections by activating all dopamine neuron terminals impinging on recorded neurons with wide-field photostimulation. This revealed strong glutamatergic connections in the NAc, OT, and ERC; moderate strength connections in the central amygdala; and weak connections in the cingulate cortex. No glutamatergic connections were found in the dorsal striatum, hippocampus, basolateral amygdala, or prefrontal cortex. These results indicate that VTA dopamine neurons elicit widespread, but regionally distinct, glutamatergic signals in the forebrain and begin to define the dopamine neuron excitatory functional connectome. Dopamine neurons are important for the control of motivated behavior and are involved in the pathophysiology of several major neuropsychiatric disorders. Recent studies have shown that some ventral midbrain dopamine neurons are

Behaviour of High Strength Steel Endplate Connections in Fire and after Fire

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Qiang, X.

2013-01-01

The aim of this research is to reveal more information and understanding on behaviour and failure mechanisms of high strength steel endplate connections (combining high strength steel endplates with either mild steel or high strength steel beams and columns in endplate connections) in fire and after

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

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Viswanathan Arunachalam

2013-01-01

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

From Structure to Activity: Using Centrality Measures to Predict Neuronal Activity.

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Fletcher, Jack McKay; Wennekers, Thomas

2018-03-01

It is clear that the topological structure of a neural network somehow determines the activity of the neurons within it. In the present work, we ask to what extent it is possible to examine the structural features of a network and learn something about its activity? Specifically, we consider how the centrality (the importance of a node in a network) of a neuron correlates with its firing rate. To investigate, we apply an array of centrality measures, including In-Degree, Closeness, Betweenness, Eigenvector, Katz, PageRank, Hyperlink-Induced Topic Search (HITS) and NeuronRank to Leaky-Integrate and Fire neural networks with different connectivity schemes. We find that Katz centrality is the best predictor of firing rate given the network structure, with almost perfect correlation in all cases studied, which include purely excitatory and excitatory-inhibitory networks, with either homogeneous connections or a small-world structure. We identify the properties of a network which will cause this correlation to hold. We argue that the reason Katz centrality correlates so highly with neuronal activity compared to other centrality measures is because it nicely captures disinhibition in neural networks. In addition, we argue that these theoretical findings are applicable to neuroscientists who apply centrality measures to functional brain networks, as well as offer a neurophysiological justification to high level cognitive models which use certain centrality measures.

Relating neuronal firing patterns to functional differentiation of cerebral cortex.

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Shigeru Shinomoto

2009-07-01

Full Text Available It has been empirically established that the cerebral cortical areas defined by Brodmann one hundred years ago solely on the basis of cellular organization are closely correlated to their function, such as sensation, association, and motion. Cytoarchitectonically distinct cortical areas have different densities and types of neurons. Thus, signaling patterns may also vary among cytoarchitectonically unique cortical areas. To examine how neuronal signaling patterns are related to innate cortical functions, we detected intrinsic features of cortical firing by devising a metric that efficiently isolates non-Poisson irregular characteristics, independent of spike rate fluctuations that are caused extrinsically by ever-changing behavioral conditions. Using the new metric, we analyzed spike trains from over 1,000 neurons in 15 cortical areas sampled by eight independent neurophysiological laboratories. Analysis of firing-pattern dissimilarities across cortical areas revealed a gradient of firing regularity that corresponded closely to the functional category of the cortical area; neuronal spiking patterns are regular in motor areas, random in the visual areas, and bursty in the prefrontal area. Thus, signaling patterns may play an important role in function-specific cerebral cortical computation.

Plasticity of Neuron-Glial Transmission: Equipping Glia for Long-Term Integration of Network Activity

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Wayne Croft

2015-01-01

Full Text Available The capacity of synaptic networks to express activity-dependent changes in strength and connectivity is essential for learning and memory processes. In recent years, glial cells (most notably astrocytes have been recognized as active participants in the modulation of synaptic transmission and synaptic plasticity, implicating these electrically nonexcitable cells in information processing in the brain. While the concept of bidirectional communication between neurons and glia and the mechanisms by which gliotransmission can modulate neuronal function are well established, less attention has been focussed on the computational potential of neuron-glial transmission itself. In particular, whether neuron-glial transmission is itself subject to activity-dependent plasticity and what the computational properties of such plasticity might be has not been explored in detail. In this review, we summarize current examples of plasticity in neuron-glial transmission, in many brain regions and neurotransmitter pathways. We argue that induction of glial plasticity typically requires repetitive neuronal firing over long time periods (minutes-hours rather than the short-lived, stereotyped trigger typical of canonical long-term potentiation. We speculate that this equips glia with a mechanism for monitoring average firing rates in the synaptic network, which is suited to the longer term roles proposed for astrocytes in neurophysiology.

Neurons the decision makers, Part I: The firing function of a single neuron.

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Saaty, Thomas

2017-02-01

This paper is concerned with understanding synthesis of electric signals in the neural system based on making pairwise comparisons. Fundamentally, every person and every animal are born with the talent to compare stimuli from things that share properties in space or over time. Comparisons always need experience to distinguish among things. Pairwise comparisons are numerically reciprocal. If a value is assigned to the larger of two elements that have a given property when compared with the smaller one, then the smaller has the reciprocal of that value when compared with the larger. Because making comparisons requires the reciprocal property, we need mathematics that can cope with division. There are four division algebras that would allow us to use our reciprocals arising from comparisons: The real numbers, the complex numbers, the non-commutative quaternions and the non-associative octonions. Rather than inferring function as from electric flow in a network, in this paper we infer the flow from function. Neurons fire in response to stimuli and their firings vary relative to the intensities of the stimuli. We believe neurons use some kind of pairwise comparison mechanism to determine when to fire based on the stimuli they receive. The ideas we develop here about flows are used to deduce how a system based on this kind of firing determination works and can be described. Furthermore the firing of neurons requires continuous comparisons. To develop a formula describing the output of these pairwise comparisons requires solving Fredholm's equation of the second kind which is satisfied if and only if a simple functional equation has solutions. The Fourier transform of the real solution of this equation leads to inverse square laws like those that are common in physics. The Fourier transform applied to a complex valued solution leads to Dirac type of firings. Such firings are dense in the very general fields of functions known as Sobolev spaces and thus can be used to

Metabolism regulates the spontaneous firing of substantia nigra pars reticulata neurons via KATP and nonselective cation channels.

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Lutas, Andrew; Birnbaumer, Lutz; Yellen, Gary

2014-12-03

Neurons use glucose to fuel glycolysis and provide substrates for mitochondrial respiration, but neurons can also use alternative fuels that bypass glycolysis and feed directly into mitochondria. To determine whether neuronal pacemaking depends on active glucose metabolism, we switched the metabolic fuel from glucose to alternative fuels, lactate or β-hydroxybutyrate, while monitoring the spontaneous firing of GABAergic neurons in mouse substantia nigra pars reticulata (SNr) brain slices. We found that alternative fuels, in the absence of glucose, sustained SNr spontaneous firing at basal rates, but glycolysis may still be supported by glycogen in the absence of glucose. To prevent any glycogen-fueled glycolysis, we directly inhibited glycolysis using either 2-deoxyglucose or iodoacetic acid. Inhibiting glycolysis in the presence of alternative fuels lowered SNr firing to a slower sustained firing rate. Surprisingly, we found that the decrease in SNr firing was not mediated by ATP-sensitive potassium (KATP) channel activity, but if we lowered the perfusion flow rate or omitted the alternative fuel, KATP channels were activated and could silence SNr firing. The KATP-independent slowing of SNr firing that occurred with glycolytic inhibition in the presence of alternative fuels was consistent with a decrease in a nonselective cationic conductance. Although mitochondrial metabolism alone can prevent severe energy deprivation and KATP channel activation in SNr neurons, active glucose metabolism appears important for keeping open a class of ion channels that is crucial for the high spontaneous firing rate of SNr neurons. Copyright © 2014 the authors 0270-6474/14/3416336-12$15.00/0.

Detecting Functional Connectivity During Audiovisual Integration with MEG: A Comparison of Connectivity Metrics.

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Ard, Tyler; Carver, Frederick W; Holroyd, Tom; Horwitz, Barry; Coppola, Richard

2015-08-01

In typical magnetoencephalography and/or electroencephalography functional connectivity analysis, researchers select one of several methods that measure a relationship between regions to determine connectivity, such as coherence, power correlations, and others. However, it is largely unknown if some are more suited than others for various types of investigations. In this study, the authors investigate seven connectivity metrics to evaluate which, if any, are sensitive to audiovisual integration by contrasting connectivity when tracking an audiovisual object versus connectivity when tracking a visual object uncorrelated with the auditory stimulus. The authors are able to assess the metrics' performances at detecting audiovisual integration by investigating connectivity between auditory and visual areas. Critically, the authors perform their investigation on a whole-cortex all-to-all mapping, avoiding confounds introduced in seed selection. The authors find that amplitude-based connectivity measures in the beta band detect strong connections between visual and auditory areas during audiovisual integration, specifically between V4/V5 and auditory cortices in the right hemisphere. Conversely, phase-based connectivity measures in the beta band as well as phase and power measures in alpha, gamma, and theta do not show connectivity between audiovisual areas. The authors postulate that while beta power correlations detect audiovisual integration in the current experimental context, it may not always be the best measure to detect connectivity. Instead, it is likely that the brain utilizes a variety of mechanisms in neuronal communication that may produce differential types of temporal relationships.

Network-Based Real-time Integrated Fire Detection and Alarm (FDA) System with Building Automation

Science.gov (United States)

Anwar, F.; Boby, R. I.; Rashid, M. M.; Alam, M. M.; Shaikh, Z.

2017-11-01

Fire alarm systems have become increasingly an important lifesaving technology in many aspects, such as applications to detect, monitor and control any fire hazard. A large sum of money is being spent annually to install and maintain the fire alarm systems in buildings to protect property and lives from the unexpected spread of fire. Several methods are already developed and it is improving on a daily basis to reduce the cost as well as increase quality. An integrated Fire Detection and Alarm (FDA) systems with building automation was studied, to reduce cost and improve their reliability by preventing false alarm. This work proposes an improved framework for FDA system to ensure a robust intelligent network of FDA control panels in real-time. A shortest path algorithmic was chosen for series of buildings connected by fiber optic network. The framework shares information and communicates with each fire alarm panels connected in peer to peer configuration and declare the network state using network address declaration from any building connected in network. The fiber-optic connection was proposed to reduce signal noises, thus increasing large area coverage, real-time communication and long-term safety. Based on this proposed method an experimental setup was designed and a prototype system was developed to validate the performance in practice. Also, the distributed network system was proposed to connect with an optional remote monitoring terminal panel to validate proposed network performance and ensure fire survivability where the information is sequentially transmitted. The proposed FDA system is different from traditional fire alarm and detection system in terms of topology as it manages group of buildings in an optimal and efficient manner.Introduction

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

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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.

Kv2 Channel Regulation of Action Potential Repolarization and Firing Patterns in Superior Cervical Ganglion Neurons and Hippocampal CA1 Pyramidal Neurons

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Liu, Pin W.

2014-01-01

Kv2 family “delayed-rectifier” potassium channels are widely expressed in mammalian neurons. Kv2 channels activate relatively slowly and their contribution to action potential repolarization under physiological conditions has been unclear. We explored the function of Kv2 channels using a Kv2-selective blocker, Guangxitoxin-1E (GxTX-1E). Using acutely isolated neurons, mixed voltage-clamp and current-clamp experiments were done at 37°C to study the physiological kinetics of channel gating and action potentials. In both rat superior cervical ganglion (SCG) neurons and mouse hippocampal CA1 pyramidal neurons, 100 nm GxTX-1E produced near-saturating block of a component of current typically constituting ∼60–80% of the total delayed-rectifier current. GxTX-1E also reduced A-type potassium current (IA), but much more weakly. In SCG neurons, 100 nm GxTX-1E broadened spikes and voltage clamp experiments using action potential waveforms showed that Kv2 channels carry ∼55% of the total outward current during action potential repolarization despite activating relatively late in the spike. In CA1 neurons, 100 nm GxTX-1E broadened spikes evoked from −70 mV, but not −80 mV, likely reflecting a greater role of Kv2 when other potassium channels were partially inactivated at −70 mV. In both CA1 and SCG neurons, inhibition of Kv2 channels produced dramatic depolarization of interspike voltages during repetitive firing. In CA1 neurons and some SCG neurons, this was associated with increased initial firing frequency. In all neurons, inhibition of Kv2 channels depressed maintained firing because neurons entered depolarization block more readily. Therefore, Kv2 channels can either decrease or increase neuronal excitability depending on the time scale of excitation. PMID:24695716

The dynamics of integrate-and-fire: mean versus variance modulations and dependence on baseline parameters.

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Pressley, Joanna; Troyer, Todd W

2011-05-01

The leaky integrate-and-fire (LIF) is the simplest neuron model that captures the essential properties of neuronal signaling. Yet common intuitions are inadequate to explain basic properties of LIF responses to sinusoidal modulations of the input. Here we examine responses to low and moderate frequency modulations of both the mean and variance of the input current and quantify how these responses depend on baseline parameters. Across parameters, responses to modulations in the mean current are low pass, approaching zero in the limit of high frequencies. For very low baseline firing rates, the response cutoff frequency matches that expected from membrane integration. However, the cutoff shows a rapid, supralinear increase with firing rate, with a steeper increase in the case of lower noise. For modulations of the input variance, the gain at high frequency remains finite. Here, we show that the low-frequency responses depend strongly on baseline parameters and derive an analytic condition specifying the parameters at which responses switch from being dominated by low versus high frequencies. Additionally, we show that the resonant responses for variance modulations have properties not expected for common oscillatory resonances: they peak at frequencies higher than the baseline firing rate and persist when oscillatory spiking is disrupted by high noise. Finally, the responses to mean and variance modulations are shown to have a complementary dependence on baseline parameters at higher frequencies, resulting in responses to modulations of Poisson input rates that are independent of baseline input statistics.

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

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

2015-10-01

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

Computing the Local Field Potential (LFP) from Integrate-and-Fire Network Models

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Cuntz, Hermann; Lansner, Anders; Panzeri, Stefano; Einevoll, Gaute T.

2015-01-01

Leaky integrate-and-fire (LIF) network models are commonly used to study how the spiking dynamics of neural networks changes with stimuli, tasks or dynamic network states. However, neurophysiological studies in vivo often rather measure the mass activity of neuronal microcircuits with the local field potential (LFP). Given that LFPs are generated by spatially separated currents across the neuronal membrane, they cannot be computed directly from quantities defined in models of point-like LIF neurons. Here, we explore the best approximation for predicting the LFP based on standard output from point-neuron LIF networks. To search for this best “LFP proxy”, we compared LFP predictions from candidate proxies based on LIF network output (e.g, firing rates, membrane potentials, synaptic currents) with “ground-truth” LFP obtained when the LIF network synaptic input currents were injected into an analogous three-dimensional (3D) network model of multi-compartmental neurons with realistic morphology, spatial distributions of somata and synapses. We found that a specific fixed linear combination of the LIF synaptic currents provided an accurate LFP proxy, accounting for most of the variance of the LFP time course observed in the 3D network for all recording locations. This proxy performed well over a broad set of conditions, including substantial variations of the neuronal morphologies. Our results provide a simple formula for estimating the time course of the LFP from LIF network simulations in cases where a single pyramidal population dominates the LFP generation, and thereby facilitate quantitative comparison between computational models and experimental LFP recordings in vivo. PMID:26657024

Computing the Local Field Potential (LFP from Integrate-and-Fire Network Models.

Directory of Open Access Journals (Sweden)

Alberto Mazzoni

2015-12-01

Full Text Available Leaky integrate-and-fire (LIF network models are commonly used to study how the spiking dynamics of neural networks changes with stimuli, tasks or dynamic network states. However, neurophysiological studies in vivo often rather measure the mass activity of neuronal microcircuits with the local field potential (LFP. Given that LFPs are generated by spatially separated currents across the neuronal membrane, they cannot be computed directly from quantities defined in models of point-like LIF neurons. Here, we explore the best approximation for predicting the LFP based on standard output from point-neuron LIF networks. To search for this best "LFP proxy", we compared LFP predictions from candidate proxies based on LIF network output (e.g, firing rates, membrane potentials, synaptic currents with "ground-truth" LFP obtained when the LIF network synaptic input currents were injected into an analogous three-dimensional (3D network model of multi-compartmental neurons with realistic morphology, spatial distributions of somata and synapses. We found that a specific fixed linear combination of the LIF synaptic currents provided an accurate LFP proxy, accounting for most of the variance of the LFP time course observed in the 3D network for all recording locations. This proxy performed well over a broad set of conditions, including substantial variations of the neuronal morphologies. Our results provide a simple formula for estimating the time course of the LFP from LIF network simulations in cases where a single pyramidal population dominates the LFP generation, and thereby facilitate quantitative comparison between computational models and experimental LFP recordings in vivo.

Computing the Local Field Potential (LFP) from Integrate-and-Fire Network Models

DEFF Research Database (Denmark)

Mazzoni, Alberto; Linden, Henrik; Cuntz, Hermann

2015-01-01

Leaky integrate-and-fire (LIF) network models are commonly used to study how the spiking dynamics of neural networks changes with stimuli, tasks or dynamic network states. However, neurophysiological studies in vivo often rather measure the mass activity of neuronal microcircuits with the local f...... in cases where a single pyramidal population dominates the LFP generation, and thereby facilitate quantitative comparison between computational models and experimental LFP recordings in vivo....

Modeling task-specific neuronal ensembles improves decoding of grasp

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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

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

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Kottas, Athanasios; Behseta, Sam

2010-03-01

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

TETRAMETHRIN AND DDT INHIBIT SPONTANEOUS FIRING IN CORTICAL NEURONAL NETWORKS

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The insecticidal and neurotoxic effects of pyrethroids result from prolonged sodium channel inactivation, which causes alterations in neuronal firing and communication. Previously, we determined the relative potencies of 11 type I and type II pyrethroid insecticides using microel...

Interspike interval correlation in a stochastic exponential integrate-and-fire model with subthreshold and spike-triggered adaptation.

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Shiau, LieJune; Schwalger, Tilo; Lindner, Benjamin

2015-06-01

We study the spike statistics of an adaptive exponential integrate-and-fire neuron stimulated by white Gaussian current noise. We derive analytical approximations for the coefficient of variation and the serial correlation coefficient of the interspike interval assuming that the neuron operates in the mean-driven tonic firing regime and that the stochastic input is weak. Our result for the serial correlation coefficient has the form of a geometric sequence and is confirmed by the comparison to numerical simulations. The theory predicts various patterns of interval correlations (positive or negative at lag one, monotonically decreasing or oscillating) depending on the strength of the spike-triggered and subthreshold components of the adaptation current. In particular, for pure subthreshold adaptation we find strong positive ISI correlations that are usually ascribed to positive correlations in the input current. Our results i) provide an alternative explanation for interspike-interval correlations observed in vivo, ii) may be useful in fitting point neuron models to experimental data, and iii) may be instrumental in exploring the role of adaptation currents for signal detection and signal transmission in single neurons.

NeuroLines: A Subway Map Metaphor for Visualizing Nanoscale Neuronal Connectivity.

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Al-Awami, Ali K; Beyer, Johanna; Strobelt, Hendrik; Kasthuri, Narayanan; Lichtman, Jeff W; Pfister, Hanspeter; Hadwiger, Markus

2014-12-01

We present NeuroLines, a novel visualization technique designed for scalable detailed analysis of neuronal connectivity at the nanoscale level. The topology of 3D brain tissue data is abstracted into a multi-scale, relative distance-preserving subway map visualization that allows domain scientists to conduct an interactive analysis of neurons and their connectivity. Nanoscale connectomics aims at reverse-engineering the wiring of the brain. Reconstructing and analyzing the detailed connectivity of neurons and neurites (axons, dendrites) will be crucial for understanding the brain and its development and diseases. However, the enormous scale and complexity of nanoscale neuronal connectivity pose big challenges to existing visualization techniques in terms of scalability. NeuroLines offers a scalable visualization framework that can interactively render thousands of neurites, and that supports the detailed analysis of neuronal structures and their connectivity. We describe and analyze the design of NeuroLines based on two real-world use-cases of our collaborators in developmental neuroscience, and investigate its scalability to large-scale neuronal connectivity data.

NeuroLines: A Subway Map Metaphor for Visualizing Nanoscale Neuronal Connectivity

KAUST Repository

Al-Awami, Ali K.; Beyer, Johanna; Strobelt, Hendrik; Kasthuri, Narayanan; Lichtman, Jeff W.; Pfister, Hanspeter; Hadwiger, Markus

2014-01-01

We present NeuroLines, a novel visualization technique designed for scalable detailed analysis of neuronal connectivity at the nanoscale level. The topology of 3D brain tissue data is abstracted into a multi-scale, relative distance-preserving subway map visualization that allows domain scientists to conduct an interactive analysis of neurons and their connectivity. Nanoscale connectomics aims at reverse-engineering the wiring of the brain. Reconstructing and analyzing the detailed connectivity of neurons and neurites (axons, dendrites) will be crucial for understanding the brain and its development and diseases. However, the enormous scale and complexity of nanoscale neuronal connectivity pose big challenges to existing visualization techniques in terms of scalability. NeuroLines offers a scalable visualization framework that can interactively render thousands of neurites, and that supports the detailed analysis of neuronal structures and their connectivity. We describe and analyze the design of NeuroLines based on two real-world use-cases of our collaborators in developmental neuroscience, and investigate its scalability to large-scale neuronal connectivity data.

NeuroLines: A Subway Map Metaphor for Visualizing Nanoscale Neuronal Connectivity

KAUST Repository

Al-Awami, Ali K.

2014-12-31

We present NeuroLines, a novel visualization technique designed for scalable detailed analysis of neuronal connectivity at the nanoscale level. The topology of 3D brain tissue data is abstracted into a multi-scale, relative distance-preserving subway map visualization that allows domain scientists to conduct an interactive analysis of neurons and their connectivity. Nanoscale connectomics aims at reverse-engineering the wiring of the brain. Reconstructing and analyzing the detailed connectivity of neurons and neurites (axons, dendrites) will be crucial for understanding the brain and its development and diseases. However, the enormous scale and complexity of nanoscale neuronal connectivity pose big challenges to existing visualization techniques in terms of scalability. NeuroLines offers a scalable visualization framework that can interactively render thousands of neurites, and that supports the detailed analysis of neuronal structures and their connectivity. We describe and analyze the design of NeuroLines based on two real-world use-cases of our collaborators in developmental neuroscience, and investigate its scalability to large-scale neuronal connectivity data.

Intracellular Methamphetamine Prevents the Dopamine-induced Enhancement of Neuronal Firing*

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Saha, Kaustuv; Sambo, Danielle; Richardson, Ben D.; Lin, Landon M.; Butler, Brittany; Villarroel, Laura; Khoshbouei, Habibeh

2014-01-01

The dysregulation of the dopaminergic system is implicated in multiple neurological and neuropsychiatric disorders such as Parkinson disease and drug addiction. The primary target of psychostimulants such as amphetamine and methamphetamine is the dopamine transporter (DAT), the major regulator of extracellular dopamine levels in the brain. However, the behavioral and neurophysiological correlates of methamphetamine and amphetamine administration are unique from one another, thereby suggesting these two compounds impact dopaminergic neurotransmission differentially. We further examined the unique mechanisms by which amphetamine and methamphetamine regulate DAT function and dopamine neurotransmission; in the present study we examined the impact of extracellular and intracellular amphetamine and methamphetamine on the spontaneous firing of cultured midbrain dopaminergic neurons and isolated DAT-mediated current. In dopaminergic neurons the spontaneous firing rate was enhanced by extracellular application of amphetamine > dopamine > methamphetamine and was DAT-dependent. Amphetamine > methamphetamine similarly enhanced DAT-mediated inward current, which was sensitive to isosmotic substitution of Na+ or Cl− ion. Although isosmotic substitution of extracellular Na+ ions blocked amphetamine and methamphetamine-induced DAT-mediated inward current similarly, the removal of extracellular Cl− ions preferentially blocked amphetamine-induced inward current. The intracellular application of methamphetamine, but not amphetamine, prevented the dopamine-induced increase in the spontaneous firing of dopaminergic neurons and the corresponding DAT-mediated inward current. The results reveal a new mechanism for methamphetamine-induced dysregulation of dopaminergic neurons. PMID:24962577

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

Robust emergence of small-world structure in networks of spiking neurons.

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Kwok, Hoi Fei; Jurica, Peter; Raffone, Antonino; van Leeuwen, Cees

2007-03-01

Spontaneous activity in biological neural networks shows patterns of dynamic synchronization. We propose that these patterns support the formation of a small-world structure-network connectivity optimal for distributed information processing. We present numerical simulations with connected Hindmarsh-Rose neurons in which, starting from random connection distributions, small-world networks evolve as a result of applying an adaptive rewiring rule. The rule connects pairs of neurons that tend fire in synchrony, and disconnects ones that fail to synchronize. Repeated application of the rule leads to small-world structures. This mechanism is robustly observed for bursting and irregular firing regimes.

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

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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.

Excitability of jcBNST neurons is reduced in alcohol-dependent animals during protracted alcohol withdrawal.

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Attila Szücs

Full Text Available Alcohol dependence and withdrawal has been shown to cause neuroadaptive changes at multiple levels of the nervous system. At the neuron level, adaptations of synaptic connections have been extensively studied in a number of brain areas and accumulating evidence also shows the importance of alcohol dependence-related changes in the intrinsic cellular properties of neurons. At the same time, it is still largely unknown how such neural adaptations impact the firing and integrative properties of neurons. To address these problems, here, we analyze physiological properties of neurons in the bed nucleus of stria terminalis (jcBNST in animals with a history of alcohol dependence. As a comprehensive approach, first we measure passive and active membrane properties of neurons using conventional current clamp protocols and then analyze their firing responses under the action of simulated synaptic bombardment via dynamic clamp. We find that most physiological properties as measured by DC current injection are barely affected during protracted withdrawal. However, neuronal excitability as measured from firing responses under simulated synaptic inputs with the dynamic clamp is markedly reduced in all 3 types of jcBNST neurons. These results support the importance of studying the effects of alcohol and drugs of abuse on the firing properties of neurons with dynamic clamp protocols designed to bring the neurons into a high conductance state. Since the jcBNST integrates excitatory inputs from the basolateral amygdala (BLA and cortical inputs from the infralimbic and the insular cortices and in turn is believed to contribute to the inhibitory input to the central nucleus of the amygdala (CeA the reduced excitability of the jcBNST during protracted withdrawal in alcohol-dependent animals will likely affect ability of the jcBNST to shape the activity and output of the CeA.

Dynamics of a structured neuron population

International Nuclear Information System (INIS)

Pakdaman, Khashayar; Salort, Delphine; Perthame, Benoît

2010-01-01

We study the dynamics of assemblies of interacting neurons. For large fully connected networks, the dynamics of the system can be described by a partial differential equation reminiscent of age-structure models used in mathematical ecology, where the 'age' of a neuron represents the time elapsed since its last discharge. The nonlinearity arises from the connectivity J of the network. We prove some mathematical properties of the model that are directly related to qualitative properties. On the one hand, we prove that it is well-posed and that it admits stationary states which, depending upon the connectivity, can be unique or not. On the other hand, we study the long time behaviour of solutions; both for small and large J, we prove the relaxation to the steady state describing asynchronous firing of the neurons. In the middle range, numerical experiments show that periodic solutions appear expressing re-synchronization of the network and asynchronous firing

Fluctuations and information filtering in coupled populations of spiking neurons with adaptation.

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Deger, Moritz; Schwalger, Tilo; Naud, Richard; Gerstner, Wulfram

2014-12-01

Finite-sized populations of spiking elements are fundamental to brain function but also are used in many areas of physics. Here we present a theory of the dynamics of finite-sized populations of spiking units, based on a quasirenewal description of neurons with adaptation. We derive an integral equation with colored noise that governs the stochastic dynamics of the population activity in response to time-dependent stimulation and calculate the spectral density in the asynchronous state. We show that systems of coupled populations with adaptation can generate a frequency band in which sensory information is preferentially encoded. The theory is applicable to fully as well as randomly connected networks and to leaky integrate-and-fire as well as to generalized spiking neurons with adaptation on multiple time scales.

Unmasking of spiral ganglion neuron firing dynamics by membrane potential and neurotrophin-3.

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Crozier, Robert A; Davis, Robin L

2014-07-16

Type I spiral ganglion neurons have a unique role relative to other sensory afferents because, as a single population, they must convey the richness, complexity, and precision of auditory information as they shape signals transmitted to the brain. To understand better the sophistication of spiral ganglion response properties, we compared somatic whole-cell current-clamp recordings from basal and apical neurons obtained during the first 2 postnatal weeks from CBA/CaJ mice. We found that during this developmental time period neuron response properties changed from uniformly excitable to differentially plastic. Low-frequency, apical and high-frequency basal neurons at postnatal day 1 (P1)-P3 were predominantly slowly accommodating (SA), firing at low thresholds with little alteration in accommodation response mode induced by changes in resting membrane potential (RMP) or added neurotrophin-3 (NT-3). In contrast, P10-P14 apical and basal neurons were predominately rapidly accommodating (RA), had higher firing thresholds, and responded to elevation of RMP and added NT-3 by transitioning to the SA category without affecting the instantaneous firing rate. Therefore, older neurons appeared to be uniformly less excitable under baseline conditions yet displayed a previously unrecognized capacity to change response modes dynamically within a remarkably stable accommodation framework. Because the soma is interposed in the signal conduction pathway, these specializations can potentially lead to shaping and filtering of the transmitted signal. These results suggest that spiral ganglion neurons possess electrophysiological mechanisms that enable them to adapt their response properties to the characteristics of incoming stimuli and thus have the capacity to encode a wide spectrum of auditory information. Copyright © 2014 the authors 0270-6474/14/349688-15$15.00/0.

A hidden Markov model approach to neuron firing patterns.

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

1996-11-01

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

A signaling network for patterning of neuronal connectivity in the Drosophila brain.

Directory of Open Access Journals (Sweden)

Mohammed Srahna

2006-10-01

Full Text Available The precise number and pattern of axonal connections generated during brain development regulates animal behavior. Therefore, understanding how developmental signals interact to regulate axonal extension and retraction to achieve precise neuronal connectivity is a fundamental goal of neurobiology. We investigated this question in the developing adult brain of Drosophila and find that it is regulated by crosstalk between Wnt, fibroblast growth factor (FGF receptor, and Jun N-terminal kinase (JNK signaling, but independent of neuronal activity. The Rac1 GTPase integrates a Wnt-Frizzled-Disheveled axon-stabilizing signal and a Branchless (FGF-Breathless (FGF receptor axon-retracting signal to modulate JNK activity. JNK activity is necessary and sufficient for axon extension, whereas the antagonistic Wnt and FGF signals act to balance the extension and retraction required for the generation of the precise wiring pattern.

Mechanisms of Gain Control by Voltage-Gated Channels in Intrinsically-Firing Neurons

Science.gov (United States)

Patel, Ameera X.; Burdakov, Denis

2015-01-01

Gain modulation is a key feature of neural information processing, but underlying mechanisms remain unclear. In single neurons, gain can be measured as the slope of the current-frequency (input-output) relationship over any given range of inputs. While much work has focused on the control of basal firing rates and spike rate adaptation, gain control has been relatively unstudied. Of the limited studies on gain control, some have examined the roles of synaptic noise and passive somatic currents, but the roles of voltage-gated channels present ubiquitously in neurons have been less explored. Here, we systematically examined the relationship between gain and voltage-gated ion channels in a conductance-based, tonically-active, model neuron. Changes in expression (conductance density) of voltage-gated channels increased (Ca2+ channel), reduced (K+ channels), or produced little effect (h-type channel) on gain. We found that the gain-controlling ability of channels increased exponentially with the steepness of their activation within the dynamic voltage window (voltage range associated with firing). For depolarization-activated channels, this produced a greater channel current per action potential at higher firing rates. This allowed these channels to modulate gain by contributing to firing preferentially at states of higher excitation. A finer analysis of the current-voltage relationship during tonic firing identified narrow voltage windows at which the gain-modulating channels exerted their effects. As a proof of concept, we show that h-type channels can be tuned to modulate gain by changing the steepness of their activation within the dynamic voltage window. These results show how the impact of an ion channel on gain can be predicted from the relationship between channel kinetics and the membrane potential during firing. This is potentially relevant to understanding input-output scaling in a wide class of neurons found throughout the brain and other nervous systems

Neuronal firing in the globus pallidus internus and the ventrolateral thalamus related to parkinsonian motor symptoms

Institute of Scientific and Technical Information of China (English)

CHEN Hai; ZHUANG Ping; ZHANG Yu-qing; LI Jian-yu; LI Yong-jie

2009-01-01

Background It has been proposed that parkinsonian motor signs result from hyperactivity in the output nucleus of the basal ganglia, which suppress the motor thalamus and cortical areas. This study aimed to explore the neuronal activity in the globus pallidus internus (GPi) and the ventrolateral thalamic nuclear group (ventral oral posterior/ventral intermediate, Vop/Vim) in patients with Parkinson's disease (PD).Methods Twenty patients with PD who underwent neurosurgery were studied. Microelectrode recording was performed in the GPi (n=10) and the Vop/Vim (n=10) intraoperatively. Electromyography (EMG) contralateral to the surgery was simultaneously performed. Single unit analysis was carried out. The interspike intervals (ISI) and coefficient of variation (CV) of ISI were calculated. Histograms of ISI were constructed. A unified Parkinson's disease rating scale (UPDRS) was used to assess the clinical outcome of surgery.Results Three hundred and sixty-three neurons were obtained from 20 trajectories. Of 175 GPi neurons, there were 15.4% with tremor frequency, 69.2% with tonic firing, and 15.4% with irregular discharge. Of 188 thalamic neurons, there were 46.8% with tremor frequency, 22.9% with tonic firing, and 30.3% with irregular discharge. The numbers of three patterns of neuron in GPi and Vop/Vim were significantly different (P <0.001). ISI analysis revealed that mean firing rate of the three patterns of GPi neurons was (80.9±63.9) Hz (n=78), which was higher than similar neurons with 62.9 Hz in a normal primate. For the Vop/Vim group, ISI revealed that mean firing rate of the three patterns of neurons (n=95) was (23.2±17.1) Hz which was lower than similar neurons with 30 Hz in the motor thalamus of normal primates. UPDRS indicated that the clinical outcome of pallidotomy was (64.3±9.5)%, (83.4±19.1)% and (63.4±36.3)%, and clinical outcome of thalamotomy was (92.2±12.9)%, (68.0±25.2)% and (44.3±27.2)% for tremor, rigidity and bradykinesia, respectively

The equilibrium of neural firing: A mathematical theory

Energy Technology Data Exchange (ETDEWEB)

Lan, Sizhong, E-mail: lsz@fuyunresearch.org [Fuyun Research, Beijing, 100055 (China)

2014-12-15

Inspired by statistical thermodynamics, we presume that neuron system has equilibrium condition with respect to neural firing. We show that, even with dynamically changeable neural connections, it is inevitable for neural firing to evolve to equilibrium. To study the dynamics between neural firing and neural connections, we propose an extended communication system where noisy channel has the tendency towards fixed point, implying that neural connections are always attracted into fixed points such that equilibrium can be reached. The extended communication system and its mathematics could be useful back in thermodynamics.

Connection Temperatures during the Mokrsko Fire Test

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J. Chlouba

2009-01-01

Full Text Available The Mokrsko fire test focused on the overall behaviour of the structure, which cannot be observed on the separate elements, and also on the temperature of connections with improved fire resistance. During the test, measurements were made of the temperature of the gas and of the elements, the overall and relative deformations, gas pressure, humidity, the radiation of the compartment to structural element and the external steel column, transport of the moisture through the walls, and also the climatic conditions. The results of the test show the differences between the behaviour of the element and the behaviour of the structure exposed to high temperatures during a fire. The collapse of the composite slab was reached. The results of the numerical simulations using the SAFIR program compared well with the measured temperature values in the structure and also in the connections. 

Effects of partial time delays on phase synchronization in Watts-Strogatz small-world neuronal networks

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Sun, Xiaojuan; Perc, Matjaž; Kurths, Jürgen

2017-05-01

In this paper, we study effects of partial time delays on phase synchronization in Watts-Strogatz small-world neuronal networks. Our focus is on the impact of two parameters, namely the time delay τ and the probability of partial time delay pdelay, whereby the latter determines the probability with which a connection between two neurons is delayed. Our research reveals that partial time delays significantly affect phase synchronization in this system. In particular, partial time delays can either enhance or decrease phase synchronization and induce synchronization transitions with changes in the mean firing rate of neurons, as well as induce switching between synchronized neurons with period-1 firing to synchronized neurons with period-2 firing. Moreover, in comparison to a neuronal network where all connections are delayed, we show that small partial time delay probabilities have especially different influences on phase synchronization of neuronal networks.

Engineering connectivity by multiscale micropatterning of individual populations of neurons.

Science.gov (United States)

Albers, Jonas; Toma, Koji; Offenhäusser, Andreas

2015-02-01

Functional networks are the basis of information processing in the central nervous system. Essential for their formation are guided neuronal growth as well as controlled connectivity and information flow. The basis of neuronal development is generated by guiding cues and geometric constraints. To investigate the neuronal growth and connectivity of adjacent neuronal networks, two-dimensional protein patterns were created. A mixture of poly-L-lysine and laminin was transferred onto a silanized glass surface by microcontact printing. The structures were populated with dissociated primary cortical embryonic rat neurons. Triangular structures with diverse opening angles, height, and design were chosen as two-dimensional structures to allow network formation with constricted gateways. Neuronal development was observed by immunohistochemistry to pursue the influence of the chosen structures on the neuronal outgrowth. Neurons were stained for MAP2, while poly-L-lysine was FITC labeled. With this study we present an easy-to-use technique to engineer two-dimensional networks in vitro with defined gateways. The presented micropatterning method is used to generate daisy-chained neuronal networks with predefined connectivity. Signal propagation among geometrically constrained networks can easily be monitored by calcium-sensitive dyes, providing insights into network communication in vitro. Copyright © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Barbed channels enhance unidirectional connectivity between neuronal networks cultured on multi electrode arrays.

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Joost eLe Feber

2015-11-01

Full Text Available Cultured neurons on multi electrode arrays (MEAs have been widely used to study various as-pects of neuronal (network functioning. A possible drawback of this approach is the lack of structure in these networks. At the single cell level, several solutions have been proposed to ena-ble directed connectivity, and promising results were obtained. At the level of connected sub-populations, a few attempts have been made with promising results. First assessment of the de-signs’ functionality, however, suggested room for further improvement.We designed a two chamber MEA aiming to create a unidirectional connection between the net-works in both chambers (‘emitting’ and ‘receiving’. To achieve this unidirectionality, all inter-connecting channels contained barbs that hindered axon growth in the opposite direction (from receiving to emitting chamber. Visual inspection showed that axons predominantly grew through the channels in the promoted direction . This observation was confirmed by spontaneous activity recordings. Cross-correlation between the signals from two electrodes inside the channels suggested signal propagation at ≈2 m/s from emitting to receiving chamber. Cross-correlation between the firing patterns in both chambers indicated that most correlated activity was initiated in the emitting chamber, which was also reflected by a significantly lower fraction of partial bursts (e. a one-chamber-only burst in the emitting chamber. Finally, electrical stimulation in the emitting chamber induced a fast response in that chamber, and a slower response in the receiving chamber. Stimulation in the receiving chamber evoked a fast response in that chamber, but no response in the emitting chamber. These results confirm the predominantly unidirectional nature of the connecting channels from emitting to receiving chamber.

Respiratory Neuron Activity in the Mesencephalon, Diencephalon and Cerebellum of the Carp

NARCIS (Netherlands)

Ballintijn, C.M.; Luiten, P.G.M.; Jüch, P.J.W.

1979-01-01

The functional properties, localization and connections of neurons with a respiratory-rhythmic firing pattern in the mesencephalon, diencephalon and cerebellum of the carp were studied. Some neurons acquire respiratory rhythm only as a side effect of respiration via sensory stimulation by movements

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.

Just-in-time connectivity for large spiking networks.

Science.gov (United States)

Lytton, William W; Omurtag, Ahmet; Neymotin, Samuel A; Hines, Michael L

2008-11-01

The scale of large neuronal network simulations is memory limited due to the need to store connectivity information: connectivity storage grows as the square of neuron number up to anatomically relevant limits. Using the NEURON simulator as a discrete-event simulator (no integration), we explored the consequences of avoiding the space costs of connectivity through regenerating connectivity parameters when needed: just in time after a presynaptic cell fires. We explored various strategies for automated generation of one or more of the basic static connectivity parameters: delays, postsynaptic cell identities, and weights, as well as run-time connectivity state: the event queue. Comparison of the JitCon implementation to NEURON's standard NetCon connectivity method showed substantial space savings, with associated run-time penalty. Although JitCon saved space by eliminating connectivity parameters, larger simulations were still memory limited due to growth of the synaptic event queue. We therefore designed a JitEvent algorithm that added items to the queue only when required: instead of alerting multiple postsynaptic cells, a spiking presynaptic cell posted a callback event at the shortest synaptic delay time. At the time of the callback, this same presynaptic cell directly notified the first postsynaptic cell and generated another self-callback for the next delay time. The JitEvent implementation yielded substantial additional time and space savings. We conclude that just-in-time strategies are necessary for very large network simulations but that a variety of alternative strategies should be considered whose optimality will depend on the characteristics of the simulation to be run.

Stress and Sucrose Intake Modulate Neuronal Activity in the Anterior Hypothalamic Area in Rats.

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Mitra, Arojit; Guèvremont, Geneviève; Timofeeva, Elena

2016-01-01

The anterior hypothalamic area (AHA) is an important integrative relay structure for a variety of autonomic, endocrine, and behavioral responses including feeding behavior and response to stress. However, changes in the activity of the AHA neurons during stress and feeding in freely moving rats are not clear. The present study investigated the firing rate and burst activity of neurons in the central nucleus of the AHA (cAHA) during sucrose intake in non-stressful conditions and after acute stress in freely behaving rats. Rats were implanted with micro-electrodes into the cAHA, and extracellular multi-unit activity was recorded during 1-h access to 10% sucrose in non-stressful conditions or after acute foot shock stress. Acute stress significantly reduced sucrose intake, total sucrose lick number, and lick frequency in licking clusters, and increased inter-lick intervals. At the cluster start (CS) of sucrose licking, the cAHA neurons increased (CS-excited, 20% of the recorded neurons), decreased (CS-inhibited, 42% of the neurons) or did not change (CS-nonresponsive, 38% of the neurons) their firing rate. Stress resulted in a significant increase in the firing rate of the CS-inhibited neurons by decreasing inter-spike intervals within the burst firing of these neurons. This increase in the stress-induced firing rate of the CS-inhibited neurons was accompanied by a disruption of the correlation between the firing rate of CS-inhibited and CS-nonresponsive neurons that was observed in non-stressful conditions. Stress did not affect the firing rate of the CS-excited and CS-nonresponsive neurons. However, stress changed the pattern of burst firing of the CS-excited and CS-nonresponsive neurons by decreasing and increasing the burst number in the CS-excited and CS-nonresponsive neurons, respectively. These results suggest that the cAHA neurons integrate the signals related to stress and intake of palatable food and play a role in the stress- and eating-related circuitry.

The functional significance of newly born neurons integrated into olfactory bulb circuits.

Science.gov (United States)

Sakamoto, Masayuki; Kageyama, Ryoichiro; Imayoshi, Itaru

2014-01-01

The olfactory bulb (OB) is the first central processing center for olfactory information connecting with higher areas in the brain, and this neuronal circuitry mediates a variety of odor-evoked behavioral responses. In the adult mammalian brain, continuous neurogenesis occurs in two restricted regions, the subventricular zone (SVZ) of the lateral ventricle and the hippocampal dentate gyrus. New neurons born in the SVZ migrate through the rostral migratory stream and are integrated into the neuronal circuits of the OB throughout life. The significance of this continuous supply of new neurons in the OB has been implicated in plasticity and memory regulation. Two decades of huge investigation in adult neurogenesis revealed the biological importance of integration of new neurons into the olfactory circuits. In this review, we highlight the recent findings about the physiological functions of newly generated neurons in rodent OB circuits and then discuss the contribution of neurogenesis in the brain function. Finally, we introduce cutting edge technologies to monitor and manipulate the activity of new neurons.

The functional significance of newly born neurons integrated into olfactory bulb circuits

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Masayuki eSakamoto

2014-05-01

Full Text Available The olfactory bulb (OB is the first central processing center for olfactory information connecting with higher areas in the brain, and this neuronal circuitry mediates a variety of odor-evoked behavioral responses. In the adult mammalian brain, continuous neurogenesis occurs in two restricted regions, the subventricular zone (SVZ of the lateral ventricle and the hippocampal dentate gyrus. New neurons born in the SVZ migrate through the rostral migratory stream and are integrated into the neuronal circuits of the OB throughout life. The significance of this continuous supply of new neurons in the OB has been implicated in plasticity and memory regulation. Two decades of huge investigation in adult neurogenesis revealed the biological importance of integration of new neurons into the olfactory circuits. In this review, we highlight the recent findings about the physiological functions of newly generated neurons in rodent OB circuits and then discuss the contribution of neurogenesis in the brain function. Finally, we introduce cutting edge technologies to monitor and manipulate the activity of new neurons.

Ghrelin decreases firing activity of gonadotropin-releasing hormone (GnRH neurons in an estrous cycle and endocannabinoid signaling dependent manner.

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Imre Farkas

Full Text Available The orexigenic peptide, ghrelin is known to influence function of GnRH neurons, however, the direct effects of the hormone upon these neurons have not been explored, yet. The present study was undertaken to reveal expression of growth hormone secretagogue receptor (GHS-R in GnRH neurons and elucidate the mechanisms of ghrelin actions upon them. Ca(2+-imaging revealed a ghrelin-triggered increase of the Ca(2+-content in GT1-7 neurons kept in a steroid-free medium, which was abolished by GHS-R-antagonist JMV2959 (10 µM suggesting direct action of ghrelin. Estradiol (1nM eliminated the ghrelin-evoked rise of Ca(2+-content, indicating the estradiol dependency of the process. Expression of GHS-R mRNA was then confirmed in GnRH-GFP neurons of transgenic mice by single cell RT-PCR. Firing rate and burst frequency of GnRH-GFP neurons were lower in metestrous than proestrous mice. Ghrelin (40 nM-4 μM administration resulted in a decreased firing rate and burst frequency of GnRH neurons in metestrous, but not in proestrous mice. Ghrelin also decreased the firing rate of GnRH neurons in males. The ghrelin-evoked alterations of the firing parameters were prevented by JMV2959, supporting the receptor-specific actions of ghrelin on GnRH neurons. In metestrous mice, ghrelin decreased the frequency of GABAergic mPSCs in GnRH neurons. Effects of ghrelin were abolished by the cannabinoid receptor type-1 (CB1 antagonist AM251 (1µM and the intracellularly applied DAG-lipase inhibitor THL (10 µM, indicating the involvement of retrograde endocannabinoid signaling. These findings demonstrate that ghrelin exerts direct regulatory effects on GnRH neurons via GHS-R, and modulates the firing of GnRH neurons in an ovarian-cycle and endocannabinoid dependent manner.

Ketogenic diet metabolites reduce firing in central neurons by opening K(ATP) channels.

Science.gov (United States)

Ma, Weiyuan; Berg, Jim; Yellen, Gary

2007-04-04

A low-carbohydrate ketogenic diet remains one of the most effective (but mysterious) treatments for severe pharmacoresistant epilepsy. We have tested for an acute effect of physiological ketone bodies on neuronal firing rates and excitability, to discover possible therapeutic mechanisms of the ketogenic diet. Physiological concentrations of ketone bodies (beta-hydroxybutyrate or acetoacetate) reduced the spontaneous firing rate of neurons in slices from rat or mouse substantia nigra pars reticulata. This region is thought to act as a "seizure gate," controlling seizure generalization. Consistent with an anticonvulsant role, the ketone body effect is larger for cells that fire more rapidly. The effect of ketone bodies was abolished by eliminating the metabolically sensitive K(ATP) channels pharmacologically or by gene knock-out. We propose that ketone bodies or glycolytic restriction treat epilepsy by augmenting a natural activity-limiting function served by K(ATP) channels in neurons.

The connection-set algebra--a novel formalism for the representation of connectivity structure in neuronal network models.

Science.gov (United States)

Djurfeldt, Mikael

2012-07-01

The connection-set algebra (CSA) is a novel and general formalism for the description of connectivity in neuronal network models, from small-scale to large-scale structure. The algebra provides operators to form more complex sets of connections from simpler ones and also provides parameterization of such sets. CSA is expressive enough to describe a wide range of connection patterns, including multiple types of random and/or geometrically dependent connectivity, and can serve as a concise notation for network structure in scientific writing. CSA implementations allow for scalable and efficient representation of connectivity in parallel neuronal network simulators and could even allow for avoiding explicit representation of connections in computer memory. The expressiveness of CSA makes prototyping of network structure easy. A C+ + version of the algebra has been implemented and used in a large-scale neuronal network simulation (Djurfeldt et al., IBM J Res Dev 52(1/2):31-42, 2008b) and an implementation in Python has been publicly released.

Fractional-order leaky integrate-and-fire model with long-term memory and power law dynamics.

Science.gov (United States)

Teka, Wondimu W; Upadhyay, Ranjit Kumar; Mondal, Argha

2017-09-01

Pyramidal neurons produce different spiking patterns to process information, communicate with each other and transform information. These spiking patterns have complex and multiple time scale dynamics that have been described with the fractional-order leaky integrate-and-Fire (FLIF) model. Models with fractional (non-integer) order differentiation that generalize power law dynamics can be used to describe complex temporal voltage dynamics. The main characteristic of FLIF model is that it depends on all past values of the voltage that causes long-term memory. The model produces spikes with high interspike interval variability and displays several spiking properties such as upward spike-frequency adaptation and long spike latency in response to a constant stimulus. We show that the subthreshold voltage and the firing rate of the fractional-order model make transitions from exponential to power law dynamics when the fractional order α decreases from 1 to smaller values. The firing rate displays different types of spike timing adaptation caused by changes on initial values. We also show that the voltage-memory trace and fractional coefficient are the causes of these different types of spiking properties. The voltage-memory trace that represents the long-term memory has a feedback regulatory mechanism and affects spiking activity. The results suggest that fractional-order models might be appropriate for understanding multiple time scale neuronal dynamics. Overall, a neuron with fractional dynamics displays history dependent activities that might be very useful and powerful for effective information processing. Copyright © 2017 Elsevier Ltd. All rights reserved.

Population coding in sparsely connected networks of noisy neurons

OpenAIRE

Tripp, Bryan P.; Orchard, Jeff

2012-01-01

This study examines the relationship between population coding and spatial connection statistics in networks of noisy neurons. Encoding of sensory information in the neocortex is thought to require coordinated neural populations, because individual cortical neurons respond to a wide range of stimuli, and exhibit highly variable spiking in response to repeated stimuli. Population coding is rooted in network structure, because cortical neurons receive information only from other neurons, and be...

Functional Characterization of Lamina X Neurons in ex-Vivo Spinal Cord Preparation

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Volodymyr Krotov

2017-11-01

Full Text Available Functional properties of lamina X neurons in the spinal cord remain unknown despite the established role of this area for somatosensory integration, visceral nociception, autonomic regulation and motoneuron output modulation. Investigations of neuronal functioning in the lamina X have been hampered by technical challenges. Here we introduce an ex-vivo spinal cord preparation with both dorsal and ventral roots still attached for functional studies of the lamina X neurons and their connectivity using an oblique LED illumination for resolved visualization of lamina X neurons in a thick tissue. With the elaborated approach, we demonstrate electrophysiological characteristics of lamina X neurons by their membrane properties, firing pattern discharge and fiber innervation (either afferent or efferent. The tissue preparation has been also probed using Ca2+ imaging with fluorescent Ca2+ dyes (membrane-impermeable or -permeable to demonstrate the depolarization-induced changes in intracellular calcium concentration in lamina X neurons. Finally, we performed visualization of subpopulations of lamina X neurons stained by retrograde labeling with aminostilbamidine dye to identify sympathetic preganglionic and projection neurons in the lamina X. Thus, the elaborated approach provides a reliable tool for investigation of functional properties and connectivity in specific neuronal subpopulations, boosting research of lamina X of the spinal cord.

Barbed channels enhance unidirectional connectivity between neuronal networks cultured on multi electrode arrays

Science.gov (United States)

le Feber, Joost; Postma, Wybren; de Weerd, Eddy; Weusthof, Marcel; Rutten, Wim L. C.

2015-01-01

Cultured neurons on multi electrode arrays (MEAs) have been widely used to study various aspects of neuronal (network) functioning. A possible drawback of this approach is the lack of structure in these networks. At the single cell level, several solutions have been proposed to enable directed connectivity, and promising results were obtained. At the level of connected sub-populations, a few attempts have been made with promising results. First assessment of the designs' functionality, however, suggested room for further improvement. We designed a two chamber MEA aiming to create a unidirectional connection between the networks in both chambers (“emitting” and “receiving”). To achieve this unidirectionality, all interconnecting channels contained barbs that hindered axon growth in the opposite direction (from receiving to emitting chamber). Visual inspection showed that axons predominantly grew through the channels in the promoted direction. This observation was confirmed by spontaneous activity recordings. Cross-correlation between the signals from two electrodes inside the channels suggested signal propagation at ≈2 m/s from emitting to receiving chamber. Cross-correlation between the firing patterns in both chambers indicated that most correlated activity was initiated in the emitting chamber, which was also reflected by a significantly lower fraction of partial bursts (i.e., a one-chamber-only burst) in the emitting chamber. Finally, electrical stimulation in the emitting chamber induced a fast response in that chamber, and a slower response in the receiving chamber. Stimulation in the receiving chamber evoked a fast response in that chamber, but no response in the emitting chamber. These results confirm the predominantly unidirectional nature of the connecting channels from emitting to receiving chamber. PMID:26578869

Short and Long-Term Attentional Firing Rates Can Be Explained by ST-Neuron Dynamics

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Oscar J. Avella Gonzalez

2018-03-01

Full Text Available Attention modulates neural selectivity and optimizes the allocation of cortical resources during visual tasks. A large number of experimental studies in primates and humans provide ample evidence. As an underlying principle of visual attention, some theoretical models suggested the existence of a gain element that enhances contrast of the attended stimuli. In contrast, the Selective Tuning model of attention (ST proposes an attentional mechanism based on suppression of irrelevant signals. In this paper, we present an updated characterization of the ST-neuron proposed by the Selective Tuning model, and suggest that the inclusion of adaptation currents (Ih to ST-neurons may explain the temporal profiles of the firing rates recorded in single V4 cells during attentional tasks. Furthermore, using the model we show that the interaction between stimulus-selectivity of a neuron and attention shapes the profile of the firing rate, and is enough to explain its fast modulation and other discontinuities observed, when the neuron responds to a sudden switch of stimulus, or when one stimulus is added to another during a visual task.

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

Schaffer collateral inputs to CA1 excitatory and inhibitory neurons follow different connectivity rules.

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Kwon, Osung; Feng, Linqing; Druckmann, Shaul; Kim, Jinhyun

2018-05-04

Neural circuits, governed by a complex interplay between excitatory and inhibitory neurons, are the substrate for information processing, and the organization of synaptic connectivity in neural network is an important determinant of circuit function. Here, we analyzed the fine structure of connectivity in hippocampal CA1 excitatory and inhibitory neurons innervated by Schaffer collaterals (SCs) using mGRASP in male mice. Our previous study revealed spatially structured synaptic connectivity between CA3-CA1 pyramidal cells (PCs). Surprisingly, parvalbumin-positive interneurons (PVs) showed a significantly more random pattern spatial structure. Notably, application of Peters' Rule for synapse prediction by random overlap between axons and dendrites enhanced structured connectivity in PCs, but, by contrast, made the connectivity pattern in PVs more random. In addition, PCs in a deep sublayer of striatum pyramidale appeared more highly structured than PCs in superficial layers, and little or no sublayer specificity was found in PVs. Our results show that CA1 excitatory PCs and inhibitory PVs innervated by the same SC inputs follow different connectivity rules. The different organizations of fine scale structured connectivity in hippocampal excitatory and inhibitory neurons provide important insights into the development and functions of neural networks. SIGNIFICANCE STATEMENT Understanding how neural circuits generate behavior is one of the central goals of neuroscience. An important component of this endeavor is the mapping of fine-scale connection patterns that underlie, and help us infer, signal processing in the brain. Here, using our recently developed synapse detection technology (mGRASP and neuTube), we provide detailed profiles of synaptic connectivity in excitatory (CA1 pyramidal) and inhibitory (CA1 parvalbumin-positive) neurons innervated by the same presynaptic inputs (CA3 Schaffer collaterals). Our results reveal that these two types of CA1 neurons follow

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

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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.

Multifaceted effects of oligodendroglial exosomes on neurons: impact on neuronal firing rate, signal transduction and gene regulation.

Science.gov (United States)

Fröhlich, Dominik; Kuo, Wen Ping; Frühbeis, Carsten; Sun, Jyh-Jang; Zehendner, Christoph M; Luhmann, Heiko J; Pinto, Sheena; Toedling, Joern; Trotter, Jacqueline; Krämer-Albers, Eva-Maria

2014-09-26

Exosomes are small membranous vesicles of endocytic origin that are released by almost every cell type. They exert versatile functions in intercellular communication important for many physiological and pathological processes. Recently, exosomes attracted interest with regard to their role in cell-cell communication in the nervous system. We have shown that exosomes released from oligodendrocytes upon stimulation with the neurotransmitter glutamate are internalized by neurons and enhance the neuronal stress tolerance. Here, we demonstrate that oligodendroglial exosomes also promote neuronal survival during oxygen-glucose deprivation, a model of cerebral ischaemia. We show the transfer from oligodendrocytes to neurons of superoxide dismutase and catalase, enzymes which are known to help cells to resist oxidative stress. Additionally, we identify various effects of oligodendroglial exosomes on neuronal physiology. Electrophysiological analysis using in vitro multi-electrode arrays revealed an increased firing rate of neurons exposed to oligodendroglial exosomes. Moreover, gene expression analysis and phosphorylation arrays uncovered differentially expressed genes and altered signal transduction pathways in neurons after exosome treatment. Our study thus provides new insight into the broad spectrum of action of oligodendroglial exosomes and their effects on neuronal physiology. The exchange of extracellular vesicles between neural cells may exhibit remarkable potential to impact brain performance. © 2014 The Author(s) Published by the Royal Society. All rights reserved.

Variable Action Potential Backpropagation during Tonic Firing and Low-Threshold Spike Bursts in Thalamocortical But Not Thalamic Reticular Nucleus Neurons.

Science.gov (United States)

Connelly, William M; Crunelli, Vincenzo; Errington, Adam C

2017-05-24

Backpropagating action potentials (bAPs) are indispensable in dendritic signaling. Conflicting Ca 2+ -imaging data and an absence of dendritic recording data means that the extent of backpropagation in thalamocortical (TC) and thalamic reticular nucleus (TRN) neurons remains unknown. Because TRN neurons signal electrically through dendrodendritic gap junctions and possibly via chemical dendritic GABAergic synapses, as well as classical axonal GABA release, this lack of knowledge is problematic. To address this issue, we made two-photon targeted patch-clamp recordings from rat TC and TRN neuron dendrites to measure bAPs directly. These recordings reveal that "tonic"' and low-threshold-spike (LTS) "burst" APs in both cell types are always recorded first at the soma before backpropagating into the dendrites while undergoing substantial distance-dependent dendritic amplitude attenuation. In TC neurons, bAP attenuation strength varies according to firing mode. During LTS bursts, somatic AP half-width increases progressively with increasing spike number, allowing late-burst spikes to propagate more efficiently into the dendritic tree compared with spikes occurring at burst onset. Tonic spikes have similar somatic half-widths to late burst spikes and undergo similar dendritic attenuation. In contrast, in TRN neurons, AP properties are unchanged between LTS bursts and tonic firing and, as a result, distance-dependent dendritic attenuation remains consistent across different firing modes. Therefore, unlike LTS-associated global electrical and calcium signals, the spatial influence of bAP signaling in TC and TRN neurons is more restricted, with potentially important behavioral-state-dependent consequences for synaptic integration and plasticity in thalamic neurons. SIGNIFICANCE STATEMENT In most neurons, action potentials (APs) initiate in the axosomatic region and propagate into the dendritic tree to provide a retrograde signal that conveys information about the level of

Connectivity of Pacemaker Neurons in the Neonatal Rat Superficial Dorsal Horn

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Ford, Neil C.; Arbabi, Shahriar; Baccei, Mark L.

2014-01-01

Pacemaker neurons with an intrinsic ability to generate rhythmic burst-firing have been characterized in lamina I of the neonatal spinal cord, where they are innervated by high-threshold sensory afferents. However, little is known about the output of these pacemakers, as the neuronal populations which are targeted by pacemaker axons have yet to be identified. The present study combines patch clamp recordings in the intact neonatal rat spinal cord with tract-tracing to demonstrate that lamina I pacemaker neurons contact multiple spinal motor pathways during early life. Retrograde labeling of premotor interneurons with the trans-synaptic virus PRV-152 revealed the presence of burst-firing in PRV-infected lamina I neurons, thereby confirming that pacemakers are synaptically coupled to motor networks in the spinal ventral horn. Notably, two classes of pacemakers could be distinguished in lamina I based on cell size and the pattern of their axonal projections. While small pacemaker neurons possessed ramified axons which contacted ipsilateral motor circuits, large pacemaker neurons had unbranched axons which crossed the midline and ascended rostrally in the contralateral white matter. Recordings from identified spino-parabrachial and spino-PAG neurons indicated the presence of pacemaker activity within neonatal lamina I projection neurons. Overall, these results show that lamina I pacemakers are positioned to regulate both the level of activity in developing motor circuits as well as the ascending flow of nociceptive information to the brain, thus highlighting a potential role for pacemaker activity in the maturation of pain and sensorimotor networks in the CNS. PMID:25380417

Neural network regulation driven by autonomous neural firings

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Cho, Myoung Won

2016-07-01

Biological neurons naturally fire spontaneously due to the existence of a noisy current. Such autonomous firings may provide a driving force for network formation because synaptic connections can be modified due to neural firings. Here, we study the effect of autonomous firings on network formation. For the temporally asymmetric Hebbian learning, bidirectional connections lose their balance easily and become unidirectional ones. Defining the difference between reciprocal connections as new variables, we could express the learning dynamics as if Ising model spins interact with each other in magnetism. We present a theoretical method to estimate the interaction between the new variables in a neural system. We apply the method to some network systems and find some tendencies of autonomous neural network regulation.

Information transmission with spiking Bayesian neurons

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Lochmann, Timm; Deneve, Sophie

2008-01-01

Spike trains of cortical neurons resulting from repeatedpresentations of a stimulus are variable and exhibit Poisson-like statistics. Many models of neural coding therefore assumed that sensory information is contained in instantaneous firing rates, not spike times. Here, we ask how much information about time-varying stimuli can be transmitted by spiking neurons with such input and output variability. In particular, does this variability imply spike generation to be intrinsically stochastic? We consider a model neuron that estimates optimally the current state of a time-varying binary variable (e.g. presence of a stimulus) by integrating incoming spikes. The unit signals its current estimate to other units with spikes whenever the estimate increased by a fixed amount. As shown previously, this computation results in integrate and fire dynamics with Poisson-like output spike trains. This output variability is entirely due to the stochastic input rather than noisy spike generation. As a result such a deterministic neuron can transmit most of the information about the time varying stimulus. This contrasts with a standard model of sensory neurons, the linear-nonlinear Poisson (LNP) model which assumes that most variability in output spike trains is due to stochastic spike generation. Although it yields the same firing statistics, we found that such noisy firing results in the loss of most information. Finally, we use this framework to compare potential effects of top-down attention versus bottom-up saliency on information transfer with spiking neurons

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

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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.

Learning causes reorganization of neuronal firing patterns to represent related experiences within a hippocampal schema.

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McKenzie, Sam; Robinson, Nick T M; Herrera, Lauren; Churchill, Jordana C; Eichenbaum, Howard

2013-06-19

According to schema theory as proposed by Piaget and Bartlett, learning involves the assimilation of new memories into networks of preexisting knowledge, as well as alteration of the original networks to accommodate the new information. Recent evidence has shown that rats form a schema of goal locations and that the hippocampus plays an essential role in adding new memories to the spatial schema. Here we examined the nature of hippocampal contributions to schema updating by monitoring firing patterns of multiple CA1 neurons as rats learned new goal locations in an environment in which there already were multiple goals. Before new learning, many neurons that fired on arrival at one goal location also fired at other goals, whereas ensemble activity patterns also distinguished different goal events, thus constituting a neural representation that linked distinct goals within a spatial schema. During new learning, some neurons began to fire as animals approached the new goals. These were primarily the same neurons that fired at original goals, the activity patterns at new goals were similar to those associated with the original goals, and new learning also produced changes in the preexisting goal-related firing patterns. After learning, activity patterns associated with the new and original goals gradually diverged, such that initial generalization was followed by a prolonged period in which new memories became distinguished within the ensemble representation. These findings support the view that consolidation involves assimilation of new memories into preexisting neural networks that accommodate relationships among new and existing memories.

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

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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.

Motif statistics and spike correlations in neuronal networks

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Hu, Yu; Shea-Brown, Eric; Trousdale, James; Josić, Krešimir

2013-01-01

Motifs are patterns of subgraphs of complex networks. We studied the impact of such patterns of connectivity on the level of correlated, or synchronized, spiking activity among pairs of cells in a recurrent network of integrate and fire neurons. For a range of network architectures, we find that the pairwise correlation coefficients, averaged across the network, can be closely approximated using only three statistics of network connectivity. These are the overall network connection probability and the frequencies of two second order motifs: diverging motifs, in which one cell provides input to two others, and chain motifs, in which two cells are connected via a third intermediary cell. Specifically, the prevalence of diverging and chain motifs tends to increase correlation. Our method is based on linear response theory, which enables us to express spiking statistics using linear algebra, and a resumming technique, which extrapolates from second order motifs to predict the overall effect of coupling on network correlation. Our motif-based results seek to isolate the effect of network architecture perturbatively from a known network state. (paper)

Urethane anesthesia depresses activities of thalamocortical neurons and alters its response to nociception in terms of dual firing modes

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Yeowool eHuh

2013-10-01

Full Text Available Anesthetics are often used to characterize the activity of single neurons in-vivo for its advantages such as reduced noise level and convenience in noxious stimulations. Of the anesthetics, urethane had been widely used in some thalamic studies under the assumption that sensory signals are still relayed to the thalamus under urethane anesthesia and that thalamic response would therefore reflect the response of the awake state. We tested whether this assumption stands by comparing thalamic activity in terms of tonic and burst firing modes during ‘the awake state’ or under ‘urethane anesthesia’ utilizing the extracellular single unit recording technique. First we have tested how thalamic relay neurons respond to the introduction of urethane and then tested how urethane influences thalamic discharges under formalin-induced nociception. Urethane significantly depressed overall firing rates of thalamic relay neurons, which was sustained despite the delayed increase of burst activity over the 4 hour recording period. Thalamic response to nociception under anesthesia was also similar overall except for the slight and transient increase of burst activity. Overall, results demonstrated that urethane suppresses the activity of thalamic relay neurons and that, despite the slight fluctuation of burst firing, formalin-induced nociception cannot significantly change the firing pattern of thalamic relay neurons that was caused by urethane.

Thermodynamic Analyses of Biomass Gasification Integrated Externally Fired, Post-Firing and Dual-Fuel Combined Cycles

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Saeed Soltani

2015-01-01

Full Text Available In the present work, the results are reported of the energy and exergy analyses of three biomass-related processes for electricity generation: the biomass gasification integrated externally fired combined cycle, the biomass gasification integrated dual-fuel combined cycle, and the biomass gasification integrated post-firing combined cycle. The energy efficiency for the biomass gasification integrated post-firing combined cycle is 3% to 6% points higher than for the other cycles. Although the efficiency of the externally fired biomass combined cycle is the lowest, it has an advantage in that it only uses biomass. The energy and exergy efficiencies are maximized for the three configurations at particular values of compressor pressure ratios, and increase with gas turbine inlet temperature. As pressure ratio increases, the mass of air per mass of steam decreases for the biomass gasification integrated post-firing combined cycle, but the pressure ratio has little influence on the ratio of mass of air per mass of steam for the other cycles. The gas turbine exergy efficiency is the highest for the three configurations. The combustion chamber for the dual-fuel cycle exhibits the highest exergy efficiency and that for the post-firing cycle the lowest. Another benefit of the biomass gasification integrated externally fired combined cycle is that it exhibits the highest air preheater and heat recovery steam generator exergy efficiencies.

Local connections of layer 5 GABAergic interneurons to corticospinal neurons

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Yasuyo H Tanaka

2011-09-01

Full Text Available In the local circuit of the cerebral cortex, GABAergic inhibitory interneurons are considered to work in collaboration with excitatory neurons. Although many interneuron subgroups have been described in the cortex, local inhibitory connections of each interneuron subgroup are only partially understood with respect to the functional neuron groups that receive these inhibitory connections. In the present study, we morphologically examined local inhibitory inputs to corticospinal neurons (CSNs in motor areas using transgenic rats in which GABAergic neurons expressed fluorescent protein Venus. By analysis of biocytin-filled axons obtained with whole-cell recording/staining in cortical slices, we classified fast-spiking (FS neurons in layer (L 5 into two types, FS1 and FS2, by their high and low densities of axonal arborization, respectively. We then investigated the connections of FS1, FS2, somatostatin-immunopositive (SOM and other (non-FS/non-SOM interneurons to CSNs that were retrogradely labeled in a Golgi-like manner in motor areas. When close appositions between the axon boutons of the intracellularly labeled interneurons and the somata/dendrites of the retrogradely labeled CSNs were examined electron-microscopically, 74% of these appositions made symmetric synaptic contacts. The axon boutons of single FS1 neurons were 2–4-fold more frequent in appositions to the somata/dendrites of CSNs than those of FS2, SOM and non-FS/non-SOM neurons. Axosomatic appositions were most frequently formed with axon boutons of FS1 and FS2 neurons (approximately 30% and least frequently formed with those of SOM neurons (7%. In contrast, SOM neurons most extensively sent axon boutons to the apical dendrites of CSNs. These results might suggest that motor outputs are controlled differentially by the subgroups of L5 GABAergic interneurons in cortical motor areas. 

Layer 5 Callosal Parvalbumin-Expressing Neurons: A Distinct Functional Group of GABAergic Neurons.

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Zurita, Hector; Feyen, Paul L C; Apicella, Alfonso Junior

2018-01-01

Previous studies have shown that parvalbumin-expressing neurons (CC-Parv neurons) connect the two hemispheres of motor and sensory areas via the corpus callosum, and are a functional part of the cortical circuit. Here we test the hypothesis that layer 5 CC-Parv neurons possess anatomical and molecular mechanisms which dampen excitability and modulate the gating of interhemispheric inhibition. In order to investigate this hypothesis we use viral tracing to determine the anatomical and electrophysiological properties of layer 5 CC-Parv and parvalbumin-expressing (Parv) neurons of the mouse auditory cortex (AC). Here we show that layer 5 CC-Parv neurons had larger dendritic fields characterized by longer dendrites that branched farther from the soma, whereas layer 5 Parv neurons had smaller dendritic fields characterized by shorter dendrites that branched nearer to the soma. The layer 5 CC-Parv neurons are characterized by delayed action potential (AP) responses to threshold currents, lower firing rates, and lower instantaneous frequencies compared to the layer 5 Parv neurons. Kv1.1 containing K + channels are the main source of the AP repolarization of the layer 5 CC-Parv and have a major role in determining both the spike delayed response, firing rate and instantaneous frequency of these neurons.

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

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

2014-05-01

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

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

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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.

Synchronization from Second Order Network Connectivity Statistics

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Zhao, Liqiong; Beverlin, Bryce; Netoff, Theoden; Nykamp, Duane Q.

2011-01-01

We investigate how network structure can influence the tendency for a neuronal network to synchronize, or its synchronizability, independent of the dynamical model for each neuron. The synchrony analysis takes advantage of the framework of second order networks, which defines four second order connectivity statistics based on the relative frequency of two-connection network motifs. The analysis identifies two of these statistics, convergent connections, and chain connections, as highly influencing the synchrony. Simulations verify that synchrony decreases with the frequency of convergent connections and increases with the frequency of chain connections. These trends persist with simulations of multiple models for the neuron dynamics and for different types of networks. Surprisingly, divergent connections, which determine the fraction of shared inputs, do not strongly influence the synchrony. The critical role of chains, rather than divergent connections, in influencing synchrony can be explained by their increasing the effective coupling strength. The decrease of synchrony with convergent connections is primarily due to the resulting heterogeneity in firing rates. PMID:21779239

Leaders of neuronal cultures in a quorum percolation model

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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.

Dense neuron clustering explains connectivity statistics in cortical microcircuits.

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Vladimir V Klinshov

Full Text Available Local cortical circuits appear highly non-random, but the underlying connectivity rule remains elusive. Here, we analyze experimental data observed in layer 5 of rat neocortex and suggest a model for connectivity from which emerge essential observed non-random features of both wiring and weighting. These features include lognormal distributions of synaptic connection strength, anatomical clustering, and strong correlations between clustering and connection strength. Our model predicts that cortical microcircuits contain large groups of densely connected neurons which we call clusters. We show that such a cluster contains about one fifth of all excitatory neurons of a circuit which are very densely connected with stronger than average synapses. We demonstrate that such clustering plays an important role in the network dynamics, namely, it creates bistable neural spiking in small cortical circuits. Furthermore, introducing local clustering in large-scale networks leads to the emergence of various patterns of persistent local activity in an ongoing network activity. Thus, our results may bridge a gap between anatomical structure and persistent activity observed during working memory and other cognitive processes.

Connectivity, excitability and activity patterns in neuronal networks

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Le Feber, Joost; Stoyanova, Irina I; Chiappalone, Michela

2014-01-01

Extremely synchronized firing patterns such as those observed in brain diseases like epilepsy may result from excessive network excitability. Although network excitability is closely related to (excitatory) connectivity, a direct measure for network excitability remains unavailable. Several methods currently exist for estimating network connectivity, most of which are related to cross-correlation. An example is the conditional firing probability (CFP) analysis which calculates the pairwise probability (CFP i,j ) that electrode j records an action potential at time t = τ, given that electrode i recorded a spike at t = 0. However, electrode i often records multiple spikes within the analysis interval, and CFP values are biased by the on-going dynamic state of the network. Here we show that in a linear approximation this bias may be removed by deconvoluting CFP i,j with the autocorrelation of i (i.e. CFP i,i ), to obtain the single pulse response (SPR i,j )—the average response at electrode j to a single spike at electrode i. Thus, in a linear system SPRs would be independent of the dynamic network state. Nonlinear components of synaptic transmission, such as facilitation and short term depression, will however still affect SPRs. Therefore SPRs provide a clean measure of network excitability. We used carbachol and ghrelin to moderately activate cultured cortical networks to affect their dynamic state. Both neuromodulators transformed the bursting firing patterns of the isolated networks into more dispersed firing. We show that the influence of the dynamic state on SPRs is much smaller than the effect on CFPs, but not zero. The remaining difference reflects the alteration in network excitability. We conclude that SPRs are less contaminated by the dynamic network state and that mild excitation may decrease network excitability, possibly through short term synaptic depression. (papers)

Substance P Differentially Modulates Firing Rate of Solitary Complex (SC) Neurons from Control and Chronic Hypoxia-Adapted Adult Rats

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Nichols, Nicole L.; Powell, Frank L.; Dean, Jay B.; Putnam, Robert W.

2014-01-01

NK1 receptors, which bind substance P, are present in the majority of brainstem regions that contain CO2/H+-sensitive neurons that play a role in central chemosensitivity. However, the effect of substance P on the chemosensitive response of neurons from these regions has not been studied. Hypoxia increases substance P release from peripheral afferents that terminate in the caudal nucleus tractus solitarius (NTS). Here we studied the effect of substance P on the chemosensitive responses of solitary complex (SC: NTS and dorsal motor nucleus) neurons from control and chronic hypoxia-adapted (CHx) adult rats. We simultaneously measured intracellular pH and electrical responses to hypercapnic acidosis in SC neurons from control and CHx adult rats using the blind whole cell patch clamp technique and fluorescence imaging microscopy. Substance P significantly increased the basal firing rate in SC neurons from control and CHx rats, although the increase was smaller in CHx rats. However, substance P did not affect the chemosensitive response of SC neurons from either group of rats. In conclusion, we found that substance P plays a role in modulating the basal firing rate of SC neurons but the magnitude of the effect is smaller for SC neurons from CHx adult rats, implying that NK1 receptors may be down regulated in CHx adult rats. Substance P does not appear to play a role in modulating the firing rate response to hypercapnic acidosis of SC neurons from either control or CHx adult rats. PMID:24516602

A chimeric path to neuronal synchronization

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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).

A chimeric path to neuronal synchronization

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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)

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)

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

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

2016-02-01

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

Cited2 Regulates Neocortical Layer II/III Generation and Somatosensory Callosal Projection Neuron Development and Connectivity.

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Fame, Ryann M; MacDonald, Jessica L; Dunwoodie, Sally L; Takahashi, Emi; Macklis, Jeffrey D

2016-06-15

The neocortex contains hundreds to thousands of distinct subtypes of precisely connected neurons, allowing it to perform remarkably complex tasks of high-level cognition. Callosal projection neurons (CPN) connect the cerebral hemispheres via the corpus callosum, integrating cortical information and playing key roles in associative cognition. CPN are a strikingly diverse set of neuronal subpopulations, and development of this diversity requires precise control by a complex, interactive set of molecular effectors. We have found that the transcriptional coregulator Cited2 regulates and refines two stages of CPN development. Cited2 is expressed broadly by progenitors in the embryonic day 15.5 subventricular zone, during the peak of superficial layer CPN birth, with a progressive postmitotic refinement in expression, becoming restricted to CPN of the somatosensory cortex postnatally. We generated progenitor-stage and postmitotic forebrain-specific Cited2 conditional knock-out mice, using the Emx1-Cre and NEX-Cre mouse lines, respectively. We demonstrate that Cited2 functions in progenitors, but is not necessary postmitotically, to regulate both (1) broad generation of layer II/III CPN and (2) acquisition of precise area-specific molecular identity and axonal/dendritic connectivity of somatosensory CPN. This novel CPN subtype-specific and area-specific control from progenitor action of Cited2 adds yet another layer of complexity to the multistage developmental regulation of neocortical development. This study identifies Cited2 as a novel subtype-specific and area-specific control over development of distinct subpopulations within the broad population of callosal projection neurons (CPN), whose axons connect the two cerebral hemispheres via the corpus callosum (CC). Currently, how the remarkable diversity of CPN subtypes is specified, and how they differentiate to form highly precise and specific circuits, are largely unknown. We found that Cited2 functions within

Effects of self-coupling and asymmetric output on metastable dynamical transient firing patterns in arrays of neurons with bidirectional inhibitory coupling.

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Horikawa, Yo

2016-04-01

Metastable dynamical transient patterns in arrays of bidirectionally coupled neurons with self-coupling and asymmetric output were studied. First, an array of asymmetric sigmoidal neurons with symmetric inhibitory bidirectional coupling and self-coupling was considered and the bifurcations of its steady solutions were shown. Metastable dynamical transient spatially nonuniform states existed in the presence of a pair of spatially symmetric stable solutions as well as unstable spatially nonuniform solutions in a restricted range of the output gain of a neuron. The duration of the transients increased exponentially with the number of neurons up to the maximum number at which the spatially nonuniform steady solutions were stabilized. The range of the output gain for which they existed reduced as asymmetry in a sigmoidal output function of a neuron increased, while the existence range expanded as the strength of inhibitory self-coupling increased. Next, arrays of spiking neuron models with slow synaptic inhibitory bidirectional coupling and self-coupling were considered with computer simulation. In an array of Class 1 Hindmarsh-Rose type models, in which each neuron showed a graded firing rate, metastable dynamical transient firing patterns were observed in the presence of inhibitory self-coupling. This agreed with the condition for the existence of metastable dynamical transients in an array of sigmoidal neurons. In an array of Class 2 Bonhoeffer-van der Pol models, in which each neuron had a clear threshold between firing and resting, long-lasting transient firing patterns with bursting and irregular motion were observed. Copyright © 2016 Elsevier Ltd. All rights reserved.

Cognitive Deficits Associated with Nav1.1 Alterations: Involvement of Neuronal Firing Dynamics and Oscillations.

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Alex C Bender

Full Text Available Brain oscillations play a critical role in information processing and may, therefore, be essential to uncovering the mechanisms of cognitive impairment in neurological disease. In Dravet syndrome (DS, a mutation in SCN1A, coding for the voltage-gated sodium channel Nav1.1, is associated with severe cognitive impairment and seizures. While seizure frequency and severity do not correlate with the extent of impairment, the slowing of brain rhythms may be involved. Here we investigate the role of Nav1.1 on brain rhythms and cognition using RNA interference. We demonstrate that knockdown of Nav1.1 impairs fast- and burst-firing properties of neurons in the medial septum in vivo. The proportion of neurons that fired phase-locked to hippocampal theta oscillations was reduced, and medial septal regulation of theta rhythm was disrupted. During a working memory task, this deficit was characterized by a decrease in theta frequency and was negatively correlated with performance. These findings suggest a fundamental role for Nav1.1 in facilitating fast-firing properties in neurons, highlight the importance of precise temporal control of theta frequency for working memory, and imply that Nav1.1 deficits may disrupt information processing in DS via a dysregulation of brain rhythms.

Substance P differentially modulates firing rate of solitary complex (SC neurons from control and chronic hypoxia-adapted adult rats.

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Nicole L Nichols

Full Text Available NK1 receptors, which bind substance P, are present in the majority of brainstem regions that contain CO2/H(+-sensitive neurons that play a role in central chemosensitivity. However, the effect of substance P on the chemosensitive response of neurons from these regions has not been studied. Hypoxia increases substance P release from peripheral afferents that terminate in the caudal nucleus tractus solitarius (NTS. Here we studied the effect of substance P on the chemosensitive responses of solitary complex (SC: NTS and dorsal motor nucleus neurons from control and chronic hypoxia-adapted (CHx adult rats. We simultaneously measured intracellular pH and electrical responses to hypercapnic acidosis in SC neurons from control and CHx adult rats using the blind whole cell patch clamp technique and fluorescence imaging microscopy. Substance P significantly increased the basal firing rate in SC neurons from control and CHx rats, although the increase was smaller in CHx rats. However, substance P did not affect the chemosensitive response of SC neurons from either group of rats. In conclusion, we found that substance P plays a role in modulating the basal firing rate of SC neurons but the magnitude of the effect is smaller for SC neurons from CHx adult rats, implying that NK1 receptors may be down regulated in CHx adult rats. Substance P does not appear to play a role in modulating the firing rate response to hypercapnic acidosis of SC neurons from either control or CHx adult rats.

[Neuroeffector connections of multimodal neurons in the African snail (Achatina fulica)].

Science.gov (United States)

Bugaĭ, V V; Zhuravlev, V L; Safonova, T A

2004-02-01

Using a new method of animal preparation, the efferent connections of giant paired neurons on the dorsal surface of visceral and right parietal ganglia of snail, Achatina fulica, were examined. It was found that spikes in giant neurons d-VLN and d-RPLN evoke postjunctional potentials in different points of the snail body and viscerae (in the heart, in pericardium, in lung cavity and kidney walls, in mantle and body wall muscles, in tentacle retractors and in cephalic artery). The preliminary analysis of synaptic latency and facilitation suggests a direct connections between giant neurons and investigated efferents.

The Edinger-Westphal nucleus of the juvenile rat contains transient- and repetitive-firing neurons

DEFF Research Database (Denmark)

Laursen, M; Rekling, J C

2006-01-01

Classically, the Edinger-Westphal nucleus is described as containing neurons controlling accommodation and pupillary constriction via projections to the ciliary ganglion. However, in several species including rat, some Edinger-Westphal neurons have ascending or descending CNS projections suggesting...... an immunohistochemical procedure directed at the peptide Urocortin, which is expressed in Edinger-Westphal neurons. Passive and active membrane responses were investigated and two different neuron types were identified. One type had a transient firing response to 400 ms depolarizing current pulses and one type had...... threshold Ca(2+) spikes were seen and these were blocked by nickel(II) chloride hexahydrate, suggesting that they are mediated via low voltage-activated Ca(2+) channels. Some biocytin-labeled neurons had axons or axonal collaterals projecting laterally or dorsally, suggesting possible non-ocular targets...

Anti-correlated cortical networks arise from spontaneous neuronal dynamics at slow timescales.

Science.gov (United States)

Kodama, Nathan X; Feng, Tianyi; Ullett, James J; Chiel, Hillel J; Sivakumar, Siddharth S; Galán, Roberto F

2018-01-12

In the highly interconnected architectures of the cerebral cortex, recurrent intracortical loops disproportionately outnumber thalamo-cortical inputs. These networks are also capable of generating neuronal activity without feedforward sensory drive. It is unknown, however, what spatiotemporal patterns may be solely attributed to intrinsic connections of the local cortical network. Using high-density microelectrode arrays, here we show that in the isolated, primary somatosensory cortex of mice, neuronal firing fluctuates on timescales from milliseconds to tens of seconds. Slower firing fluctuations reveal two spatially distinct neuronal ensembles, which correspond to superficial and deeper layers. These ensembles are anti-correlated: when one fires more, the other fires less and vice versa. This interplay is clearest at timescales of several seconds and is therefore consistent with shifts between active sensing and anticipatory behavioral states in mice.

Selectivity and sparseness in randomly connected balanced networks.

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Cengiz Pehlevan

Full Text Available Neurons in sensory cortex show stimulus selectivity and sparse population response, even in cases where no strong functionally specific structure in connectivity can be detected. This raises the question whether selectivity and sparseness can be generated and maintained in randomly connected networks. We consider a recurrent network of excitatory and inhibitory spiking neurons with random connectivity, driven by random projections from an input layer of stimulus selective neurons. In this architecture, the stimulus-to-stimulus and neuron-to-neuron modulation of total synaptic input is weak compared to the mean input. Surprisingly, we show that in the balanced state the network can still support high stimulus selectivity and sparse population response. In the balanced state, strong synapses amplify the variation in synaptic input and recurrent inhibition cancels the mean. Functional specificity in connectivity emerges due to the inhomogeneity caused by the generative statistical rule used to build the network. We further elucidate the mechanism behind and evaluate the effects of model parameters on population sparseness and stimulus selectivity. Network response to mixtures of stimuli is investigated. It is shown that a balanced state with unselective inhibition can be achieved with densely connected input to inhibitory population. Balanced networks exhibit the "paradoxical" effect: an increase in excitatory drive to inhibition leads to decreased inhibitory population firing rate. We compare and contrast selectivity and sparseness generated by the balanced network to randomly connected unbalanced networks. Finally, we discuss our results in light of experiments.

The iso-response method: measuring neuronal stimulus integration with closed-loop experiments

Science.gov (United States)

Gollisch, Tim; Herz, Andreas V. M.

2012-01-01

Throughout the nervous system, neurons integrate high-dimensional input streams and transform them into an output of their own. This integration of incoming signals involves filtering processes and complex non-linear operations. The shapes of these filters and non-linearities determine the computational features of single neurons and their functional roles within larger networks. A detailed characterization of signal integration is thus a central ingredient to understanding information processing in neural circuits. Conventional methods for measuring single-neuron response properties, such as reverse correlation, however, are often limited by the implicit assumption that stimulus integration occurs in a linear fashion. Here, we review a conceptual and experimental alternative that is based on exploring the space of those sensory stimuli that result in the same neural output. As demonstrated by recent results in the auditory and visual system, such iso-response stimuli can be used to identify the non-linearities relevant for stimulus integration, disentangle consecutive neural processing steps, and determine their characteristics with unprecedented precision. Automated closed-loop experiments are crucial for this advance, allowing rapid search strategies for identifying iso-response stimuli during experiments. Prime targets for the method are feed-forward neural signaling chains in sensory systems, but the method has also been successfully applied to feedback systems. Depending on the specific question, “iso-response” may refer to a predefined firing rate, single-spike probability, first-spike latency, or other output measures. Examples from different studies show that substantial progress in understanding neural dynamics and coding can be achieved once rapid online data analysis and stimulus generation, adaptive sampling, and computational modeling are tightly integrated into experiments. PMID:23267315

Response of spiking neurons to correlated inputs

International Nuclear Information System (INIS)

Moreno, Ruben; Rocha, Jaime de la; Renart, Alfonso; Parga, Nestor

2002-01-01

The effect of a temporally correlated afferent current on the firing rate of a leaky integrate-and-fire neuron is studied. This current is characterized in terms of rates, autocorrelations, and cross correlations, and correlation time scale τ c of excitatory and inhibitory inputs. The output rate ν out is calculated in the Fokker-Planck formalism in the limit of both small and large τ c compared to the membrane time constant τ of the neuron. By simulations we check the analytical results, provide an interpolation valid for all τ c , and study the neuron's response to rapid changes in the correlation magnitude

Neuroeffector connections of giant multimodal neurons in the African snail Achatina fulica.

Science.gov (United States)

Bugai, V V; Zhuravlev, V L; Safonova, T A

2005-07-01

A new method of making preparations was used to analyse the neuroeffector connections of the paired giant neurons of the African snail Achatina fulica. These neurons were found to induce postsynaptic potentials in the muscles of the mantle, heart, the wall of the pulmonary cavity, and the muscular elements of the renal complex, the pericardium, the sexual apparatus, the walls of the cerebral arteries, the filaments of the columellar muscles, the wall of the abdomen, and the tentacle retractor muscles. Rhythmic neuron activity led to the development of marked facilitation and long-term potentiation of synaptic potentials. The possible significance of the multiple neuroeffector connections of giant neurons is discussed.

Molecular and functional differences in voltage-activated sodium currents between GABA projection neurons and dopamine neurons in the substantia nigra

OpenAIRE

Ding, Shengyuan; Wei, Wei; Zhou, Fu-Ming

2011-01-01

GABA projection neurons (GABA neurons) in the substantia nigra pars reticulata (SNr) and dopamine projection neurons (DA neurons) in substantia nigra pars compacta (SNc) have strikingly different firing properties. SNc DA neurons fire low-frequency, long-duration spikes, whereas SNr GABA neurons fire high-frequency, short-duration spikes. Since voltage-activated sodium (NaV) channels are critical to spike generation, the different firing properties raise the possibility that, compared with DA...

A memristive spiking neuron with firing rate coding

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Marina eIgnatov

2015-10-01

Full Text Available Perception, decisions, and sensations are all encoded into trains of action potentials in the brain. The relation between stimulus strength and all-or-nothing spiking of neurons is widely believed to be the basis of this coding. This initiated the development of spiking neuron models; one of today's most powerful conceptual tool for the analysis and emulation of neural dynamics. The success of electronic circuit models and their physical realization within silicon field-effect transistor circuits lead to elegant technical approaches. Recently, the spectrum of electronic devices for neural computing has been extended by memristive devices, mainly used to emulate static synaptic functionality. Their capabilities for emulations of neural activity were recently demonstrated using a memristive neuristor circuit, while a memristive neuron circuit has so far been elusive. Here, a spiking neuron model is experimentally realized in a compact circuit comprising memristive and memcapacitive devices based on the strongly correlated electron material vanadium dioxide (VO2 and on the chemical electromigration cell Ag/TiO2-x/Al. The circuit can emulate dynamical spiking patterns in response to an external stimulus including adaptation, which is at the heart of firing rate coding as first observed by E.D. Adrian in 1926.

Delay selection by spike-timing-dependent plasticity in recurrent networks of spiking neurons receiving oscillatory inputs.

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Robert R Kerr

Full Text Available Learning rules, such as spike-timing-dependent plasticity (STDP, change the structure of networks of neurons based on the firing activity. A network level understanding of these mechanisms can help infer how the brain learns patterns and processes information. Previous studies have shown that STDP selectively potentiates feed-forward connections that have specific axonal delays, and that this underlies behavioral functions such as sound localization in the auditory brainstem of the barn owl. In this study, we investigate how STDP leads to the selective potentiation of recurrent connections with different axonal and dendritic delays during oscillatory activity. We develop analytical models of learning with additive STDP in recurrent networks driven by oscillatory inputs, and support the results using simulations with leaky integrate-and-fire neurons. Our results show selective potentiation of connections with specific axonal delays, which depended on the input frequency. In addition, we demonstrate how this can lead to a network becoming selective in the amplitude of its oscillatory response to this frequency. We extend this model of axonal delay selection within a single recurrent network in two ways. First, we show the selective potentiation of connections with a range of both axonal and dendritic delays. Second, we show axonal delay selection between multiple groups receiving out-of-phase, oscillatory inputs. We discuss the application of these models to the formation and activation of neuronal ensembles or cell assemblies in the cortex, and also to missing fundamental pitch perception in the auditory brainstem.

Mechanisms of Winner-Take-All and Group Selection in Neuronal Spiking Networks.

Science.gov (United States)

Chen, Yanqing

2017-01-01

A major function of central nervous systems is to discriminate different categories or types of sensory input. Neuronal networks accomplish such tasks by learning different sensory maps at several stages of neural hierarchy, such that different neurons fire selectively to reflect different internal or external patterns and states. The exact mechanisms of such map formation processes in the brain are not completely understood. Here we study the mechanism by which a simple recurrent/reentrant neuronal network accomplish group selection and discrimination to different inputs in order to generate sensory maps. We describe the conditions and mechanism of transition from a rhythmic epileptic state (in which all neurons fire synchronized and indiscriminately to any input) to a winner-take-all state in which only a subset of neurons fire for a specific input. We prove an analytic condition under which a stable bump solution and a winner-take-all state can emerge from the local recurrent excitation-inhibition interactions in a three-layer spiking network with distinct excitatory and inhibitory populations, and demonstrate the importance of surround inhibitory connection topology on the stability of dynamic patterns in spiking neural network.

A critical period for experience-dependent remodeling of adult-born neuron connectivity.

Science.gov (United States)

Bergami, Matteo; Masserdotti, Giacomo; Temprana, Silvio G; Motori, Elisa; Eriksson, Therese M; Göbel, Jana; Yang, Sung Min; Conzelmann, Karl-Klaus; Schinder, Alejandro F; Götz, Magdalena; Berninger, Benedikt

2015-02-18

Neurogenesis in the dentate gyrus (DG) of the adult hippocampus is a process regulated by experience. To understand whether experience also modifies the connectivity of new neurons, we systematically investigated changes in their innervation following environmental enrichment (EE). We found that EE exposure between 2-6 weeks following neuron birth, rather than merely increasing the number of new neurons, profoundly affected their pattern of monosynaptic inputs. Both local innervation by interneurons and to even greater degree long-distance innervation by cortical neurons were markedly enhanced. Furthermore, following EE, new neurons received inputs from CA3 and CA1 inhibitory neurons that were rarely observed under control conditions. While EE-induced changes in inhibitory innervation were largely transient, cortical innervation remained increased after returning animals to control conditions. Our findings demonstrate an unprecedented experience-dependent reorganization of connections impinging onto adult-born neurons, which is likely to have important impact on their contribution to hippocampal information processing. Copyright © 2015 Elsevier Inc. All rights reserved.

Stable Control of Firing Rate Mean and Variance by Dual Homeostatic Mechanisms.

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Cannon, Jonathan; Miller, Paul

2017-12-01

Homeostatic processes that provide negative feedback to regulate neuronal firing rates are essential for normal brain function. Indeed, multiple parameters of individual neurons, including the scale of afferent synapse strengths and the densities of specific ion channels, have been observed to change on homeostatic time scales to oppose the effects of chronic changes in synaptic input. This raises the question of whether these processes are controlled by a single slow feedback variable or multiple slow variables. A single homeostatic process providing negative feedback to a neuron's firing rate naturally maintains a stable homeostatic equilibrium with a characteristic mean firing rate; but the conditions under which multiple slow feedbacks produce a stable homeostatic equilibrium have not yet been explored. Here we study a highly general model of homeostatic firing rate control in which two slow variables provide negative feedback to drive a firing rate toward two different target rates. Using dynamical systems techniques, we show that such a control system can be used to stably maintain a neuron's characteristic firing rate mean and variance in the face of perturbations, and we derive conditions under which this happens. We also derive expressions that clarify the relationship between the homeostatic firing rate targets and the resulting stable firing rate mean and variance. We provide specific examples of neuronal systems that can be effectively regulated by dual homeostasis. One of these examples is a recurrent excitatory network, which a dual feedback system can robustly tune to serve as an integrator.

Interaction of NMDA receptor and pacemaking mechanisms in the midbrain dopaminergic neuron.

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Joon Ha

Full Text Available Dopamine neurotransmission has been found to play a role in addictive behavior and is altered in psychiatric disorders. Dopaminergic (DA neurons display two functionally distinct modes of electrophysiological activity: low- and high-frequency firing. A puzzling feature of the DA neuron is the following combination of its responses: N-methyl-D-aspartate receptor (NMDAR activation evokes high-frequency firing, whereas other tonic excitatory stimuli (α-amino-3-hydroxyl-5-methyl-4-isoxazolepropionate receptor (AMPAR activation or applied depolarization block firing instead. We suggest a new computational model that reproduces this combination of responses and explains recent experimental data. Namely, somatic NMDAR stimulation evokes high-frequency firing and is more effective than distal dendritic stimulation. We further reduce the model to a single compartment and analyze the mechanism of the distinct high-frequency response to NMDAR activation vs. other stimuli. Standard nullcline analysis shows that the mechanism is based on a decrease in the amplitude of calcium oscillations. The analysis confirms that the nonlinear voltage dependence provided by the magnesium block of the NMDAR determine its capacity to elevate the firing frequency. We further predict that the moderate slope of the voltage dependence plays the central role in the frequency elevation. Additionally, we suggest a repolarizing current that sustains calcium-independent firing or firing in the absence of calcium-dependent repolarizing currents. We predict that the ether-a-go-go current (ERG, which has been observed in the DA neuron, is the best fit for this critical role. We show that a calcium-dependent and a calcium-independent oscillatory mechanisms form a structure of interlocked negative feedback loops in the DA neuron. The structure connects research of DA neuron firing with circadian biology and determines common minimal models for investigation of robustness of oscillations

Performance assessment on high strength steel endplate connections after fire

NARCIS (Netherlands)

Qiang, X.; Wu, N.; Jiang, X.; Bijlaard, F.S.K.; Kolstein, M.H.

2017-01-01

Purpose – This study aims to reveal more information and understanding on performance and failure mechanisms of high strength steel endplate connections after fire. Design/methodology/approach – An experimental and numerical study on seven endplate connections after

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.

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.

Information in a Network of Neuronal Cells: Effect of Cell Density and Short-Term Depression

KAUST Repository

Onesto, Valentina; Cosentino, Carlo; Di Fabrizio, Enzo M.; Cesarelli, Mario; Amato, Francesco; Gentile, Francesco

2016-01-01

Neurons are specialized, electrically excitable cells which use electrical to chemical signals to transmit and elaborate information. Understanding how the cooperation of a great many of neurons in a grid may modify and perhaps improve the information quality, in contrast to few neurons in isolation, is critical for the rational design of cell-materials interfaces for applications in regenerative medicine, tissue engineering, and personalized lab-on-a-chips. In the present paper, we couple an integrate-and-fire model with information theory variables to analyse the extent of information in a network of nerve cells. We provide an estimate of the information in the network in bits as a function of cell density and short-term depression time. In the model, neurons are connected through a Delaunay triangulation of not-intersecting edges; in doing so, the number of connecting synapses per neuron is approximately constant to reproduce the early time of network development in planar neural cell cultures. In simulations where the number of nodes is varied, we observe an optimal value of cell density for which information in the grid is maximized. In simulations in which the posttransmission latency time is varied, we observe that information increases as the latency time decreases and, for specific configurations of the grid, it is largely enhanced in a resonance effect.

Information in a Network of Neuronal Cells: Effect of Cell Density and Short-Term Depression

KAUST Repository

Onesto, Valentina

2016-05-10

Neurons are specialized, electrically excitable cells which use electrical to chemical signals to transmit and elaborate information. Understanding how the cooperation of a great many of neurons in a grid may modify and perhaps improve the information quality, in contrast to few neurons in isolation, is critical for the rational design of cell-materials interfaces for applications in regenerative medicine, tissue engineering, and personalized lab-on-a-chips. In the present paper, we couple an integrate-and-fire model with information theory variables to analyse the extent of information in a network of nerve cells. We provide an estimate of the information in the network in bits as a function of cell density and short-term depression time. In the model, neurons are connected through a Delaunay triangulation of not-intersecting edges; in doing so, the number of connecting synapses per neuron is approximately constant to reproduce the early time of network development in planar neural cell cultures. In simulations where the number of nodes is varied, we observe an optimal value of cell density for which information in the grid is maximized. In simulations in which the posttransmission latency time is varied, we observe that information increases as the latency time decreases and, for specific configurations of the grid, it is largely enhanced in a resonance effect.

Information in a Network of Neuronal Cells: Effect of Cell Density and Short-Term Depression

Directory of Open Access Journals (Sweden)

Valentina Onesto

2016-01-01

Full Text Available Neurons are specialized, electrically excitable cells which use electrical to chemical signals to transmit and elaborate information. Understanding how the cooperation of a great many of neurons in a grid may modify and perhaps improve the information quality, in contrast to few neurons in isolation, is critical for the rational design of cell-materials interfaces for applications in regenerative medicine, tissue engineering, and personalized lab-on-a-chips. In the present paper, we couple an integrate-and-fire model with information theory variables to analyse the extent of information in a network of nerve cells. We provide an estimate of the information in the network in bits as a function of cell density and short-term depression time. In the model, neurons are connected through a Delaunay triangulation of not-intersecting edges; in doing so, the number of connecting synapses per neuron is approximately constant to reproduce the early time of network development in planar neural cell cultures. In simulations where the number of nodes is varied, we observe an optimal value of cell density for which information in the grid is maximized. In simulations in which the posttransmission latency time is varied, we observe that information increases as the latency time decreases and, for specific configurations of the grid, it is largely enhanced in a resonance effect.

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.

Human Nav1.8: enhanced persistent and ramp currents contribute to distinct firing properties of human DRG neurons

Science.gov (United States)

Han, Chongyang; Estacion, Mark; Huang, Jianying; Vasylyev, Dymtro; Zhao, Peng; Dib-Hajj, Sulayman D.

2015-01-01

Although species-specific differences in ion channel properties are well-documented, little has been known about the properties of the human Nav1.8 channel, an important contributor to pain signaling. Here we show, using techniques that include voltage clamp, current clamp, and dynamic clamp in dorsal root ganglion (DRG) neurons, that human Nav1.8 channels display slower inactivation kinetics and produce larger persistent current and ramp current than previously reported in other species. DRG neurons expressing human Nav1.8 channels unexpectedly produce significantly longer-lasting action potentials, including action potentials with half-widths in some cells >10 ms, and increased firing frequency compared with the narrower and usually single action potentials generated by DRG neurons expressing rat Nav1.8 channels. We also show that native human DRG neurons recapitulate these properties of Nav1.8 current and the long-lasting action potentials. Together, our results demonstrate strikingly distinct properties of human Nav1.8, which contribute to the firing properties of human DRG neurons. PMID:25787950

The human and fire connection

Science.gov (United States)

Theresa B. Jain

2014-01-01

We refer to fire as a natural disturbance, but unlike other disturbances such as forest insects and diseases, fire has had an intimate relationship with humans. Fire facilitated human evolution over two million years ago when our ancestors began to use fire to cook. Fire empowered our furbearers to adapt to cold climates, allowing humans to disperse and settle into...

Democratic reinforcement: A principle for brain function

International Nuclear Information System (INIS)

Stassinopoulos, D.; Bak, P.

1995-01-01

We introduce a simple ''toy'' brain model. The model consists of a set of randomly connected, or layered integrate-and-fire neurons. Inputs to and outputs from the environment are connected randomly to subsets of neurons. The connections between firing neurons are strengthened or weakened according to whether the action was successful or not. Unlike previous reinforcement learning algorithms, the feedback from the environment is democratic: it affects all neurons in the same way, irrespective of their position in the network and independent of the output signal. Thus no unrealistic back propagation or other external computation is needed. This is accomplished by a global threshold regulation which allows the system to self-organize into a highly susceptible, possibly ''critical'' state with low activity and sparse connections between firing neurons. The low activity permits memory in quiescent areas to be conserved since only firing neurons are modified when new information is being taught

Complete and phase synchronization in a heterogeneous small-world neuronal network

International Nuclear Information System (INIS)

Fang, Han; Qi-Shao, Lu; Quan-Bao, Ji; Marian, Wiercigroch

2009-01-01

Synchronous firing of neurons is thought to be important for information communication in neuronal networks. This paper investigates the complete and phase synchronization in a heterogeneous small-world chaotic Hindmarsh–Rose neuronal network. The effects of various network parameters on synchronization behaviour are discussed with some biological explanations. Complete synchronization of small-world neuronal networks is studied theoretically by the master stability function method. It is shown that the coupling strength necessary for complete or phase synchronization decreases with the neuron number, the node degree and the connection density are increased. The effect of heterogeneity of neuronal networks is also considered and it is found that the network heterogeneity has an adverse effect on synchrony. (general)

Bayesian population decoding of spiking neurons.

Science.gov (United States)

Gerwinn, Sebastian; Macke, Jakob; Bethge, Matthias

2009-01-01

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

Bayesian population decoding of spiking neurons

Directory of Open Access Journals (Sweden)

Sebastian Gerwinn

2009-10-01

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

Subfornical organ neurons integrate cardiovascular and metabolic signals.

Science.gov (United States)

Cancelliere, Nicole M; Ferguson, Alastair V

2017-02-01

The subfornical organ (SFO) is a critical circumventricular organ involved in the control of cardiovascular and metabolic homeostasis. Despite the plethora of circulating signals continuously sensed by the SFO, studies investigating how these signals are integrated are lacking. In this study, we use patch-clamp techniques to investigate how the traditionally classified "cardiovascular" hormone ANG II, "metabolic" hormone CCK and "metabolic" signal glucose interact and are integrated in the SFO. Sequential bath application of CCK (10 nM) and ANG (10 nM) onto dissociated SFO neurons revealed that 63% of responsive SFO neurons depolarized to both CCK and ANG; 25% depolarized to ANG only; and 12% hyperpolarized to CCK only. We next investigated the effects of glucose by incubating and recording neurons in either hypoglycemic, normoglycemic, or hyperglycemic conditions and comparing the proportions of responses to ANG ( n = 55) or CCK ( n = 83) application in each condition. A hyperglycemic environment was associated with a larger proportion of depolarizing responses to ANG ( χ 2 , P neurons excited by CCK are also excited by ANG and that glucose environment affects the responsiveness of neurons to both of these hormones, highlighting the ability of SFO neurons to integrate multiple metabolic and cardiovascular signals. These findings have important implications for this structure's role in the control of various autonomic functions during hyperglycemia. Copyright © 2017 the American Physiological Society.

Connectivity effects in the dynamic model of neural networks

International Nuclear Information System (INIS)

Choi, J; Choi, M Y; Yoon, B-G

2009-01-01

We study, via extensive Monte Carlo calculations, the effects of connectivity in the dynamic model of neural networks, to observe that the Mattis-state order parameter increases with the number of coupled neurons. Such effects appear more pronounced when the average number of connections is increased by introducing shortcuts in the network. In particular, the power spectra of the order parameter at stationarity are found to exhibit power-law behavior, depending on how the average number of connections is increased. The cluster size distribution of the 'memory-unmatched' sites also follows a power law and possesses strong correlations with the power spectra. It is further observed that the distribution of waiting times for neuron firing fits roughly to a power law, again depending on how neuronal connections are increased

Integrating fire management analysis into land management planning

Science.gov (United States)

Thomas J. Mills

1983-01-01

The analysis of alternative fire management programs should be integrated into the land and resource management planning process, but a single fire management analysis model cannot meet all planning needs. Therefore, a set of simulation models that are analytically separate from integrated land management planning models are required. The design of four levels of fire...

Inhibitory coherence in a heterogeneous population of subthreshold and suprathreshold type-I neurons

International Nuclear Information System (INIS)

Kim, Sang-Yoon; Hong, Duk-Geun; Kim, Jean; Lim, Woochang

2012-01-01

We study inhibitory coherence (i.e. collective coherence by synaptic inhibition) in a population of globally coupled type-I neurons, which can fire at arbitrarily low frequency. No inhibitory coherence is observed in a homogeneous population composed of only subthreshold neurons, which exhibit noise-induced firings. In addition to subthreshold neurons, there exist spontaneously firing suprathreshold neurons in a noisy environment of a real brain. To take into consideration the effect of suprathreshold neurons on inhibitory coherence, we consider a heterogeneous population of subthreshold and suprathreshold neurons and investigate the inhibitory coherence by increasing the fraction of suprathreshold neurons P supra . As P supra passes a threshold P* supra , suprathreshold neurons begin to synchronize and play the role of coherent inhibitors for the emergence of inhibitory coherence. Thus, regularly oscillating population-averaged global potential appears for P supra > P* supra . For this coherent case, suprathreshold neurons exhibit sparse spike synchronization (i.e. individual potentials of suprathreshold neurons consist of coherent sparse spikings and coherent subthreshold small-amplitude hoppings). By virtue of their coherent inhibition, sparsely synchronized suprathreshold neurons suppress the noisy activity of subthreshold neurons. Thus, subthreshold neurons exhibit hopping synchronization (i.e. only coherent subthreshold hopping oscillations without spikings appear in the individual potentials of subthreshold neurons). We also characterize the inhibitory coherence in terms of the ‘statistical-mechanical’ spike-based and correlation-based measures, which quantify the average contributions of the microscopic individual spikes and individual potentials to the macroscopic global potential. Finally, the effect of sparse randomness of synaptic connectivity on the inhibitory coherence is briefly discussed. (paper)

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

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Schwalger, Tilo; Deger, Moritz; Gerstner, Wulfram

2017-04-01

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

Time Resolution Dependence of Information Measures for Spiking Neurons: Scaling and Universality

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James P Crutchfield

2015-08-01

Full Text Available The mutual information between stimulus and spike-train response is commonly used to monitor neural coding efficiency, but neuronal computation broadly conceived requires more refined and targeted information measures of input-output joint processes. A first step towards that larger goal is todevelop information measures for individual output processes, including information generation (entropy rate, stored information (statisticalcomplexity, predictable information (excess entropy, and active information accumulation (bound information rate. We calculate these for spike trains generated by a variety of noise-driven integrate-and-fire neurons as a function of time resolution and for alternating renewal processes. We show that their time-resolution dependence reveals coarse-grained structural properties of interspike interval statistics; e.g., $tau$-entropy rates that diverge less quickly than the firing rate indicate interspike interval correlations. We also find evidence that the excess entropy and regularized statistical complexity of different types of integrate-and-fire neurons are universal in the continuous-time limit in the sense that they do not depend on mechanism details. This suggests a surprising simplicity in the spike trains generated by these model neurons. Interestingly, neurons with gamma-distributed ISIs and neurons whose spike trains are alternating renewal processes do not fall into the same universality class. These results lead to two conclusions. First, the dependence of information measures on time resolution reveals mechanistic details about spike train generation. Second, information measures can be used as model selection tools for analyzing spike train processes.

Metabolic sensing neurons and the control of energy homeostasis.

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Levin, Barry E

2006-11-30

The brain and periphery carry on a constant conversation; the periphery informs the brain about its metabolic needs and the brain provides for these needs through its control of somatomotor, autonomic and neurohumoral pathways involved in energy intake, expenditure and storage. Metabolic sensing neurons are the integrators of a variety of metabolic, humoral and neural inputs from the periphery. Such neurons, originally called "glucosensing", also respond to fatty acids, hormones and metabolites from the periphery. They are integrated within neural pathways involved in the regulation of energy homeostasis. Unlike most neurons, they utilize glucose and other metabolites as signaling molecules to regulate their membrane potential and firing rate. For glucosensing neurons, glucokinase acts as the rate-limiting step in glucosensing while the pathways that mediate responses to metabolites like lactate, ketone bodies and fatty acids are less well characterized. Many metabolic sensing neurons also respond to insulin and leptin and other peripheral hormones and receive neural inputs from peripheral organs. Each set of afferent signals arrives with different temporal profiles and by different routes and these inputs are summated at the level of the membrane potential to produce a given neural firing pattern. In some obese individuals, the relative sensitivity of metabolic sensing neurons to various peripheral inputs is genetically reduced. This may provide one mechanism underlying their propensity to become obese when exposed to diets high in fat and caloric density. Thus, metabolic sensing neurons may provide a potential therapeutic target for the treatment of obesity.

Grid connected integrated community energy system. Phase II: final stage 2 report. Preliminary design of cogeneration plant

Energy Technology Data Exchange (ETDEWEB)

1978-03-22

The preliminary design of a dual-purpose power plant to be located on the University of Minnesota is described. This coal-fired plant will produce steam and electric power for a grid-connected Integrated Community Energy System. (LCL)

Human Na(v)1.8: enhanced persistent and ramp currents contribute to distinct firing properties of human DRG neurons.

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Han, Chongyang; Estacion, Mark; Huang, Jianying; Vasylyev, Dymtro; Zhao, Peng; Dib-Hajj, Sulayman D; Waxman, Stephen G

2015-05-01

Although species-specific differences in ion channel properties are well-documented, little has been known about the properties of the human Nav1.8 channel, an important contributor to pain signaling. Here we show, using techniques that include voltage clamp, current clamp, and dynamic clamp in dorsal root ganglion (DRG) neurons, that human Na(v)1.8 channels display slower inactivation kinetics and produce larger persistent current and ramp current than previously reported in other species. DRG neurons expressing human Na(v)1.8 channels unexpectedly produce significantly longer-lasting action potentials, including action potentials with half-widths in some cells >10 ms, and increased firing frequency compared with the narrower and usually single action potentials generated by DRG neurons expressing rat Na(v)1.8 channels. We also show that native human DRG neurons recapitulate these properties of Na(v)1.8 current and the long-lasting action potentials. Together, our results demonstrate strikingly distinct properties of human Na(v)1.8, which contribute to the firing properties of human DRG neurons.

Burst firing enhances neural output correlation

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Ho Ka eChan

2016-05-01

Full Text Available Neurons communicate and transmit information predominantly through spikes. Given that experimentally observed neural spike trains in a variety of brain areas can be highly correlated, it is important to investigate how neurons process correlated inputs. Most previous work in this area studied the problem of correlation transfer analytically by making significant simplifications on neural dynamics. Temporal correlation between inputs that arises from synaptic filtering, for instance, is often ignored when assuming that an input spike can at most generate one output spike. Through numerical simulations of a pair of leaky integrate-and-fire (LIF neurons receiving correlated inputs, we demonstrate that neurons in the presence of synaptic filtering by slow synapses exhibit strong output correlations. We then show that burst firing plays a central role in enhancing output correlations, which can explain the above-mentioned observation because synaptic filtering induces bursting. The observed changes of correlations are mostly on a long time scale. Our results suggest that other features affecting the prevalence of neural burst firing in biological neurons, e.g., adaptive spiking mechanisms, may play an important role in modulating the overall level of correlations in neural networks.

Algorithm FIRE-Feynman Integral REduction

International Nuclear Information System (INIS)

Smirnov, A.V.

2008-01-01

The recently developed algorithm FIRE performs the reduction of Feynman integrals to master integrals. It is based on a number of strategies, such as applying the Laporta algorithm, the s-bases algorithm, region-bases and integrating explicitly over loop momenta when possible. Currently it is being used in complicated three-loop calculations.

Ketogenic diet prevents neuronal firing increase within the substantia nigra during pentylenetetrazole-induced seizure in rats.

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Viggiano, Andrea; Stoddard, Madison; Pisano, Simone; Operto, Francesca Felicia; Iovane, Valentina; Monda, Marcellino; Coppola, Giangennaro

2016-07-01

The mechanism responsible for the anti-seizure effect of ketogenic diets is poorly understood. Because the substantia nigra pars reticulata (SNr) is a "gate" center for seizures, the aim of the present experiment was to evaluate if a ketogenic diet modifies the neuronal response of this nucleus when a seizure-inducing drug is administered in rats. Two groups of rats were given a standard diet (group 1) or a ketogenic diet (group 2) for four weeks, then the threshold for seizure induction and the firing rate of putative GABAergic neurons within the SNr were evaluated with progressive infusion of pentylenetetrazole under general anesthesia. The results demonstrated that the ketogenic diet abolished the correlation between the firing rate response of SNr-neurons and the seizure-threshold. This result suggests that the anti-seizure effect of ketogenic diets can be due to a decrease in reactivity of GABAergic SNr-neurons. Copyright © 2016 Elsevier Inc. All rights reserved.

Reliable activation of immature neurons in the adult hippocampus.

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Lucas A Mongiat

Full Text Available Neurons born in the adult dentate gyrus develop, mature, and connect over a long interval that can last from six to eight weeks. It has been proposed that, during this period, developing neurons play a relevant role in hippocampal signal processing owing to their distinctive electrical properties. However, it has remained unknown whether immature neurons can be recruited into a network before synaptic and functional maturity have been achieved. To address this question, we used retroviral expression of green fluorescent protein to identify developing granule cells of the adult mouse hippocampus and investigate the balance of afferent excitation, intrinsic excitability, and firing behavior by patch clamp recordings in acute slices. We found that glutamatergic inputs onto young neurons are significantly weaker than those of mature cells, yet stimulation of cortical excitatory axons elicits a similar spiking probability in neurons at either developmental stage. Young neurons are highly efficient in transducing ionic currents into membrane depolarization due to their high input resistance, which decreases substantially in mature neurons as the inward rectifier potassium (Kir conductance increases. Pharmacological blockade of Kir channels in mature neurons mimics the high excitability characteristic of young neurons. Conversely, Kir overexpression induces mature-like firing properties in young neurons. Therefore, the differences in excitatory drive of young and mature neurons are compensated by changes in membrane excitability that render an equalized firing activity. These observations demonstrate that the adult hippocampus continuously generates a population of highly excitable young neurons capable of information processing.

Dentate network activity is necessary for spatial working memory by supporting CA3 sharp-wave ripple generation and prospective firing of CA3 neurons.

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Sasaki, Takuya; Piatti, Verónica C; Hwaun, Ernie; Ahmadi, Siavash; Lisman, John E; Leutgeb, Stefan; Leutgeb, Jill K

2018-02-01

Complex spatial working memory tasks have been shown to require both hippocampal sharp-wave ripple (SWR) activity and dentate gyrus (DG) neuronal activity. We therefore asked whether DG inputs to CA3 contribute to spatial working memory by promoting SWR generation. Recordings from DG and CA3 while rats performed a dentate-dependent working memory task on an eight-arm radial maze revealed that the activity of dentate neurons and the incidence rate of SWRs both increased during reward consumption. We then found reduced reward-related CA3 SWR generation without direct input from dentate granule neurons. Furthermore, CA3 cells with place fields in not-yet-visited arms preferentially fired during SWRs at reward locations, and these prospective CA3 firing patterns were more pronounced for correct trials and were dentate-dependent. These results indicate that coordination of CA3 neuronal activity patterns by DG is necessary for the generation of neuronal firing patterns that support goal-directed behavior and memory.

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

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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.

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

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Liyuan Zhang

2017-07-01

Full Text Available 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.

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.

Diverse coupling of neurons to populations in sensory cortex.

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Okun, Michael; Steinmetz, Nicholas; Cossell, Lee; Iacaruso, M Florencia; Ko, Ho; Barthó, Péter; Moore, Tirin; Hofer, Sonja B; Mrsic-Flogel, Thomas D; Carandini, Matteo; Harris, Kenneth D

2015-05-28

A large population of neurons can, in principle, produce an astronomical number of distinct firing patterns. In cortex, however, these patterns lie in a space of lower dimension, as if individual neurons were "obedient members of a huge orchestra". Here we use recordings from the visual cortex of mouse (Mus musculus) and monkey (Macaca mulatta) to investigate the relationship between individual neurons and the population, and to establish the underlying circuit mechanisms. We show that neighbouring neurons can differ in their coupling to the overall firing of the population, ranging from strongly coupled 'choristers' to weakly coupled 'soloists'. Population coupling is largely independent of sensory preferences, and it is a fixed cellular attribute, invariant to stimulus conditions. Neurons with high population coupling are more strongly affected by non-sensory behavioural variables such as motor intention. Population coupling reflects a causal relationship, predicting the response of a neuron to optogenetically driven increases in local activity. Moreover, population coupling indicates synaptic connectivity; the population coupling of a neuron, measured in vivo, predicted subsequent in vitro estimates of the number of synapses received from its neighbours. Finally, population coupling provides a compact summary of population activity; knowledge of the population couplings of n neurons predicts a substantial portion of their n(2) pairwise correlations. Population coupling therefore represents a novel, simple measure that characterizes the relationship of each neuron to a larger population, explaining seemingly complex network firing patterns in terms of basic circuit variables.

Inducing repetitive action potential firing in neurons via synthesized photoresponsive nanoscale cellular prostheses.

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Lu, Siyuan; Madhukar, Anupam

2013-02-01

Recently we reported an analysis that examined the potential of synthesized photovoltaic functional abiotic nanosystems (PVFANs) to modulate membrane potential and activate action potential firing in neurons. Here we extend the analysis to delineate the requirements on the electronic energy levels and the attendant photophysical properties of the PVFANs to induce repetitive action potential under continuous light, a capability essential for the proposed potential application of PVFANs as retinal cellular prostheses to compensate for loss of photoreceptors. We find that repetitive action potential firing demands two basic characteristics in the electronic response of the PVFANs: an exponential dependence of the PVFAN excited state decay rate on the membrane potential and a three-state system such that, following photon absorption, the electron decay from the excited state to the ground state is via intermediate state(s) whose lifetime is comparable to the refractory time following an action potential. In this study, the potential of synthetic photovoltaic functional abiotic nanosystems (PVFANs) is examined under continuous light to modulate membrane potential and activate action potential firing in neurons with the proposed potential application of PVFANs as retinal cellular prostheses. Copyright © 2013 Elsevier Inc. All rights reserved.

Analysis of connectivity map: Control to glutamate injured and phenobarbital treated neuronal network

Science.gov (United States)

Kamal, Hassan; Kanhirodan, Rajan; Srinivas, Kalyan V.; Sikdar, Sujit K.

2010-04-01

We study the responses of a cultured neural network when it is exposed to epileptogenesis glutamate injury causing epilepsy and subsequent treatment with phenobarbital by constructing connectivity map of neurons using correlation matrix. This study is particularly useful in understanding the pharmaceutical drug induced changes in the neuronal network properties with insights into changes at the systems biology level.

Dynamics and spike trains statistics in conductance-based integrate-and-fire neural networks with chemical and electric synapses

International Nuclear Information System (INIS)

Cofré, Rodrigo; Cessac, Bruno

2013-01-01

We investigate the effect of electric synapses (gap junctions) on collective neuronal dynamics and spike statistics in a conductance-based integrate-and-fire neural network, driven by Brownian noise, where conductances depend upon spike history. We compute explicitly the time evolution operator and show that, given the spike-history of the network and the membrane potentials at a given time, the further dynamical evolution can be written in a closed form. We show that spike train statistics is described by a Gibbs distribution whose potential can be approximated with an explicit formula, when the noise is weak. This potential form encompasses existing models for spike trains statistics analysis such as maximum entropy models or generalized linear models (GLM). We also discuss the different types of correlations: those induced by a shared stimulus and those induced by neurons interactions

On the number of neurons and time scale of integration underlying the formation of percepts in the brain.

Science.gov (United States)

Wohrer, Adrien; Machens, Christian K

2015-03-01

All of our perceptual experiences arise from the activity of neural populations. Here we study the formation of such percepts under the assumption that they emerge from a linear readout, i.e., a weighted sum of the neurons' firing rates. We show that this assumption constrains the trial-to-trial covariance structure of neural activities and animal behavior. The predicted covariance structure depends on the readout parameters, and in particular on the temporal integration window w and typical number of neurons K used in the formation of the percept. Using these predictions, we show how to infer the readout parameters from joint measurements of a subject's behavior and neural activities. We consider three such scenarios: (1) recordings from the complete neural population, (2) recordings of neuronal sub-ensembles whose size exceeds K, and (3) recordings of neuronal sub-ensembles that are smaller than K. Using theoretical arguments and artificially generated data, we show that the first two scenarios allow us to recover the typical spatial and temporal scales of the readout. In the third scenario, we show that the readout parameters can only be recovered by making additional assumptions about the structure of the full population activity. Our work provides the first thorough interpretation of (feed-forward) percept formation from a population of sensory neurons. We discuss applications to experimental recordings in classic sensory decision-making tasks, which will hopefully provide new insights into the nature of perceptual integration.

All-optical functional synaptic connectivity mapping in acute brain slices using the calcium integrator CaMPARI.

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Zolnik, Timothy A; Sha, Fern; Johenning, Friedrich W; Schreiter, Eric R; Looger, Loren L; Larkum, Matthew E; Sachdev, Robert N S

2017-03-01

The genetically encoded fluorescent calcium integrator calcium-modulated photoactivatable ratiobetric integrator (CaMPARI) reports calcium influx induced by synaptic and neural activity. Its fluorescence is converted from green to red in the presence of violet light and calcium. The rate of conversion - the sensitivity to activity - is tunable and depends on the intensity of violet light. Synaptic activity and action potentials can independently initiate significant CaMPARI conversion. The level of conversion by subthreshold synaptic inputs is correlated to the strength of input, enabling optical readout of relative synaptic strength. When combined with optogenetic activation of defined presynaptic neurons, CaMPARI provides an all-optical method to map synaptic connectivity. The calcium-modulated photoactivatable ratiometric integrator (CaMPARI) is a genetically encoded calcium integrator that facilitates the study of neural circuits by permanently marking cells active during user-specified temporal windows. Permanent marking enables measurement of signals from large swathes of tissue and easy correlation of activity with other structural or functional labels. One potential application of CaMPARI is labelling neurons postsynaptic to specific populations targeted for optogenetic stimulation, giving rise to all-optical functional connectivity mapping. Here, we characterized the response of CaMPARI to several common types of neuronal calcium signals in mouse acute cortical brain slices. Our experiments show that CaMPARI is effectively converted by both action potentials and subthreshold synaptic inputs, and that conversion level is correlated to synaptic strength. Importantly, we found that conversion rate can be tuned: it is linearly related to light intensity. At low photoconversion light levels CaMPARI offers a wide dynamic range due to slower conversion rate; at high light levels conversion is more rapid and more sensitive to activity. Finally, we employed Ca

Orexin/Hypocretin and Organizing Principles for a Diversity of Wake-Promoting Neurons in the Brain.

Science.gov (United States)

Schöne, Cornelia; Burdakov, Denis

2017-01-01

An enigmatic feature of behavioural state control is the rich diversity of wake-promoting neural systems. This diversity has been rationalized as 'robustness via redundancy', wherein wakefulness control is not critically dependent on one type of neuron or molecule. Studies of the brain orexin/hypocretin system challenge this view by demonstrating that wakefulness control fails upon loss of this neurotransmitter system. Since orexin neurons signal arousal need, and excite other wake-promoting neurons, their actions illuminate nonredundant principles of arousal control. Here, we suggest such principles by reviewing the orexin system from a collective viewpoint of biology, physics and engineering. Orexin peptides excite other arousal-promoting neurons (noradrenaline, histamine, serotonin, acetylcholine neurons), either by activating mixed-cation conductances or by inhibiting potassium conductances. Ohm's law predicts that these opposite conductance changes will produce opposite effects on sensitivity of neuronal excitability to current inputs, thus enabling orexin to differentially control input-output gain of its target networks. Orexin neurons also produce other transmitters, including glutamate. When orexin cells fire, glutamate-mediated downstream excitation displays temporal decay, but orexin-mediated excitation escalates, as if orexin transmission enabled arousal controllers to compute a time integral of arousal need. Since the anatomical and functional architecture of the orexin system contains negative feedback loops (e.g. orexin ➔ histamine ➔ noradrenaline/serotonin-orexin), such computations may stabilize wakefulness via integral feedback, a basic engineering strategy for set point control in uncertain environments. Such dynamic behavioural control requires several distinct wake-promoting modules, which perform nonredundant transformations of arousal signals and are connected in feedback loops.

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

Science.gov (United States)

Gerstner, Wulfram

2017-01-01

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

Mechanism for propagation of rate signals through a 10-layer feedforward neuronal network

International Nuclear Information System (INIS)

Jie, Li; Wan-Qing, Yu; Ding, Xu; Feng, Liu; Wei, Wang

2009-01-01

Using numerical simulations, we explore the mechanism for propagation of rate signals through a 10-layer feedforward network composed of Hodgkin–Huxley (HH) neurons with sparse connectivity. When white noise is afferent to the input layer, neuronal firing becomes progressively more synchronous in successive layers and synchrony is well developed in deeper layers owing to the feedforward connections between neighboring layers. The synchrony ensures the successful propagation of rate signals through the network when the synaptic conductance is weak. As the synaptic time constant τ syn varies, coherence resonance is observed in the network activity due to the intrinsic property of HH neurons. This makes the output firing rate single-peaked as a function of τ syn , suggesting that the signal propagation can be modulated by the synaptic time constant. These results are consistent with experimental results and advance our understanding of how information is processed in feedforward networks. (cross-disciplinary physics and related areas of science and technology)

The projection and synaptic organisation of NTS afferent connections with presympathetic neurons, GABA and nNOS neurons in the paraventricular nucleus of the hypothalamus

Science.gov (United States)

Affleck, V.S.; Coote, J.H.; Pyner, S.

2012-01-01

Elevated sympathetic nerve activity, strongly associated with cardiovascular disease, is partly generated from the presympathetic neurons of the paraventricular nucleus of the hypothalamus (PVN). The PVN-presympathetic neurons regulating cardiac and vasomotor sympathetic activity receive information about cardiovascular status from receptors in the heart and circulation. These receptors signal changes via afferent neurons terminating in the nucleus tractus solitarius (NTS), some of which may result in excitation or inhibition of PVN-presympathetic neurons. Understanding the anatomy and neurochemistry of NTS afferent connections within the PVN could provide important clues to the impairment in homeostasis cardiovascular control associated with disease. Transynaptic labelling has shown the presence of neuronal nitric oxide synthase (nNOS)-containing neurons and GABA interneurons that terminate on presympathetic PVN neurons any of which may be the target for NTS afferents. So far NTS connections to these diverse neuronal pools have not been demonstrated and were investigated in this study. Anterograde (biotin dextran amine – BDA) labelling of the ascending projection from the NTS and retrograde (fluorogold – FG or cholera toxin B subunit – CTB) labelling of PVN presympathetic neurons combined with immunohistochemistry for GABA and nNOS was used to identify the terminal neuronal targets of the ascending projection from the NTS. It was shown that NTS afferent terminals are apposed to either PVN-GABA interneurons or to nitric oxide producing neurons or even directly to presympathetic neurons. Furthermore, there was evidence that some NTS axons were positive for vesicular glutamate transporter 2 (vGLUT2). The data provide an anatomical basis for the different functions of cardiovascular receptors that mediate their actions via the NTS–PVN pathways. PMID:22698695

Dynamics of human subthalamic neuron phase-locking to motor and sensory cortical oscillations during movement.

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Lipski, Witold J; Wozny, Thomas A; Alhourani, Ahmad; Kondylis, Efstathios D; Turner, Robert S; Crammond, Donald J; Richardson, Robert Mark

2017-09-01

Coupled oscillatory activity recorded between sensorimotor regions of the basal ganglia-thalamocortical loop is thought to reflect information transfer relevant to movement. A neuronal firing-rate model of basal ganglia-thalamocortical circuitry, however, has dominated thinking about basal ganglia function for the past three decades, without knowledge of the relationship between basal ganglia single neuron firing and cortical population activity during movement itself. We recorded activity from 34 subthalamic nucleus (STN) neurons, simultaneously with cortical local field potentials and motor output, in 11 subjects with Parkinson's disease (PD) undergoing awake deep brain stimulator lead placement. STN firing demonstrated phase synchronization to both low- and high-beta-frequency cortical oscillations, and to the amplitude envelope of gamma oscillations, in motor cortex. We found that during movement, the magnitude of this synchronization was dynamically modulated in a phase-frequency-specific manner. Importantly, we found that phase synchronization was not correlated with changes in neuronal firing rate. Furthermore, we found that these relationships were not exclusive to motor cortex, because STN firing also demonstrated phase synchronization to both premotor and sensory cortex. The data indicate that models of basal ganglia function ultimately will need to account for the activity of populations of STN neurons that are bound in distinct functional networks with both motor and sensory cortices and code for movement parameters independent of changes in firing rate. NEW & NOTEWORTHY Current models of basal ganglia-thalamocortical networks do not adequately explain simple motor functions, let alone dysfunction in movement disorders. Our findings provide data that inform models of human basal ganglia function by demonstrating how movement is encoded by networks of subthalamic nucleus (STN) neurons via dynamic phase synchronization with cortex. The data also

Optogenetic identification of hypothalamic orexin neuron projections to paraventricular spinally projecting neurons.

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Dergacheva, Olga; Yamanaka, Akihiro; Schwartz, Alan R; Polotsky, Vsevolod Y; Mendelowitz, David

2017-04-01

Orexin neurons, and activation of orexin receptors, are generally thought to be sympathoexcitatory; however, the functional connectivity between orexin neurons and a likely sympathetic target, the hypothalamic spinally projecting neurons (SPNs) in the paraventricular nucleus of the hypothalamus (PVN) has not been established. To test the hypothesis that orexin neurons project directly to SPNs in the PVN, channelrhodopsin-2 (ChR2) was selectively expressed in orexin neurons to enable photoactivation of ChR2-expressing fibers while examining evoked postsynaptic currents in SPNs in rat hypothalamic slices. Selective photoactivation of orexin fibers elicited short-latency postsynaptic currents in all SPNs tested ( n = 34). These light-triggered responses were heterogeneous, with a majority being excitatory glutamatergic responses (59%) and a minority of inhibitory GABAergic (35%) and mixed glutamatergic and GABAergic currents (6%). Both glutamatergic and GABAergic responses were present in the presence of tetrodotoxin and 4-aminopyridine, suggesting a monosynaptic connection between orexin neurons and SPNs. In addition to generating postsynaptic responses, photostimulation facilitated action potential firing in SPNs (current clamp configuration). Glutamatergic, but not GABAergic, postsynaptic currents were diminished by application of the orexin receptor antagonist almorexant, indicating orexin release facilitates glutamatergic neurotransmission in this pathway. This work identifies a neuronal circuit by which orexin neurons likely exert sympathoexcitatory control of cardiovascular function. NEW & NOTEWORTHY This is the first study to establish, using innovative optogenetic approaches in a transgenic rat model, that there are robust heterogeneous projections from orexin neurons to paraventricular spinally projecting neurons, including excitatory glutamatergic and inhibitory GABAergic neurotransmission. Endogenous orexin release modulates glutamatergic, but not

Human embryonic stem cell-derived neurons adopt and regulate the activity of an established neural network

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Weick, Jason P.; Liu, Yan; Zhang, Su-Chun

2011-01-01

Whether hESC-derived neurons can fully integrate with and functionally regulate an existing neural network remains unknown. Here, we demonstrate that hESC-derived neurons receive unitary postsynaptic currents both in vitro and in vivo and adopt the rhythmic firing behavior of mouse cortical networks via synaptic integration. Optical stimulation of hESC-derived neurons expressing Channelrhodopsin-2 elicited both inhibitory and excitatory postsynaptic currents and triggered network bursting in mouse neurons. Furthermore, light stimulation of hESC-derived neurons transplanted to the hippocampus of adult mice triggered postsynaptic currents in host pyramidal neurons in acute slice preparations. Thus, hESC-derived neurons can participate in and modulate neural network activity through functional synaptic integration, suggesting they are capable of contributing to neural network information processing both in vitro and in vivo. PMID:22106298

A self-resetting spiking phase-change neuron

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

2018-05-01

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

A self-resetting spiking phase-change neuron.

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

2018-05-11

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

Spiking neuron devices consisting of single-flux-quantum circuits

International Nuclear Information System (INIS)

Hirose, Tetsuya; Asai, Tetsuya; Amemiya, Yoshihito

2006-01-01

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

A Computational Model of the SC Multisensory Neurons: Integrative Capabilities, Maturation, and Plasticity

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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

High Content Analysis of Hippocampal Neuron-Astrocyte Co-cultures Shows a Positive Effect of Fortasyn Connect on Neuronal Survival and Postsynaptic Maturation.

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van Deijk, Anne-Lieke F; Broersen, Laus M; Verkuyl, J Martin; Smit, August B; Verheijen, Mark H G

2017-01-01

Neuronal and synaptic membranes are composed of a phospholipid bilayer. Supplementation with dietary precursors for phospholipid synthesis -docosahexaenoic acid (DHA), uridine and choline- has been shown to increase neurite outgrowth and synaptogenesis both in vivo and in vitro . A role for multi-nutrient intervention with specific precursors and cofactors has recently emerged in early Alzheimer's disease, which is characterized by decreased synapse numbers in the hippocampus. Moreover, the medical food Souvenaid, containing the specific nutrient combination Fortasyn Connect (FC), improves memory performance in early Alzheimer's disease patients, possibly via maintaining brain connectivity. This suggests an effect of FC on synapses, but the underlying cellular mechanism is not fully understood. Therefore, we investigated the effect of FC (consisting of DHA, eicosapentaenoic acid (EPA), uridine, choline, phospholipids, folic acid, vitamins B12, B6, C and E, and selenium), on synaptogenesis by supplementing it to primary neuron-astrocyte co-cultures, a cellular model that mimics metabolic dependencies in the brain. We measured neuronal developmental processes using high content screening in an automated manner, including neuronal survival, neurite morphology, as well as the formation and maturation of synapses. Here, we show that FC supplementation resulted in increased numbers of neurons without affecting astrocyte number. Furthermore, FC increased postsynaptic PSD95 levels in both immature and mature synapses. These findings suggest that supplementation with FC to neuron-astrocyte co-cultures increased both neuronal survival and the maturation of postsynaptic terminals, which might aid the functional interpretation of FC-based intervention strategies in neurological diseases characterized by neuronal loss and impaired synaptic functioning.

High Content Analysis of Hippocampal Neuron-Astrocyte Co-cultures Shows a Positive Effect of Fortasyn Connect on Neuronal Survival and Postsynaptic Maturation

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Anne-Lieke F. van Deijk

2017-08-01

Full Text Available Neuronal and synaptic membranes are composed of a phospholipid bilayer. Supplementation with dietary precursors for phospholipid synthesis –docosahexaenoic acid (DHA, uridine and choline– has been shown to increase neurite outgrowth and synaptogenesis both in vivo and in vitro. A role for multi-nutrient intervention with specific precursors and cofactors has recently emerged in early Alzheimer's disease, which is characterized by decreased synapse numbers in the hippocampus. Moreover, the medical food Souvenaid, containing the specific nutrient combination Fortasyn Connect (FC, improves memory performance in early Alzheimer's disease patients, possibly via maintaining brain connectivity. This suggests an effect of FC on synapses, but the underlying cellular mechanism is not fully understood. Therefore, we investigated the effect of FC (consisting of DHA, eicosapentaenoic acid (EPA, uridine, choline, phospholipids, folic acid, vitamins B12, B6, C and E, and selenium, on synaptogenesis by supplementing it to primary neuron-astrocyte co-cultures, a cellular model that mimics metabolic dependencies in the brain. We measured neuronal developmental processes using high content screening in an automated manner, including neuronal survival, neurite morphology, as well as the formation and maturation of synapses. Here, we show that FC supplementation resulted in increased numbers of neurons without affecting astrocyte number. Furthermore, FC increased postsynaptic PSD95 levels in both immature and mature synapses. These findings suggest that supplementation with FC to neuron-astrocyte co-cultures increased both neuronal survival and the maturation of postsynaptic terminals, which might aid the functional interpretation of FC-based intervention strategies in neurological diseases characterized by neuronal loss and impaired synaptic functioning.

Orexin-A increases the firing activity of hippocampal CA1 neurons through orexin-1 receptors.

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Chen, Xin-Yi; Chen, Lei; Du, Yi-Feng

2017-07-01

Orexins including two peptides, orexin-A and orexin-B, are produced in the posterior lateral hypothalamus. Much evidence has indicated that central orexinergic systems play numerous functions including energy metabolism, feeding behavior, sleep/wakefulness, and neuroendocrine and sympathetic activation. Morphological studies have shown that the hippocampal CA1 regions receive orexinergic innervation originating from the hypothalamus. Positive orexin-1 (OX 1 ) receptors are detected in the CA1 regions. Previous behavioral studies have shown that microinjection of OX 1 receptor antagonist into the hippocampus impairs acquisition and consolidation of spatial memory. However, up to now, little has been known about the direct electrophysiological effects of orexin-A on hippocampal CA1 neurons. Employing multibarrel single-unit extracellular recordings, the present study showed that micropressure administration of orexin-A significantly increased the spontaneous firing rate from 2.96 ± 0.85 to 8.45 ± 1.86 Hz (P neurons in male rats. Furthermore, application of the specific OX 1 receptor antagonist SB-334867 alone significantly decreased the firing rate from 4.02 ± 1.08 to 2.11 ± 0.58 Hz in 7 out of the 17 neurons (P neurons. Coapplication of SB-334867 completely blocked orexin-A-induced excitation of hippocampal CA1 neurons. The PLC pathway may be involved in activation of OX 1 receptor-induced excitation of CA1 neurons. Taken together, the present study's results suggest that orexin-A produces excitatory effects on hippocampal neurons via OX 1 receptors. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Adaptation in the visual cortex: influence of membrane trajectory and neuronal firing pattern on slow afterpotentials.

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Vanessa F Descalzo

Full Text Available The input/output relationship in primary visual cortex neurons is influenced by the history of the preceding activity. To understand the impact that membrane potential trajectory and firing pattern has on the activation of slow conductances in cortical neurons we compared the afterpotentials that followed responses to different stimuli evoking similar numbers of action potentials. In particular, we compared afterpotentials following the intracellular injection of either square or sinusoidal currents lasting 20 seconds. Both stimuli were intracellular surrogates of different neuronal responses to prolonged visual stimulation. Recordings from 99 neurons in slices of visual cortex revealed that for stimuli evoking an equivalent number of spikes, sinusoidal current injection activated a slow afterhyperpolarization of significantly larger amplitude (8.5 ± 3.3 mV and duration (33 ± 17 s than that evoked by a square pulse (6.4 ± 3.7 mV, 28 ± 17 s; p<0.05. Spike frequency adaptation had a faster time course and was larger during plateau (square pulse than during intermittent (sinusoidal depolarizations. Similar results were obtained in 17 neurons intracellularly recorded from the visual cortex in vivo. The differences in the afterpotentials evoked with both protocols were abolished by removing calcium from the extracellular medium or by application of the L-type calcium channel blocker nifedipine, suggesting that the activation of a calcium-dependent current is at the base of this afterpotential difference. These findings suggest that not only the spikes, but the membrane potential values and firing patterns evoked by a particular stimulation protocol determine the responses to any subsequent incoming input in a time window that spans for tens of seconds to even minutes.

Asymmetry in electrical coupling between neurons alters multistable firing behavior

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Pisarchik, A. N.; Jaimes-Reátegui, R.; García-Vellisca, M. A.

2018-03-01

The role of asymmetry in electrical synaptic connection between two neuronal oscillators is studied in the Hindmarsh-Rose model. We demonstrate that the asymmetry induces multistability in spiking dynamics of the coupled neuronal oscillators. The coexistence of at least three attractors, one chaotic and two periodic orbits, for certain coupling strengths is demonstrated with time series, phase portraits, bifurcation diagrams, basins of attraction of the coexisting states, Lyapunov exponents, and standard deviations of peak amplitudes and interspike intervals. The experimental results with analog electronic circuits are in good agreement with the results of numerical simulations.

Energy-efficient neuron, synapse and STDP integrated circuits.

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Cruz-Albrecht, Jose M; Yung, Michael W; Srinivasa, Narayan

2012-06-01

Ultra-low energy biologically-inspired neuron and synapse integrated circuits are presented. The synapse includes a spike timing dependent plasticity (STDP) learning rule circuit. These circuits have been designed, fabricated and tested using a 90 nm CMOS process. Experimental measurements demonstrate proper operation. The neuron and the synapse with STDP circuits have an energy consumption of around 0.4 pJ per spike and synaptic operation respectively.

Synaptic Dynamics and Neuronal Network Connectivity are reflected in the Distribution of Times in Up states

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Khanh eDao Duc

2015-07-01

Full Text Available The dynamics of neuronal networks connected by synaptic dynamics can sustain long periods of depolarization that can last for hundreds of milliseconds such as Up states recorded during sleep or anesthesia. Yet the underlying mechanism driving these periods remain unclear. We show here within a mean-field model that the residence times of the neuronal membrane potential in cortical Up states does not follow a Poissonian law, but presents several peaks. Furthermore, the present modeling approach allows extracting some information about the neuronal network connectivity from the time distribution histogram. Based on a synaptic-depression model, we find that these peaks, that can be observed in histograms of patch-clamp recordings are not artifacts of electrophysiological measurements, but rather are an inherent property of the network dynamics. Analysis of the equations reveals a stable focus located close to the unstable limit cycle, delimiting a region that defines the Up state. The model further shows that the peaks observed in the Up state time distribution are due to winding around the focus before escaping from the basin of attraction. Finally, we use in vivo recordings of intracellular membrane potential and we recover from the peak distribution, some information about the network connectivity. We conclude that it is possible to recover the network connectivity from the distribution of times that the neuronal membrane voltage spends in Up states.

Sustained Firing of Model Central Auditory Neurons Yields a Discriminative Spectro-temporal Representation for Natural Sounds

OpenAIRE

Carlin, Michael A.; Elhilali, Mounya

2013-01-01

The processing characteristics of neurons in the central auditory system are directly shaped by and reflect the statistics of natural acoustic environments, but the principles that govern the relationship between natural sound ensembles and observed responses in neurophysiological studies remain unclear. In particular, accumulating evidence suggests the presence of a code based on sustained neural firing rates, where central auditory neurons exhibit strong, persistent responses to their prefe...

Integrate-and-fire vs Poisson models of LGN input to V1 cortex: noisier inputs reduce orientation selectivity.

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Lin, I-Chun; Xing, Dajun; Shapley, Robert

2012-12-01

One of the reasons the visual cortex has attracted the interest of computational neuroscience is that it has well-defined inputs. The lateral geniculate nucleus (LGN) of the thalamus is the source of visual signals to the primary visual cortex (V1). Most large-scale cortical network models approximate the spike trains of LGN neurons as simple Poisson point processes. However, many studies have shown that neurons in the early visual pathway are capable of spiking with high temporal precision and their discharges are not Poisson-like. To gain an understanding of how response variability in the LGN influences the behavior of V1, we study response properties of model V1 neurons that receive purely feedforward inputs from LGN cells modeled either as noisy leaky integrate-and-fire (NLIF) neurons or as inhomogeneous Poisson processes. We first demonstrate that the NLIF model is capable of reproducing many experimentally observed statistical properties of LGN neurons. Then we show that a V1 model in which the LGN input to a V1 neuron is modeled as a group of NLIF neurons produces higher orientation selectivity than the one with Poisson LGN input. The second result implies that statistical characteristics of LGN spike trains are important for V1's function. We conclude that physiologically motivated models of V1 need to include more realistic LGN spike trains that are less noisy than inhomogeneous Poisson processes.

Neuromorphic Silicon Neuron Circuits

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Indiveri, Giacomo; Linares-Barranco, Bernabé; Hamilton, Tara Julia; van Schaik, André; Etienne-Cummings, Ralph; Delbruck, Tobi; Liu, Shih-Chii; Dudek, Piotr; Häfliger, Philipp; Renaud, Sylvie; Schemmel, Johannes; Cauwenberghs, Gert; Arthur, John; Hynna, Kai; Folowosele, Fopefolu; Saighi, Sylvain; Serrano-Gotarredona, Teresa; Wijekoon, Jayawan; Wang, Yingxue; Boahen, Kwabena

2011-01-01

Hardware implementations of spiking neurons can be extremely useful for a large variety of applications, ranging from high-speed modeling of large-scale neural systems to real-time behaving systems, to bidirectional brain–machine interfaces. The specific circuit solutions used to implement silicon neurons depend on the application requirements. In this paper we describe the most common building blocks and techniques used to implement these circuits, and present an overview of a wide range of neuromorphic silicon neurons, which implement different computational models, ranging from biophysically realistic and conductance-based Hodgkin–Huxley models to bi-dimensional generalized adaptive integrate and fire models. We compare the different design methodologies used for each silicon neuron design described, and demonstrate their features with experimental results, measured from a wide range of fabricated VLSI chips. PMID:21747754

Neuromorphic silicon neuron circuits

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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.

Nicotinic α4β2 Cholinergic Receptor Influences on Dorsolateral Prefrontal Cortical Neuronal Firing during a Working Memory Task.

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Sun, Yongan; Yang, Yang; Galvin, Veronica C; Yang, Shengtao; Arnsten, Amy F; Wang, Min

2017-05-24

The primate dorsolateral prefrontal cortex (dlPFC) subserves top-down regulation of attention and working memory abilities. Depletion studies show that the neuromodulator acetylcholine (ACh) is essential to dlPFC working memory functions, but the receptor and cellular bases for cholinergic actions are just beginning to be understood. The current study found that nicotinic receptors comprised of α4 and β2 subunits (α4β2-nAChR) enhance the task-related firing of delay and fixation cells in the dlPFC of monkeys performing a working memory task. Iontophoresis of α4β2-nAChR agonists increased the neuronal firing and enhanced the spatial tuning of delay cells, neurons that represent visual space in the absence of sensory stimulation. These enhancing effects were reversed by coapplication of a α4β2-nAChR antagonist, consistent with actions at α4β2-nAChR. Delay cell firing was reduced when distractors were presented during the delay epoch, whereas stimulation of α4β2-nAChR protected delay cells from these deleterious effects. Iontophoresis of α4β2-nAChR agonists also enhanced the firing of fixation cells, neurons that increase firing when the monkey initiates a trial, and maintain firing until the trial is completed. These neurons are thought to contribute to sustained attention and top-down motor control and have never before been the subject of pharmacological inquiry. These findings begin to build a picture of the cellular actions underlying the beneficial effects of ACh on attention and working memory. The data may also help to explain why genetic insults to α4 subunits are associated with working memory and attentional deficits and why α4β2-nAChR agonists may have therapeutic potential. SIGNIFICANCE STATEMENT The acetylcholine (ACh) arousal system in the brain is needed for robust attention and working memory functions, but the receptor and cellular bases for its beneficial effects are poorly understood in the newly evolved primate brain. The current

Variable synaptic strengths controls the firing rate distribution in feedforward neural networks.

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Ly, Cheng; Marsat, Gary

2018-02-01

Heterogeneity of firing rate statistics is known to have severe consequences on neural coding. Recent experimental recordings in weakly electric fish indicate that the distribution-width of superficial pyramidal cell firing rates (trial- and time-averaged) in the electrosensory lateral line lobe (ELL) depends on the stimulus, and also that network inputs can mediate changes in the firing rate distribution across the population. We previously developed theoretical methods to understand how two attributes (synaptic and intrinsic heterogeneity) interact and alter the firing rate distribution in a population of integrate-and-fire neurons with random recurrent coupling. Inspired by our experimental data, we extend these theoretical results to a delayed feedforward spiking network that qualitatively capture the changes of firing rate heterogeneity observed in in-vivo recordings. We demonstrate how heterogeneous neural attributes alter firing rate heterogeneity, accounting for the effect with various sensory stimuli. The model predicts how the strength of the effective network connectivity is related to intrinsic heterogeneity in such delayed feedforward networks: the strength of the feedforward input is positively correlated with excitability (threshold value for spiking) when firing rate heterogeneity is low and is negatively correlated with excitability with high firing rate heterogeneity. We also show how our theory can be used to predict effective neural architecture. We demonstrate that neural attributes do not interact in a simple manner but rather in a complex stimulus-dependent fashion to control neural heterogeneity and discuss how it can ultimately shape population codes.

Detection of 5-hydroxytryptamine (5-HT) in vitro using a hippocampal neuronal network-based biosensor with extracellular potential analysis of neurons.

Science.gov (United States)

Hu, Liang; Wang, Qin; Qin, Zhen; Su, Kaiqi; Huang, Liquan; Hu, Ning; Wang, Ping

2015-04-15

5-hydroxytryptamine (5-HT) is an important neurotransmitter in regulating emotions and related behaviors in mammals. To detect and monitor the 5-HT, effective and convenient methods are demanded in investigation of neuronal network. In this study, hippocampal neuronal networks (HNNs) endogenously expressing 5-HT receptors were employed as sensing elements to build an in vitro neuronal network-based biosensor. The electrophysiological characteristics were analyzed in both neuron and network levels. The firing rates and amplitudes were derived from signal to determine the biosensor response characteristics. The experimental results demonstrate a dose-dependent inhibitory effect of 5-HT on hippocampal neuron activities, indicating the effectiveness of this hybrid biosensor in detecting 5-HT with a response range from 0.01μmol/L to 10μmol/L. In addition, the cross-correlation analysis of HNNs activities suggests 5-HT could weaken HNN connectivity reversibly, providing more specificity of this biosensor in detecting 5-HT. Moreover, 5-HT induced spatiotemporal firing pattern alterations could be monitored in neuron and network levels simultaneously by this hybrid biosensor in a convenient and direct way. With those merits, this neuronal network-based biosensor will be promising to be a valuable and utility platform for the study of neurotransmitter in vitro. Copyright © 2014 Elsevier B.V. All rights reserved.

Performance of Link-To-Stub Bolted Connection in Column-Tree Moment Resisting Frames under Fire Conditions

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Mahmood Yahyai

2015-12-01

Full Text Available Column-tree moment resisting frames, as the efficient shop-welded and field-bolted structural systems, are used in many countries. Very limited research has been carried out on such systems under fire conditions. This paper presents experimental investigations of the behavior of beam and bolted splice connections in steel column-tree moment resisting frames exposed to fire. Two full-scale steel sub-frames with different splice connections were tested under ISO 834 standard fire. The flange splice plates were configured as a single plate with single shear bolts in first specimen, and as double plates with double shear bolts in second specimen. The observation of thermal and structural fire behaviors including temperature histories, temperature-deflection of the beam, temperature-rotation of splice connections and failure modes were investigated. The temperature-deflection and temperature-rotation curves remained in the elastic range until about 600°C. Beyond 600°C, the behavior would be highly nonlinear plastic. The beam splice connection failed due to shear fracture of top bolts at temperatures beyond 750°C. Consequently, stub beam web failed at those temperatures because of block-shear. Using double plates with double shear bolts for flange splices would enhance the temperature resistance and rotational capacity of the beam splice connections. Both tests results confirmed that specimens retain the capacity to support the design load when the average beam temperature does not exceed 600°C. This temperature limit confirms the temperature criteria provided by ASTM E119 and ANSI/UL 263 for a restrained beam, and can be used to specify the minimum fire resistance criteria for beams in column-tree MRFs. The measured time-deflection curves showed that the restrained fire resistance rating for both unprotected specimens obtained about 15 minutes in both tests.

Dynamics of Competition between Subnetworks of Spiking Neuronal Networks in the Balanced State

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Lagzi, Fereshteh; Rotter, Stefan

2015-01-01

We explore and analyze the nonlinear switching dynamics of neuronal networks with non-homogeneous connectivity. The general significance of such transient dynamics for brain function is unclear; however, for instance decision-making processes in perception and cognition have been implicated with it. The network under study here is comprised of three subnetworks of either excitatory or inhibitory leaky integrate-and-fire neurons, of which two are of the same type. The synaptic weights are arranged to establish and maintain a balance between excitation and inhibition in case of a constant external drive. Each subnetwork is randomly connected, where all neurons belonging to a particular population have the same in-degree and the same out-degree. Neurons in different subnetworks are also randomly connected with the same probability; however, depending on the type of the pre-synaptic neuron, the synaptic weight is scaled by a factor. We observed that for a certain range of the “within” versus “between” connection weights (bifurcation parameter), the network activation spontaneously switches between the two sub-networks of the same type. This kind of dynamics has been termed “winnerless competition”, which also has a random component here. In our model, this phenomenon is well described by a set of coupled stochastic differential equations of Lotka-Volterra type that imply a competition between the subnetworks. The associated mean-field model shows the same dynamical behavior as observed in simulations of large networks comprising thousands of spiking neurons. The deterministic phase portrait is characterized by two attractors and a saddle node, its stochastic component is essentially given by the multiplicative inherent noise of the system. We find that the dwell time distribution of the active states is exponential, indicating that the noise drives the system randomly from one attractor to the other. A similar model for a larger number of populations might

Dynamics of Competition between Subnetworks of Spiking Neuronal Networks in the Balanced State.

Science.gov (United States)

Lagzi, Fereshteh; Rotter, Stefan

2015-01-01

We explore and analyze the nonlinear switching dynamics of neuronal networks with non-homogeneous connectivity. The general significance of such transient dynamics for brain function is unclear; however, for instance decision-making processes in perception and cognition have been implicated with it. The network under study here is comprised of three subnetworks of either excitatory or inhibitory leaky integrate-and-fire neurons, of which two are of the same type. The synaptic weights are arranged to establish and maintain a balance between excitation and inhibition in case of a constant external drive. Each subnetwork is randomly connected, where all neurons belonging to a particular population have the same in-degree and the same out-degree. Neurons in different subnetworks are also randomly connected with the same probability; however, depending on the type of the pre-synaptic neuron, the synaptic weight is scaled by a factor. We observed that for a certain range of the "within" versus "between" connection weights (bifurcation parameter), the network activation spontaneously switches between the two sub-networks of the same type. This kind of dynamics has been termed "winnerless competition", which also has a random component here. In our model, this phenomenon is well described by a set of coupled stochastic differential equations of Lotka-Volterra type that imply a competition between the subnetworks. The associated mean-field model shows the same dynamical behavior as observed in simulations of large networks comprising thousands of spiking neurons. The deterministic phase portrait is characterized by two attractors and a saddle node, its stochastic component is essentially given by the multiplicative inherent noise of the system. We find that the dwell time distribution of the active states is exponential, indicating that the noise drives the system randomly from one attractor to the other. A similar model for a larger number of populations might suggest a

Design of time-pulse coded optoelectronic neuronal elements for nonlinear transformation and integration

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Krasilenko, Vladimir G.; Nikolsky, Alexander I.; Lazarev, Alexander A.; Lazareva, Maria V.

2008-03-01

In the paper the actuality of neurophysiologically motivated neuron arrays with flexibly programmable functions and operations with possibility to select required accuracy and type of nonlinear transformation and learning are shown. We consider neurons design and simulation results of multichannel spatio-time algebraic accumulation - integration of optical signals. Advantages for nonlinear transformation and summation - integration are shown. The offered circuits are simple and can have intellectual properties such as learning and adaptation. The integrator-neuron is based on CMOS current mirrors and comparators. The performance: consumable power - 100...500 μW, signal period- 0.1...1ms, input optical signals power - 0.2...20 μW time delays - less 1μs, the number of optical signals - 2...10, integration time - 10...100 of signal periods, accuracy or integration error - about 1%. Various modifications of the neuron-integrators with improved performance and for different applications are considered in the paper.

Quantifying chaotic dynamics from integrate-and-fire processes

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Pavlov, A. N. [Department of Physics, Saratov State University, Astrakhanskaya Str. 83, 410012 Saratov (Russian Federation); Saratov State Technical University, Politehnicheskaya Str. 77, 410054 Saratov (Russian Federation); Pavlova, O. N. [Department of Physics, Saratov State University, Astrakhanskaya Str. 83, 410012 Saratov (Russian Federation); Mohammad, Y. K. [Department of Physics, Saratov State University, Astrakhanskaya Str. 83, 410012 Saratov (Russian Federation); Tikrit University Salahudin, Tikrit Qadisiyah, University Str. P.O. Box 42, Tikrit (Iraq); Kurths, J. [Potsdam Institute for Climate Impact Research, Telegraphenberg A 31, 14473 Potsdam (Germany); Institute of Physics, Humboldt University Berlin, 12489 Berlin (Germany)

2015-01-15

Characterizing chaotic dynamics from integrate-and-fire (IF) interspike intervals (ISIs) is relatively easy performed at high firing rates. When the firing rate is low, a correct estimation of Lyapunov exponents (LEs) describing dynamical features of complex oscillations reflected in the IF ISI sequences becomes more complicated. In this work we discuss peculiarities and limitations of quantifying chaotic dynamics from IF point processes. We consider main factors leading to underestimated LEs and demonstrate a way of improving numerical determining of LEs from IF ISI sequences. We show that estimations of the two largest LEs can be performed using around 400 mean periods of chaotic oscillations in the regime of phase-coherent chaos. Application to real data is discussed.

Neuronal plasticity and multisensory integration in filial imprinting.

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Town, Stephen Michael; McCabe, Brian John

2011-03-10

Many organisms sample their environment through multiple sensory systems and the integration of multisensory information enhances learning. However, the mechanisms underlying multisensory memory formation and their similarity to unisensory mechanisms remain unclear. Filial imprinting is one example in which experience is multisensory, and the mechanisms of unisensory neuronal plasticity are well established. We investigated the storage of audiovisual information through experience by comparing the activity of neurons in the intermediate and medial mesopallium of imprinted and naïve domestic chicks (Gallus gallus domesticus) in response to an audiovisual imprinting stimulus and novel object and their auditory and visual components. We find that imprinting enhanced the mean response magnitude of neurons to unisensory but not multisensory stimuli. Furthermore, imprinting enhanced responses to incongruent audiovisual stimuli comprised of mismatched auditory and visual components. Our results suggest that the effects of imprinting on the unisensory and multisensory responsiveness of IMM neurons differ and that IMM neurons may function to detect unexpected deviations from the audiovisual imprinting stimulus.

Neuronal Plasticity and Multisensory Integration in Filial Imprinting

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Town, Stephen Michael; McCabe, Brian John

2011-01-01

Many organisms sample their environment through multiple sensory systems and the integration of multisensory information enhances learning. However, the mechanisms underlying multisensory memory formation and their similarity to unisensory mechanisms remain unclear. Filial imprinting is one example in which experience is multisensory, and the mechanisms of unisensory neuronal plasticity are well established. We investigated the storage of audiovisual information through experience by comparing the activity of neurons in the intermediate and medial mesopallium of imprinted and naïve domestic chicks (Gallus gallus domesticus) in response to an audiovisual imprinting stimulus and novel object and their auditory and visual components. We find that imprinting enhanced the mean response magnitude of neurons to unisensory but not multisensory stimuli. Furthermore, imprinting enhanced responses to incongruent audiovisual stimuli comprised of mismatched auditory and visual components. Our results suggest that the effects of imprinting on the unisensory and multisensory responsiveness of IMM neurons differ and that IMM neurons may function to detect unexpected deviations from the audiovisual imprinting stimulus. PMID:21423770

Rate response of neurons subject to fast or frozen noise: from stochastic and homogeneous to deterministic and heterogeneous populations.

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Alijani, Azadeh Khajeh; Richardson, Magnus J E

2011-07-01

The response of a neuronal population to afferent drive can be expected to be sensitive to both the distribution and dynamics of membrane voltages within the population. Voltage fluctuations can be driven by synaptic noise, neuromodulators, or cellular inhomogeneities: processes ranging from millisecond autocorrelation times to effectively static or "frozen" noise. Here we extend previous studies of filtered fluctuations to the experimentally verified exponential integrate-and-fire model. How fast or frozen fluctuations affect the steady-state rate and firing-rate response are both examined using perturbative solutions and limits of a 1 + 2 dimensional Fokker-Planck equation. The central finding is that, under conditions of a more-or-less constant population voltage variance, the firing-rate response is only weakly dependent on the fluctuation filter constant: The voltage distribution is the principal determinant of the population response. This result is unexpected given the nature of the systems underlying the extreme limits of fast and frozen fluctuations; the first limit represents a homogeneous population of neurons firing stochastically, whereas the second limit is equivalent to a heterogeneous population of neurons firing deterministically.

Characterisation of rebound depolarisation in mice deep dorsal horn neurons in vitro.

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Rivera-Arconada, Ivan; Lopez-Garcia, Jose A

2015-09-01

Spinal dorsal horn neurons constitute the first relay for pain processing and participate in the processing of other sensory, motor and autonomic information. At the cellular level, intrinsic excitability is a factor contributing to network function. In turn, excitability is set by the array of ionic conductance expressed by neurons. Here, we set out to characterise rebound depolarisation following hyperpolarisation, a feature frequently described in dorsal horn neurons but never addressed in depth. To this end, an in vitro preparation of the spinal cord from mice pups was used combined with whole-cell recordings in current and voltage clamp modes. Results show the expression of H- and/or T-type currents in a significant proportion of dorsal horn neurons. The expression of these currents determines the presence of rebound behaviour at the end of hyperpolarising pulses. T-type calcium currents were associated to high-amplitude rebounds usually involving high-frequency action potential firing. H-currents were associated to low-amplitude rebounds less prone to elicit firing or firing at lower frequencies. For a large proportion of neurons expressing both currents, the H-current constitutes a mechanism to ensure a faster response after hyperpolarisations, adjusting the latency of the rebound firing. We conclude that rebound depolarisation and firing are intrinsic factors to many dorsal horn neurons that may constitute a mechanism to integrate somatosensory information in the spinal cord, allowing for a rapid switch from inhibited-to-excited states.

PCB 136 Atropselectively Alters Morphometric and Functional Parameters of Neuronal Connectivity in Cultured Rat Hippocampal Neurons via Ryanodine Receptor-Dependent Mechanisms

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Yang, Dongren; Kania-Korwel, Izabela; Ghogha, Atefeh; Chen, Hao; Stamou, Marianna; Bose, Diptiman D.; Pessah, Isaac N.; Lehmler, Hans-Joachim; Lein, Pamela J.

2014-01-01

We recently demonstrated that polychlorinated biphenyl (PCB) congeners with multiple ortho chlorine substitutions sensitize ryanodine receptors (RyRs), and this activity promotes Ca2+-dependent dendritic growth in cultured neurons. Many ortho-substituted congeners display axial chirality, and we previously reported that the chiral congener PCB 136 (2,2′,3,3′,6,6′-hexachlorobiphenyl) atropselectively sensitizes RyRs. Here, we test the hypothesis that PCB 136 atropisomers differentially alter dendritic growth and other parameters of neuronal connectivity influenced by RyR activity. (−)-PCB 136, which potently sensitizes RyRs, enhances dendritic growth in primary cultures of rat hippocampal neurons, whereas (+)-PCB 136, which lacks RyR activity, has no effect on dendritic growth. The dendrite-promoting activity of (−)-PCB 136 is observed at concentrations ranging from 0.1 to 100nM and is blocked by pharmacologic RyR antagonism. Neither atropisomer alters axonal growth or cell viability. Quantification of PCB 136 atropisomers in hippocampal cultures indicates that atropselective effects on dendritic growth are not due to differential partitioning of atropisomers into cultured cells. Imaging of hippocampal neurons loaded with Ca2+-sensitive dye demonstrates that (−)-PCB 136 but not (+)-PCB 136 increases the frequency of spontaneous Ca2+ oscillations. Similarly, (−)-PCB 136 but not (+)-PCB 136 increases the activity of hippocampal neurons plated on microelectrode arrays. These data support the hypothesis that atropselective effects on RyR activity translate into atropselective effects of PCB 136 atropisomers on neuronal connectivity, and suggest that the variable atropisomeric enrichment of chiral PCBs observed in the human population may be a significant determinant of individual susceptibility for adverse neurodevelopmental outcomes following PCB exposure. PMID:24385416

A complex-valued firing-rate model that approximates the dynamics of spiking networks.

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Evan S Schaffer

2013-10-01

Full Text Available Firing-rate models provide an attractive approach for studying large neural networks because they can be simulated rapidly and are amenable to mathematical analysis. Traditional firing-rate models assume a simple form in which the dynamics are governed by a single time constant. These models fail to replicate certain dynamic features of populations of spiking neurons, especially those involving synchronization. We present a complex-valued firing-rate model derived from an eigenfunction expansion of the Fokker-Planck equation and apply it to the linear, quadratic and exponential integrate-and-fire models. Despite being almost as simple as a traditional firing-rate description, this model can reproduce firing-rate dynamics due to partial synchronization of the action potentials in a spiking model, and it successfully predicts the transition to spike synchronization in networks of coupled excitatory and inhibitory neurons.

A complex-valued firing-rate model that approximates the dynamics of spiking networks.

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Schaffer, Evan S; Ostojic, Srdjan; Abbott, L F

2013-10-01

Firing-rate models provide an attractive approach for studying large neural networks because they can be simulated rapidly and are amenable to mathematical analysis. Traditional firing-rate models assume a simple form in which the dynamics are governed by a single time constant. These models fail to replicate certain dynamic features of populations of spiking neurons, especially those involving synchronization. We present a complex-valued firing-rate model derived from an eigenfunction expansion of the Fokker-Planck equation and apply it to the linear, quadratic and exponential integrate-and-fire models. Despite being almost as simple as a traditional firing-rate description, this model can reproduce firing-rate dynamics due to partial synchronization of the action potentials in a spiking model, and it successfully predicts the transition to spike synchronization in networks of coupled excitatory and inhibitory neurons.

Glutamate mediated astrocytic filtering of neuronal activity.

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Gilad Wallach

2014-12-01

Full Text Available Neuron-astrocyte communication is an important regulatory mechanism in various brain functions but its complexity and role are yet to be fully understood. In particular, the temporal pattern of astrocyte response to neuronal firing has not been fully characterized. Here, we used neuron-astrocyte cultures on multi-electrode arrays coupled to Ca2+ imaging and explored the range of neuronal stimulation frequencies while keeping constant the amount of stimulation. Our results reveal that astrocytes specifically respond to the frequency of neuronal stimulation by intracellular Ca2+ transients, with a clear onset of astrocytic activation at neuron firing rates around 3-5 Hz. The cell-to-cell heterogeneity of the astrocyte Ca2+ response was however large and increasing with stimulation frequency. Astrocytic activation by neurons was abolished with antagonists of type I metabotropic glutamate receptor, validating the glutamate-dependence of this neuron-to-astrocyte pathway. Using a realistic biophysical model of glutamate-based intracellular calcium signaling in astrocytes, we suggest that the stepwise response is due to the supralinear dynamics of intracellular IP3 and that the heterogeneity of the responses may be due to the heterogeneity of the astrocyte-to-astrocyte couplings via gap junction channels. Therefore our results present astrocyte intracellular Ca2+ activity as a nonlinear integrator of glutamate-dependent neuronal activity.

Glutamate Mediated Astrocytic Filtering of Neuronal Activity

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Herzog, Nitzan; De Pittà, Maurizio; Jacob, Eshel Ben; Berry, Hugues; Hanein, Yael

2014-01-01

Neuron-astrocyte communication is an important regulatory mechanism in various brain functions but its complexity and role are yet to be fully understood. In particular, the temporal pattern of astrocyte response to neuronal firing has not been fully characterized. Here, we used neuron-astrocyte cultures on multi-electrode arrays coupled to Ca2+ imaging and explored the range of neuronal stimulation frequencies while keeping constant the amount of stimulation. Our results reveal that astrocytes specifically respond to the frequency of neuronal stimulation by intracellular Ca2+ transients, with a clear onset of astrocytic activation at neuron firing rates around 3-5 Hz. The cell-to-cell heterogeneity of the astrocyte Ca2+ response was however large and increasing with stimulation frequency. Astrocytic activation by neurons was abolished with antagonists of type I metabotropic glutamate receptor, validating the glutamate-dependence of this neuron-to-astrocyte pathway. Using a realistic biophysical model of glutamate-based intracellular calcium signaling in astrocytes, we suggest that the stepwise response is due to the supralinear dynamics of intracellular IP3 and that the heterogeneity of the responses may be due to the heterogeneity of the astrocyte-to-astrocyte couplings via gap junction channels. Therefore our results present astrocyte intracellular Ca2+ activity as a nonlinear integrator of glutamate-dependent neuronal activity. PMID:25521344

Arcuate NPY neurons sense and integrate peripheral metabolic signals to control feeding.

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Kohno, Daisuke; Yada, Toshihiko

2012-12-01

NPY neuron in the hypothalamic arcuate nucleus is a key feeding center. Studies have shown that NPY neuron in the arcuate nucleus has a role to induce food intake. The arcuate nucleus is structurally unique with lacking blood brain barrier. Peripheral energy signals including hormones and nutrition can reach the arcuate nucleus. In this review, we discuss sensing and integrating peripheral signals in NPY neurons. In the arcuate nucleus, ghrelin mainly activates NPY neurons. Leptin and insulin suppress the ghrelin-induced activation in 30-40% of the ghrelin-activated NPY neurons. Lowering glucose concentration activates 40% of NPY neurons. These results indicate that NPY neuron in the arcuate nucleus is a feeding center in which major peripheral energy signals are directly sensed and integrated. Furthermore, there are subpopulations of NPY neurons in regard to their responsiveness to peripheral signals. These findings suggest that NPY neuron in the arcuate nucleus is an essential feeding center to induce food intake in response to peripheral metabolic state. Copyright © 2012 Elsevier Ltd. All rights reserved.

Replicating receptive fields of simple and complex cells in primary visual cortex in a neuronal network model with temporal and population sparseness and reliability.

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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.

Prenatal androgenization of female mice programs an increase in firing activity of gonadotropin-releasing hormone (GnRH) neurons that is reversed by metformin treatment in adulthood.

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Roland, Alison V; Moenter, Suzanne M

2011-02-01

Prenatal androgenization (PNA) of female mice with dihydrotestosterone programs reproductive dysfunction in adulthood, characterized by elevated luteinizing hormone levels, irregular estrous cycles, and central abnormalities. Here, we evaluated activity of GnRH neurons from PNA mice and the effects of in vivo treatment with metformin, an activator of AMP-activated protein kinase (AMPK) that is commonly used to treat the fertility disorder polycystic ovary syndrome. Estrous cycles were monitored in PNA and control mice before and after metformin administration. Before metformin, cycles were longer in PNA mice and percent time in estrus lower; metformin normalized cycles in PNA mice. Extracellular recordings were used to monitor GnRH neuron firing activity in brain slices from diestrous mice. Firing rate was higher and quiescence lower in GnRH neurons from PNA mice, demonstrating increased GnRH neuron activity. Metformin treatment of PNA mice restored firing activity and LH to control levels. To assess whether AMPK activation contributed to the metformin-induced reduction in GnRH neuron activity, the AMPK antagonist compound C was acutely applied to cells. Compound C stimulated cells from metformin-treated, but not untreated, mice, suggesting that AMPK was activated in GnRH neurons, or afferent neurons, in the former group. GnRH neurons from metformin-treated mice also showed a reduced inhibitory response to low glucose. These studies indicate that PNA causes enhanced firing activity of GnRH neurons and elevated LH that are reversible by metformin, raising the possibility that central AMPK activation by metformin may play a role in its restoration of reproductive cycles in polycystic ovary syndrome.

Walk like me, talk like me. The connection between mirror neurons and autism spectrum disorder.

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Saffin, Jillian M; Tohid, Hassaan

2016-04-01

Understanding social cognition has become a hallmark in deciphering autism spectrum disorder. Neurobiological theories are taking precedence in causation studies as researchers look to abnormalities in brain development as the cause of deficits in social behavior, cognitive processes, and language. Following their discovery in the 1990s, mirror neurons have become a dominant theory for that the mirror neuron system may play a critical role in the pathophysiology of various symptoms of autism. Over the decades, the theory has evolved from the suggestion of a broken mirror neuron system to impairments in mirror neuron circuitry. The mirror neuron system has not gained total support due to inconsistent findings; a comprehensive analysis of the growing body of research could shed light on the benefits, or the disadvantage of continuing to study mirror neurons and their connection to autism.

Phase precession through acceleration of local theta rhythm: a biophysical model for the interaction between place cells and local inhibitory neurons.

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Castro, Luísa; Aguiar, Paulo

2012-08-01

Phase precession is one of the most well known examples within the temporal coding hypothesis. Here we present a biophysical spiking model for phase precession in hippocampal CA1 which focuses on the interaction between place cells and local inhibitory interneurons. The model's functional block is composed of a place cell (PC) connected with a local inhibitory cell (IC) which is modulated by the population theta rhythm. Both cells receive excitatory inputs from the entorhinal cortex (EC). These inputs are both theta modulated and space modulated. The dynamics of the two neuron types are described by integrate-and-fire models with conductance synapses, and the EC inputs are described using non-homogeneous Poisson processes. Phase precession in our model is caused by increased drive to specific PC/IC pairs when the animal is in their place field. The excitation increases the IC's firing rate, and this modulates the PC's firing rate such that both cells precess relative to theta. Our model implies that phase coding in place cells may not be independent from rate coding. The absence of restrictive connectivity constraints in this model predicts the generation of phase precession in any network with similar architecture and subject to a clocking rhythm, independently of the involvement in spatial tasks.

Altered neuronal firing pattern of the basal ganglia nucleus plays a role in levodopa-induced dyskinesia in patients with Parkinson's disease

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Xiaoyu eLi

2015-11-01

Full Text Available Background: Levodopa therapy alleviates the symptoms of Parkinson's disease (PD, but long-term treatment often leads to motor complications such as levodopa-induced dyskinesia (LID. Aim: To explore the neuronal activity in the basal ganglia nuclei in patients with PD and LID. Methods: Thirty patients with idiopathic PD (age, 55.1±11.0 years; disease duration, 8.7±5.6 years were enrolled between August 2006 and August 2013 at the Xuanwu Hospital, Capital Medical University, China. Their Hoehn and Yahr scores ranged from 2 to 4 and their UPDRS III scores were 28.5±5.2. Fifteen of them had severe LID (UPDRS IV scores of 6.7±1.6. Microelectrode recording was performed in the globus pallidus internus (GPi and subthalamic nucleus (STN during pallidotomy (n=12 or STN deep brain stimulation (DBS; bilateral, n=12; unilateral, n=6. The firing patterns and frequencies of various cell types were analyzed by assessing single cell interspike intervals (ISIs and the corresponding coefficient of variation (CV. Results: A total of 295 neurons were identified from the GPi (n=12 and STN (n=18. These included 26 (8.8% highly grouped discharge, 30 (10.2% low frequency firing, 78 (26.4% rapid tonic discharge, 103 (34.9% irregular activity, and 58 (19.7% tremor-related activity. There were significant differences between the two groups (P<0.05 for neurons with irregular firing, highly irregular cluster-like firing, and low-frequency firing. Conclusion: Altered neuronal activity was observed in the basal ganglia nucleus of GPi and STN, and may play important roles in the pathophysiology of PD and LID.

Memory formation orchestrates the wiring of adult-born hippocampal neurons into brain circuits.

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Petsophonsakul, Petnoi; Richetin, Kevin; Andraini, Trinovita; Roybon, Laurent; Rampon, Claire

2017-08-01

During memory formation, structural rearrangements of dendritic spines provide a mean to durably modulate synaptic connectivity within neuronal networks. New neurons generated throughout the adult life in the dentate gyrus of the hippocampus contribute to learning and memory. As these neurons become incorporated into the network, they generate huge numbers of new connections that modify hippocampal circuitry and functioning. However, it is yet unclear as to how the dynamic process of memory formation influences their synaptic integration into neuronal circuits. New memories are established according to a multistep process during which new information is first acquired and then consolidated to form a stable memory trace. Upon recall, memory is transiently destabilized and vulnerable to modification. Using contextual fear conditioning, we found that learning was associated with an acceleration of dendritic spines formation of adult-born neurons, and that spine connectivity becomes strengthened after memory consolidation. Moreover, we observed that afferent connectivity onto adult-born neurons is enhanced after memory retrieval, while extinction training induces a change of spine shapes. Together, these findings reveal that the neuronal activity supporting memory processes strongly influences the structural dendritic integration of adult-born neurons into pre-existing neuronal circuits. Such change of afferent connectivity is likely to impact the overall wiring of hippocampal network, and consequently, to regulate hippocampal function.

Developmental changes in electrophysiological properties and a transition from electrical to chemical coupling between excitatory layer 4 neurons in the rat barrel cortex

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Fliza eValiullina

2016-01-01

Full Text Available During development, sensory systems switch from an immature to an adult mode of function along with the emergence of the active cortical states. Here, we used patch-clamp recordings from neocortical slices in vitro to characterize the developmental changes in the basic electrophysiological properties of excitatory L4 neurons and their connectivity before and after the developmental switch, which occurs in the rat barrel cortex in vivo at postnatal day P8. Prior to the switch, L4 neurons had lower resting membrane potentials, higher input resistance, lower membrane capacity, as well as action potentials (APs with smaller amplitudes, longer durations and higher AP thresholds compared to the neurons after the switch. A sustained firing pattern also emerged around the switch. Dual patch-clamp recordings from L4 neurons revealed that recurrent connections between L4 excitatory cells do not exist before and develop rapidly across the switch. In contrast, electrical coupling between these neurons waned around the switch. We suggest that maturation of electrophysiological features, particularly acquisition of a sustained firing pattern, and a transition from the immature electrical to mature chemical synaptic coupling between excitatory L4 neurons, contributes to the developmental switch in the cortical mode of function.

The Relevance of AgRP Neuron-Derived GABA Inputs to POMC Neurons Differs for Spontaneous and Evoked Release.

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Rau, Andrew R; Hentges, Shane T

2017-08-02

Hypothalamic agouti-related peptide (AgRP) neurons potently stimulate food intake, whereas proopiomelanocortin (POMC) neurons inhibit feeding. Whether AgRP neurons exert their orexigenic actions, at least in part, by inhibiting anorexigenic POMC neurons remains unclear. Here, the connectivity between GABA-releasing AgRP neurons and POMC neurons was examined in brain slices from male and female mice. GABA-mediated spontaneous IPSCs (sIPSCs) in POMC neurons were unaffected by disturbing GABA release from AgRP neurons either by cell type-specific deletion of the vesicular GABA transporter or by expression of botulinum toxin in AgRP neurons to prevent vesicle-associated membrane protein 2-dependent vesicle fusion. Additionally, there was no difference in the ability of μ-opioid receptor (MOR) agonists to inhibit sIPSCs in POMC neurons when MORs were deleted from AgRP neurons, and activation of the inhibitory designer receptor hM4Di on AgRP neurons did not affect sIPSCs recorded from POMC neurons. These approaches collectively indicate that AgRP neurons do not significantly contribute to the strong spontaneous GABA input to POMC neurons. Despite these observations, optogenetic stimulation of AgRP neurons reliably produced evoked IPSCs in POMC neurons, leading to the inhibition of POMC neuron firing. Thus, AgRP neurons can potently affect POMC neuron function without contributing a significant source of spontaneous GABA input to POMC neurons. Together, these results indicate that the relevance of GABAergic inputs from AgRP to POMC neurons is state dependent and highlight the need to consider different types of transmitter release in circuit mapping and physiologic regulation. SIGNIFICANCE STATEMENT Agouti-related peptide (AgRP) neurons play an important role in driving food intake, while proopiomelanocortin (POMC) neurons inhibit feeding. Despite the importance of these two well characterized neuron types in maintaining metabolic homeostasis, communication between these

Introduction of Integrity Evaluation Criteria Developing during and after fire for Nuclear Power Plant Buildings

International Nuclear Information System (INIS)

Lee, Jin Woo

2016-01-01

The first project for nuclear power plant built in Korea to taking account the engineering based approach was started on October 2015 including the whole process such as fire hazard analysis, standard fire modeling, cable tray fire with multi spurious operation, structural fire integrity evaluation, and large area fire induced air craft crash. This paper covers the brief developing scheme and roadmap focusing on structural fire evaluation criteria. The meaningful first step for developing the structural fire integrity in nuclear power plant building is started with the series of fire related sub sections mentioned in earlier section. The recognition and sufficient effort of fire research leads to set up the safe and reliable design of nuclear power plant

Introduction of Integrity Evaluation Criteria Developing during and after fire for Nuclear Power Plant Buildings

Energy Technology Data Exchange (ETDEWEB)

Lee, Jin Woo [KEPCo, Gimcheon (Korea, Republic of)

2016-05-15

The first project for nuclear power plant built in Korea to taking account the engineering based approach was started on October 2015 including the whole process such as fire hazard analysis, standard fire modeling, cable tray fire with multi spurious operation, structural fire integrity evaluation, and large area fire induced air craft crash. This paper covers the brief developing scheme and roadmap focusing on structural fire evaluation criteria. The meaningful first step for developing the structural fire integrity in nuclear power plant building is started with the series of fire related sub sections mentioned in earlier section. The recognition and sufficient effort of fire research leads to set up the safe and reliable design of nuclear power plant.

Regulatory Mechanisms Controlling Maturation of Serotonin Neuron Identity and Function.

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Spencer, William C; Deneris, Evan S

2017-01-01

The brain serotonin (5-hydroxytryptamine; 5-HT) system has been extensively studied for its role in normal physiology and behavior, as well as, neuropsychiatric disorders. The broad influence of 5-HT on brain function, is in part due to the vast connectivity pattern of 5-HT-producing neurons throughout the CNS. 5-HT neurons are born and terminally specified midway through embryogenesis, then enter a protracted period of maturation, where they functionally integrate into CNS circuitry and then are maintained throughout life. The transcriptional regulatory networks controlling progenitor cell generation and terminal specification of 5-HT neurons are relatively well-understood, yet the factors controlling 5-HT neuron maturation are only recently coming to light. In this review, we first provide an update on the regulatory network controlling 5-HT neuron development, then delve deeper into the properties and regulatory strategies governing 5-HT neuron maturation. In particular, we discuss the role of the 5-HT neuron terminal selector transcription factor (TF) Pet-1 as a key regulator of 5-HT neuron maturation. Pet-1 was originally shown to positively regulate genes needed for 5-HT synthesis, reuptake and vesicular transport, hence 5-HT neuron-type transmitter identity. It has now been shown to regulate, both positively and negatively, many other categories of genes in 5-HT neurons including ion channels, GPCRs, transporters, neuropeptides, and other transcription factors. Its function as a terminal selector results in the maturation of 5-HT neuron excitability, firing characteristics, and synaptic modulation by several neurotransmitters. Furthermore, there is a temporal requirement for Pet-1 in the control of postmitotic gene expression trajectories thus indicating a direct role in 5-HT neuron maturation. Proper regulation of the maturation of cellular identity is critical for normal neuronal functioning and perturbations in the gene regulatory networks controlling

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

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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.

Application of unfolding transformation in the random matrix theory to analyze in vivo neuronal spike firing during awake and anesthetized conditions

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Risako Kato

2018-03-01

Full Text Available General anesthetics decrease the frequency and density of spike firing. This effect makes it difficult to detect spike regularity. To overcome this problem, we developed a method utilizing the unfolding transformation which analyzes the energy level statistics in the random matrix theory. We regarded the energy axis as time axis of neuron spike and analyzed the time series of cortical neural firing in vivo. Unfolding transformation detected regularities of neural firing while changes in firing densities were associated with pentobarbital. We found that unfolding transformation enables us to compare firing regularity between awake and anesthetic conditions on a universal scale. Keywords: Unfolding transformation, Spike-timing, Regularity

Noise focusing and the emergence of coherent activity in neuronal cultures

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Orlandi, Javier G.; Soriano, Jordi; Alvarez-Lacalle, Enrique; Teller, Sara; Casademunt, Jaume

2013-09-01

At early stages of development, neuronal cultures in vitro spontaneously reach a coherent state of collective firing in a pattern of nearly periodic global bursts. Although understanding the spontaneous activity of neuronal networks is of chief importance in neuroscience, the origin and nature of that pulsation has remained elusive. By combining high-resolution calcium imaging with modelling in silico, we show that this behaviour is controlled by the propagation of waves that nucleate randomly in a set of points that is specific to each culture and is selected by a non-trivial interplay between dynamics and topology. The phenomenon is explained by the noise focusing effect--a strong spatio-temporal localization of the noise dynamics that originates in the complex structure of avalanches of spontaneous activity. Results are relevant to neuronal tissues and to complex networks with integrate-and-fire dynamics and metric correlations, for instance, in rumour spreading on social networks.

Synaptic and intrinsic activation of GABAergic neurons in the cardiorespiratory brainstem network.

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Frank, Julie G; Mendelowitz, David

2012-01-01

GABAergic pathways in the brainstem play an essential role in respiratory rhythmogenesis and interactions between the respiratory and cardiovascular neuronal control networks. However, little is known about the identity and function of these GABAergic inhibitory neurons and what determines their activity. In this study we have identified a population of GABAergic neurons in the ventrolateral medulla that receive increased excitatory post-synaptic potentials during inspiration, but also have spontaneous firing in the absence of synaptic input. Using transgenic mice that express GFP under the control of the Gad1 (GAD67) gene promoter, we determined that this population of GABAergic neurons is in close apposition to cardioinhibitory parasympathetic cardiac neurons in the nucleus ambiguus (NA). These neurons fire in synchronization with inspiratory activity. Although they receive excitatory glutamatergic synaptic inputs during inspiration, this excitatory neurotransmission was not altered by blocking nicotinic receptors, and many of these GABAergic neurons continue to fire after synaptic blockade. The spontaneous firing in these GABAergic neurons was not altered by the voltage-gated calcium channel blocker cadmium chloride that blocks both neurotransmission to these neurons and voltage-gated Ca(2+) currents, but spontaneous firing was diminished by riluzole, demonstrating a role of persistent sodium channels in the spontaneous firing in these cardiorespiratory GABAergic neurons that possess a pacemaker phenotype. The spontaneously firing GABAergic neurons identified in this study that increase their activity during inspiration would support respiratory rhythm generation if they acted primarily to inhibit post-inspiratory neurons and thereby release inspiration neurons to increase their activity. This population of inspiratory-modulated GABAergic neurons could also play a role in inhibiting neurons that are most active during expiration and provide a framework for

Synaptic and intrinsic activation of GABAergic neurons in the cardiorespiratory brainstem network.

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Julie G Frank

Full Text Available GABAergic pathways in the brainstem play an essential role in respiratory rhythmogenesis and interactions between the respiratory and cardiovascular neuronal control networks. However, little is known about the identity and function of these GABAergic inhibitory neurons and what determines their activity. In this study we have identified a population of GABAergic neurons in the ventrolateral medulla that receive increased excitatory post-synaptic potentials during inspiration, but also have spontaneous firing in the absence of synaptic input. Using transgenic mice that express GFP under the control of the Gad1 (GAD67 gene promoter, we determined that this population of GABAergic neurons is in close apposition to cardioinhibitory parasympathetic cardiac neurons in the nucleus ambiguus (NA. These neurons fire in synchronization with inspiratory activity. Although they receive excitatory glutamatergic synaptic inputs during inspiration, this excitatory neurotransmission was not altered by blocking nicotinic receptors, and many of these GABAergic neurons continue to fire after synaptic blockade. The spontaneous firing in these GABAergic neurons was not altered by the voltage-gated calcium channel blocker cadmium chloride that blocks both neurotransmission to these neurons and voltage-gated Ca(2+ currents, but spontaneous firing was diminished by riluzole, demonstrating a role of persistent sodium channels in the spontaneous firing in these cardiorespiratory GABAergic neurons that possess a pacemaker phenotype. The spontaneously firing GABAergic neurons identified in this study that increase their activity during inspiration would support respiratory rhythm generation if they acted primarily to inhibit post-inspiratory neurons and thereby release inspiration neurons to increase their activity. This population of inspiratory-modulated GABAergic neurons could also play a role in inhibiting neurons that are most active during expiration and provide a

Oscillatory integration windows in neurons

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Gupta, Nitin; Singh, Swikriti Saran; Stopfer, Mark

2016-01-01

Oscillatory synchrony among neurons occurs in many species and brain areas, and has been proposed to help neural circuits process information. One hypothesis states that oscillatory input creates cyclic integration windows: specific times in each oscillatory cycle when postsynaptic neurons become especially responsive to inputs. With paired local field potential (LFP) and intracellular recordings and controlled stimulus manipulations we directly test this idea in the locust olfactory system. We find that inputs arriving in Kenyon cells (KCs) sum most effectively in a preferred window of the oscillation cycle. With a computational model, we show that the non-uniform structure of noise in the membrane potential helps mediate this process. Further experiments performed in vivo demonstrate that integration windows can form in the absence of inhibition and at a broad range of oscillation frequencies. Our results reveal how a fundamental coincidence-detection mechanism in a neural circuit functions to decode temporally organized spiking. PMID:27976720