CiteULike: Tag network_dynamics

Discovering Functional Communities in Dynamical Networks

Cosma Shalizi — 2008-01-07T03:55:44-00:00

(29 Sep 2006)

Many networks are important because they are substrates for dynamical systems, and their pattern of functional connectivity can itself be dynamic -- they can functionally reorganize, even if their underlying anatomical structure remains fixed. However, the recent rapid progress in discovering the community structure of networks has overwhelmingly focused on that constant anatomical connectivity. In this paper, we lay out the problem of discovering_functional communities_, and describe an approach to doing so. This method combines recent work on measuring information sharing across stochastic networks with an existing and successful community-discovery algorithm for weighted networks. We illustrate it with an application to a large biophysical model of the transition from beta to gamma rhythms in the hippocampus.

BDNF boosts spike fidelity in chaotic neural oscillations.

S Fujisawa — 2007-09-21T17:20:40-00:00

Biophys J, Vol. 86, No. 3. (March 2004), pp. 1820-1828.

Oscillatory activity and its nonlinear dynamics are of fundamental importance for information processing in the central nervous system. Here we show that in aperiodic oscillations, brain-derived neurotrophic factor (BDNF), a member of the neurotrophin family, enhances the accuracy of action potentials in terms of spike reliability and temporal precision. Cultured hippocampal neurons displayed irregular oscillations of membrane potential in response to sinusoidal 20-Hz somatic current injection, yielding wobbly orbits in the phase space, i.e., a strange attractor. Brief application of BDNF suppressed this unpredictable dynamics and stabilized membrane potential fluctuations, leading to rhythmical firing. Even in complex oscillations induced by external stimuli of 40 Hz (gamma) on a 5-Hz (theta) carrier, BDNF-treated neurons generated more precisely timed spikes, i.e., phase-locked firing, coupled with theta-phase precession. These phenomena were sensitive to K252a, an inhibitor of tyrosine receptor kinases and appeared attributable to BDNF-evoked Na(+) current. The data are the first indication of pharmacological control of endogenous chaos. BDNF diminishes the ambiguity of spike time jitter and thereby might assure neural encoding, such as spike timing-dependent synaptic plasticity.

Real-time computing without stable states: a new framework for neural computation based on perturbations.

W Maass — 2007-09-21T10:50:29-00:00

Neural Comput, Vol. 14, No. 11. (November 2002), pp. 2531-2560.

A key challenge for neural modeling is to explain how a continuous stream of multimodal input from a rapidly changing environment can be processed by stereotypical recurrent circuits of integrate-and-fire neurons in real time. We propose a new computational model for real-time computing on time-varying input that provides an alternative to paradigms based on Turing machines or attractor neural networks. It does not require a task-dependent construction of neural circuits. Instead, it is based on principles of high-dimensional dynamical systems in combination with statistical learning theory and can be implemented on generic evolved or found recurrent circuitry. It is shown that the inherent transient dynamics of the high-dimensional dynamical system formed by a sufficiently large and heterogeneous neural circuit may serve as universal analog fading memory. Readout neurons can learn to extract in real time from the current state of such recurrent neural circuit information about current and past inputs that may be needed for diverse tasks. Stable internal states are not required for giving a stable output, since transient internal states can be transformed by readout neurons into stable target outputs due to the high dimensionality of the dynamical system. Our approach is based on a rigorous computational model, the liquid state machine, that, unlike Turing machines, does not require sequential transitions between well-defined discrete internal states. It is supported, as the Turing machine is, by rigorous mathematical results that predict universal computational power under idealized conditions, but for the biologically more realistic scenario of real-time processing of time-varying inputs. Our approach provides new perspectives for the interpretation of neural coding, the design of experiments and data analysis in neurophysiology, and the solution of problems in robotics and neurotechnology.

Dependence of Neuronal Correlations on Filter Characteristics and Marginal Spike Train Statistics.

Tom Tetzlaff — 2008-05-06T13:50:28-00:00

Neural computation (25 April 2008)

Correlated neural activity has been observed at various signal levels (e.g., spike count, membrane potential, local field potential, EEG, fMRI BOLD). Most of these signals can be considered as superpositions of pike trains filtered by components of the neural system (synapses, membranes) and the measurement process. It is largely unknown how the spike train correlation structure is altered by this filtering and what the consequences for the dynamics of the system and for the interpretation of measured correlations are. In this study, we focus on linearly filtered spike trains and particularly consider correlations caused by overlapping presynaptic neuron populations. We demonstrate that correlation functions and statistical second-order measures like the variance, the covariance, and the correlation coefficient generally exhibit a complex dependence on the filter properties and the statistics of the presynaptic spike trains. We point out that both contributions can play a significant role in modulating the interaction strength between neurons or neuron populations. In many applications, the coherence allows a filter-independent quantification of correlated activity. In different network models, we discuss the estimation of network connectivity from the high-frequency coherence of simultaneous intracellular recordings of pairs of neurons.

Encoding and Decoding Spikes for Dynamic Stimuli.

Rama Natarajan — 2008-07-07T17:30:02-00:00

Neural computation (3 April 2008)

Naturally occurring sensory stimuli are dynamic. In this article, we consider how spiking neural populations might transmit information about continuous dynamic stimulus variables. The combination of simple encoders and temporal stimulus correlations leads to a code in which information is not readily available to downstream neurons. Here, we explore a complex encoder that is paired with a simple decoder that allows representation and manipulation of the dynamic information in neural systems. The encoder we present takes the form of a biologically plausible recurrent spiking neural network where the output population recodes its inputs to produce spikes that are independently decodeable. We show that this network can be learned in a supervised manner by a simple local learning rule.

NEUROSCIENCE: Transient Dynamics for Neural Processing.

Misha Rabinovich — 2008-07-07T14:25:03-00:00

Science (New York, N.Y.), Vol. 321, No. 5885. (4 July 2008), pp. 48-50.

Pathological Effect of Homeostatic Synaptic Scaling on Network Dynamics in Diseases of the Cortex

Flavio Frohlich — 2008-02-18T18:02:46-00:00

J. Neurosci., Vol. 28, No. 7. (13 February 2008), pp. 1709-1720.

Slow periodic EEG discharges are common in CNS disorders. The pathophysiology of this aberrant rhythmic activity is poorly understood. We used a computational model of a neocortical network with a dynamic homeostatic scaling rule to show that loss of input (partial deafferentation) can trigger network reorganization that results in pathological periodic discharges. The decrease in average firing rate in the network by deafferentation was compensated by homeostatic synaptic scaling of recurrent excitation among pyramidal cells. Synaptic scaling succeeded in recovering the network target firing rate for all degrees of deafferentation (fraction of deafferented cells), but there was a critical degree of deafferentation for pathological network reorganization. For deafferentation degrees below this value, homeostatic upregulation of recurrent excitation had minimal effect on the macroscopic network dynamics. For deafferentation above this threshold, however, a slow periodic oscillation appeared, patterns of activity were less sparse, and bursting occurred in individual neurons. Also, comparison of spike-triggered afferent and recurrent excitatory conductances revealed that information transmission was strongly impaired. These results suggest that homeostatic plasticity can lead to secondary functional impairment in case of cortical disorders associated with cell loss. 10.1523/JNEUROSCI.4263-07.2008

Intrinsic dynamics in neuronal networks. I. Theory.

PE Latham — 2008-03-07T16:08:06-00:00

J Neurophysiol, Vol. 83, No. 2. (February 2000), pp. 808-827.

Many networks in the mammalian nervous system remain active in the absence of stimuli. This activity falls into two main patterns: steady firing at low rates and rhythmic bursting. How are these firing patterns generated? Specifically, how do dynamic interactions between excitatory and inhibitory neurons produce these firing patterns, and how do networks switch from one firing pattern to the other? We investigated these questions theoretically by examining the intrinsic dynamics of large networks of neurons. Using both a semianalytic model based on mean firing rate dynamics and simulations with large neuronal networks, we found that the dynamics, and thus the firing patterns, are controlled largely by one parameter, the fraction of endogenously active cells. When no endogenously active cells are present, networks are either silent or fire at a high rate; as the number of endogenously active cells increases, there is a transition to bursting; and, with a further increase, there is a second transition to steady firing at a low rate. A secondary role is played by network connectivity, which determines whether activity occurs at a constant mean firing rate or oscillates around that mean. These conclusions require only conventional assumptions: excitatory input to a neuron increases its firing rate, inhibitory input decreases it, and neurons exhibit spike-frequency adaptation. These conclusions also lead to two experimentally testable predictions: 1) isolated networks that fire at low rates must contain endogenously active cells and 2) a reduction in the fraction of endogenously active cells in such networks must lead to bursting.

Computational significance of transient dynamics in cortical networks

Durstewitz — 2008-01-08T11:47:10-00:00

European Journal of Neuroscience, Vol. 27, No. 1. (January 2008), pp. 217-227.

Analysis of neural response for excitation-inhibition balanced networks with reversal potentials for large numbers of inputs

AN Burkitt — 2007-09-21T12:09:13-00:00

Neural Networks, 1999. IJCNN '99. International Joint Conference on, Vol. 1 (1999), pp. 305-308 vol.1.

The observed variability in the spike rate of cortical neurons has been hypothesized to result from a balance in the excitatory and inhibitory synaptic inputs that the neurons receive. The coefficient of variation of the spike rate is calculated in the limit of a large number of inputs using the integrated-input technique, which is extended here to include the effect of reversal potentials. The output spike rate is found to increase monotonically over two orders of magnitude, thereby solving the dynamic range (or gain control) problem. The coefficient of variation is approximately 1.0 for low input rates and increases to around 1.6 at high input rates, well within the range observed in the response of cortical neurons

Single Neurons Can Induce Phase Transitions of Cortical Recurrent Networks with Multiple Internal States.

Shigeyoshi Fujisawa — 2005-09-09T15:49:48-00:00

Cereb Cortex (10 August 2005)

Fluctuations of membrane potential of cortical neurons, referred to here as internal states, are essential for brain function, but little is known about how these internal states emerge and are maintained, or what determines transitions between these states. We performed intracellular recordings from hippocampal CA3 pyramidal cells ex vivo and found that neurons display multiple and hierarchical internal states, which are linked to cholinergic activity and are characterized by several power law structures in membrane potential dynamics. Multiple recordings from adjacent neurons revealed that the internal states were coherent between neurons, indicating that the internal state of any given cell in a local network could represent the network activity state. Repeated stimulation of single neurons led over time to transitions to different internal states in both the stimulated neuron and neighboring neurons. Thus, single-cell activation is sufficient to shift the state of the entire local network. As the states shift to more active levels, theta- and gamma-frequency components developed in the form of subthreshold oscillations. State transitions were associated with changes in membrane conductance but were not accompanied by a change in reversal potential. These data suggest that the recurrent network organizes the internal states of individual neurons into synchronization through network activity with balanced excitation and inhibition, and that this organization is discrete, heterogeneous and dynamic in nature. Thus, neuronal states reflect the 'phase' of an active network, a novel demonstration of the dynamics and flexibility of cortical microcircuitry.

Structure of spontaneous UP and DOWN transitions self-organizing in a cortical network model.

S Kang — 2008-04-03T08:06:25-00:00

PLoS computational biology, Vol. 4, No. 3. (March 2008)

Synaptic plasticity is considered to play a crucial role in the experience-dependent self-organization of local cortical networks. In the absence of sensory stimuli, cerebral cortex exhibits spontaneous membrane potential transitions between an UP and a DOWN state. To reveal how cortical networks develop spontaneous activity, or conversely, how spontaneous activity structures cortical networks, we analyze the self-organization of a recurrent network model of excitatory and inhibitory neurons, which is realistic enough to replicate UP-DOWN states, with spike-timing-dependent plasticity (STDP). The individual neurons in the self-organized network exhibit a variety of temporal patterns in the two-state transitions. In addition, the model develops a feed-forward network-like structure that produces a diverse repertoire of precise sequences of the UP state. Our model shows that the self-organized activity well resembles the spontaneous activity of cortical networks if STDP is accompanied by the pruning of weak synapses. These results suggest that the two-state membrane potential transitions play an active role in structuring local cortical circuits.

Correlations and Population Dynamics in Cortical Networks.

Birgit Kriener — 2008-05-06T13:48:28-00:00

Neural computation (25 April 2008)

The function of cortical networks depends on the collective interplay between neurons and neuronal populations, which is reflected in the correlation of signals that can be recorded at different levels. To correctly interpret these observations it is important to understand the origin of neuronal correlations. Here we study how cells in large recurrent networks of excitatory and inhibitory neurons interact and how the associated correlations affect stationary states of idle network activity. We demonstrate that the structure of the connectivity matrix of such networks induces considerable correlations between synaptic currents as well as between subthreshold membrane potentials, provided Dale's principle is respected. If, in contrast, synaptic weights are randomly distributed, input correlations can vanish, even for densely connected networks. Although correlations are strongly attenuated when proceeding from membrane potentials to action potentials (spikes), the resulting weak correlations in the spike output can cause substantial fluctuations in the population activity, even in highly diluted networks. We show that simple mean-field models that take the structure of the coupling matrix into account can adequately describe the power spectra of the population activity. The consequences of Dale's principle on correlations and rate fluctuations are discussed in the light of recent experimental findings.

Polychronization: Computation with Spikes

Eugene Izhikevich — 2005-12-11T22:46:39-00:00

Neural Computation, Vol. 18, No. 2. (February 2006), pp. 245-282.

Regulation of spike timing in visual cortical circuits.

Paul Tiesinga — 2008-01-18T23:34:01-00:00

Nat Rev Neurosci (17 January 2008)

A train of action potentials (a spike train) can carry information in both the average firing rate and the pattern of spikes in the train. But can such a spike-pattern code be supported by cortical circuits? Neurons in vitro produce a spike pattern in response to the injection of a fluctuating current. However, cortical neurons in vivo are modulated by local oscillatory neuronal activity and by top-down inputs. In a cortical circuit, precise spike patterns thus reflect the interaction between internally generated activity and sensory information encoded by input spike trains. We review the evidence for precise and reliable spike timing in the cortex and discuss its computational role.

Neuronal oscillations in cortical networks.

G Buzsáki — 2005-10-05T00:55:25-00:00

Science, Vol. 304, No. 5679. (25 June 2004), pp. 1926-1929.

Clocks tick, bridges and skyscrapers vibrate, neuronal networks oscillate. Are neuronal oscillations an inevitable by-product, similar to bridge vibrations, or an essential part of the brain's design? Mammalian cortical neurons form behavior-dependent oscillating networks of various sizes, which span five orders of magnitude in frequency. These oscillations are phylogenetically preserved, suggesting that they are functionally relevant. Recent findings indicate that network oscillations bias input selection, temporally link neurons into assemblies, and facilitate synaptic plasticity, mechanisms that cooperatively support temporal representation and long-term consolidation of information.

Variability v.s. synchronicity of neuronal activity in local cortical network models with different wiring topologies.

K Kitano — 2008-07-08T12:06:18-00:00

Journal of computational neuroscience, Vol. 23, No. 2. (October 2007), pp. 237-250.

Dynamical behavior of a biological neuronal network depends significantly on the spatial pattern of synaptic connections among neurons. While neuronal network dynamics has extensively been studied with simple wiring patterns, such as all-to-all or random synaptic connections, not much is known about the activity of networks with more complicated wiring topologies. Here, we examined how different wiring topologies may influence the response properties of neuronal networks, paying attention to irregular spike firing, which is known as a characteristic of in vivo cortical neurons, and spike synchronicity. We constructed a recurrent network model of realistic neurons and systematically rewired the recurrent synapses to change the network topology, from a localized regular and a "small-world" network topology to a distributed random network topology. Regular and small-world wiring patterns greatly increased the irregularity or the coefficient of variation (Cv) of output spike trains, whereas such an increase was small in random connectivity patterns. For given strength of recurrent synapses, the firing irregularity exhibited monotonous decreases from the regular to the random network topology. By contrast, the spike coherence between an arbitrary neuron pair exhibited a non-monotonous dependence on the topological wiring pattern. More precisely, the wiring pattern to maximize the spike coherence varied with the strength of recurrent synapses. In a certain range of the synaptic strength, the spike coherence was maximal in the small-world network topology, and the long-range connections introduced in this wiring changed the dependence of spike synchrony on the synaptic strength moderately. However, the effects of this network topology were not really special in other properties of network activity.

The number of synaptic inputs and the synchrony of large, sparse neuronal networks.

D Golomb — 2007-02-23T17:10:42-00:00

Neural Comput, Vol. 12, No. 5. (May 2000), pp. 1095-1139.

The prevalence of coherent oscillations in various frequency ranges in the central nervous system raises the question of the mechanisms that synchronize large populations of neurons. We study synchronization in models of large networks of spiking neurons with random sparse connectivity. Synchrony occurs only when the average number of synapses, M, that a cell receives is larger than a critical value, Mc. Below Mc, the system is in an asynchronous state. In the limit of weak coupling, assuming identical neurons, we reduce the model to a system of phase oscillators that are coupled via an effective interaction, gamma. In this framework, we develop an approximate theory for sparse networks of identical neurons to estimate Mc analytically from the Fourier coefficients of gamma. Our approach relies on the assumption that the dynamics of a neuron depend mainly on the number of cells that are presynaptic to it. We apply this theory to compute Mc for a model of inhibitory networks of integrate-and-fire (I&F) neurons as a function of the intrinsic neuronal properties (e.g., the refractory period Tr), the synaptic time constants, and the strength of the external stimulus, Iext. The number Mc is found to be nonmonotonous with the strength of Iext. For Tr = 0, we estimate the minimum value of Mc over all the parameters of the model to be 363.8. Above Mc, the neurons tend to fire in smeared one-cluster states at high firing rates and smeared two-or-more-cluster states at low firing rates. Refractoriness decreases Mc at intermediate and high firing rates. These results are compared to numerical simulations. We show numerically that systems with different sizes, N, behave in the same way provided the connectivity, M, is such that 1/Meff = 1/M - 1/N remains constant when N varies. This allows extrapolating the large N behavior of a network from numerical simulations of networks of relatively small sizes (N = 800 in our case). We find that our theory predicts with remarkable accuracy the value of Mc and the patterns of synchrony above Mc, provided the synaptic coupling is not too large. We also study the strong coupling regime of inhibitory sparse networks. All of our simulations demonstrate that increasing the coupling strength reduces the level of synchrony of the neuronal activity. Above a critical coupling strength, the network activity is asynchronous. We point out a fundamental limitation for the mechanisms of synchrony relying on inhibition alone, if heterogeneities in the intrinsic properties of the neurons and spatial fluctuations in the external input are also taken into account.

Are corticothalamic 'up' states fragments of wakefulness?

A Destexhe — 2008-01-18T14:00:52-00:00

Trends Neurosci, Vol. 30, No. 7. (July 2007), pp. 334-342.

The slow (<1 Hz) oscillation, with its alternating 'up' and 'down' states in individual neurons, is a defining feature of the electroencephalogram (EEG) during slow-wave sleep (SWS). Although this oscillation is well preserved across mammalian species, its physiological role is unclear. Electrophysiological and computational evidence from the cortex and thalamus now indicates that slow-oscillation 'up' states and the 'activated' state of wakefulness are remarkably similar dynamic entities. This is consistent with behavioural experiments suggesting that slow-oscillation 'up' states provide a context for the replay, and possible consolidation, of previous experience. In this scenario, the T-type Ca(2+) channel-dependent bursts of action potentials that initiate each 'up' state in thalamocortical (TC) neurons might function as triggers for synaptic and cellular plasticity in corticothalamic networks. This review is part of the INMED/TINS special issue Physiogenic and pathogenic oscillations: the beauty and the beast, based on presentations at the annual INMED/TINS symposium (http://inmednet.com).

Activated cortical states: Experiments, analyses and models.

S El Boustani — 2008-01-18T13:59:26-00:00

J Physiol Paris, Vol. 101, No. 1-3. (y 2007), pp. 99-109.

In awake animals, the cerebral cortex displays an "activated" state, with distinct characteristics compared to other states like slow-wave sleep or anesthesia. These characteristics include a sustained depolarized membrane potential (V(m)) and irregular firing activity. In the present paper, we evaluate our understanding of cortical activated states from a computational neuroscience point of view. We start by reviewing the electrophysiological characteristics of activated cortical states based on recordings and analysis performed in awake cat association cortex. These analyses show that cortical activity is characterized by an apparent Poisson-distributed stochastic dynamics, both at the single-cell and population levels, and that single cells display a high-conductance state dominated by inhibition. We next overview computational models of the "awake" cortex, and perform the same analyses as in the experiments. Many properties identified experimentally are indeed reproduced by models, such as depolarized V(m), irregular firing with apparent Poisson statistics, and the determinant role of inhibitory fluctuations on spiking. However, other features are not well reproduced, such as firing statistics and the conductance state of the membrane, suggesting that the network state displayed by models is not entirely correct. We also show how networks can approach a correct conductance state, suggesting ways by which future models will generate activity fully consistent with experimental data.

Cannabinoid-mediated disinhibition and working memory: dynamical interplay of multiple feedback mechanisms in a continuous attractor model of prefrontal cortex.

E Carter — 2008-03-07T15:59:09-00:00

Cereb Cortex, Vol. 17 Suppl 1 (September 2007)

Recurrent excitation is believed to underlie persistent neural activity observed in the prefrontal cortex and elsewhere during working memory. However, other positive and negative feedback mechanisms, operating on disparate timescales, may also play significant roles in determining the behavior of a working memory circuit. In this study, we examined dynamical interactions of multiple feedback mechanisms in a biophysically based neural model of spatial working memory. In such continuous attractor networks, a self-sustained activity pattern tends to drift randomly, resulting in a decreased accuracy of memory over time. Moreover, attractor states become unstable when spike-frequency adaptation reduces the excitability of persistently firing pyramidal neurons. Here, we show that a slow activity-dependent local disinhibition, namely cannabinoid-dependent depolarization-induced suppression of inhibition (DSI), can counteract these destabilizing effects, rendering working memory function more robust. In addition, the slow DSI effect gives rise to trial-to-trial correlations of memory-guided behavioral responses. On the other hand, computer simulations revealed that a global cannabinoid agonist (mimicking the effect of drug intake) yields the opposite effect. Thus, this work suggests a circuit scenario according to which endogenous DSI is beneficial for, whereas an exogenous drug such as marijuana is detrimental to, working memory and possibly other prefrontal functions.

Spike-timing-dependent plasticity in balanced random networks.

A Morrison — 2007-09-25T16:29:47-00:00

Neural Comput, Vol. 19, No. 6. (June 2007), pp. 1437-1467.

The balanced random network model attracts considerable interest because it explains the irregular spiking activity at low rates and large membrane potential fluctuations exhibited by cortical neurons in vivo. In this article, we investigate to what extent this model is also compatible with the experimentally observed phenomenon of spike-timing-dependent plasticity (STDP). Confronted with the plethora of theoretical models for STDP available, we reexamine the experimental data. On this basis, we propose a novel STDP update rule, with a multiplicative dependence on the synaptic weight for depression, and a power law dependence for potentiation. We show that this rule, when implemented in large, balanced networks of realistic connectivity and sparseness, is compatible with the asynchronous irregular activity regime. The resultant equilibrium weight distribution is unimodal with fluctuating individual weight trajectories and does not exhibit development of structure. We investigate the robustness of our results with respect to the relative strength of depression. We introduce synchronous stimulation to a group of neurons and demonstrate that the decoupling of this group from the rest of the network is so severe that it cannot effectively control the spiking of other neurons, even those with the highest convergence from this group.

The dynamical stability of reverberatory neural circuits.

J Tegnér — 2007-01-15T15:24:15-00:00

Biol Cybern, Vol. 87, No. 5-6. (December 2002), pp. 471-481.

The concept of reverberation proposed by Lorente de Nó and Hebb is key to understanding strongly recurrent cortical networks. In particular, synaptic reverberation is now viewed as a likely mechanism for the active maintenance of working memory in the prefrontal cortex. Theoretically, this has spurred a debate as to how such a potentially explosive mechanism can provide stable working-memory function given the synaptic and cellular mechanisms at play in the cerebral cortex. We present here new evidence for the participation of NMDA receptors in the stabilization of persistent delay activity in a biophysical network model of conductance-based neurons. We show that the stability of working-memory function, and the required NMDA/AMPA ratio at recurrent excitatory synapses, depend on physiological properties of neurons and synaptic interactions, such as the time constants of excitation and inhibition, mutual inhibition between interneurons, differential NMDA receptor participation at excitatory projections to pyramidal neurons and interneurons, or the presence of slow intrinsic ion currents in pyramidal neurons. We review other mechanisms proposed to enhance the dynamical stability of synaptically generated attractor states of a reverberatory circuit. This recent work represents a necessary and significant step towards testing attractor network models by cortical electrophysiology.

Rapid state switching in balanced cortical network models

MV Tsodyks — 2007-09-21T12:01:23-00:00

Network: Computation in Neural Systems, Vol. 6, No. 2. (1995), pp. 111-124.

We have explored a network model of cortical microcircuits based on integrate-and-fire neurons in a regime where the reset following a spike is small, recurrent excitation is balanced by feedback inhibition, and the activity is highly irregular. This regime cannot be described by a mean-field theory based on average activity levels because essential features of the model depend on fluctuations from the average. We propose a new way of scaling the strength of synaptic interaction with the size of the network: rather than scale the amplitude of the synapse we scale the neurotransmitter release probabilities with the number of inputs to keep the average input constant. This is consistent with the low transmitter release probability observed in a majority of hippocampal synapses. Another prominent feature of this regime is the ability of the network to switch rapidly between different states, as demonstrated in a model based on an orientation columns in the mammalian visual cortex. Both network and intrinsic properties of neurons contribute to achieving the balance condition that allows rapid state switching.

Interplay between a phase response curve and spike-timing-dependent plasticity leading to wireless clustering.

H Câteau — 2008-07-31T16:08:41-00:00

Physical review. E, Statistical, nonlinear, and soft matter physics, Vol. 77, No. 5 Pt 1. (May 2008)

A phase response curve (PRC) characterizes the signal transduction between oscillators such as neurons on a fixed network in a minimal manner, while spike-timing-dependent plasiticity (STDP) characterizes the way of rewiring networks in an activity-dependent manner. This paper demonstrates that these two key properties both related to the interaction times of oscillators work synergetically to carve functionally useful circuits. STDP working on neurons that prefer asynchrony converts the initial asynchronous firing to clustered firing with synchrony within a cluster. They get synchronized within a cluster despite their preference to asynchrony because STDP selectively disrupts intracluster connections, which we call wireless clustering. Our PRC analysis reveals a triad mechanism: the network structure affects how the PRC is read out to determine the synchrony tendency, the synchrony tendency affects how the STDP works, and STDP affects the network structure, closing the loop.

Connection topology selection in central pattern generators by maximizing the gain of information.

GR Stiesberg — 2008-04-09T16:24:12-00:00

Neural computation, Vol. 19, No. 4. (April 2007), pp. 974-993.

A study of a general central pattern generator (CPG) is carried out by means of a measure of the gain of information between the number of available topology configurations and the output rhythmic activity. The neurons of the CPG are chaotic Hindmarsh-Rose models that cooperate dynamically to generate either chaotic or regular spatiotemporal patterns. These model neurons are implemented by computer simulations and electronic circuits. Out of a random pool of input configurations, a small subset of them maximizes the gain of information. Two important characteristics of this subset are emphasized: (1) the most regular output activities are chosen, and (2) none of the selected input configurations are networks with open topology. These two principles are observed in living CPGs as well as in model CPGs that are the most efficient in controlling mechanical tasks, and they are evidence that the information-theoretical analysis can be an invaluable tool in searching for general properties of CPGs.

Inter-trial neuronal activity in inferior temporal cortex: a putative vehicle to generate long-term visual associations.

V Yakovlev — 2008-03-07T16:45:09-00:00

Nat Neurosci, Vol. 1, No. 4. (August 1998), pp. 310-317.

When monkeys perform a delayed match-to-sample task, some neurons in the anterior inferotemporal cortex show sustained activity following the presentation of specific visual stimuli, typically only those that are shown repeatedly. When sample stimuli are shown in a fixed temporal order, the few images that evoke delay activity in a given neuron are often neighboring stimuli in the sequence, suggesting that this delay activity may be the neural correlate of associative long-term memory. Here we report that stimulus-selective sustained activity is also evident following the presentation of the test stimulus in the same task. We use a neural network model to demonstrate that persistent stimulus-selective activity across the intertrial interval can lead to similar mnemonic representations (distributions of delay activity across the neural population) for neighboring visual stimuli. Thus, inferotemporal cortex may contain neural machinery for generating long-term stimulus-stimulus associations.

Precisely timed spatiotemporal patterns of neural activity in dissociated cortical cultures.

JD Rolston — 2007-08-18T21:27:04-00:00

Neuroscience, Vol. 148, No. 1. (10 August 2007), pp. 294-303.

Recurring patterns of neural activity, a potential substrate of both information transfer and transformation in cortical networks, have been observed in the intact brain and in brain slices. Do these patterns require the inherent cortical microcircuitry of such preparations or are they a general property of self-organizing neuronal networks? In networks of dissociated cortical neurons from rats-which lack evidence of the intact brain's intrinsic cortical architecture-we have observed a robust set of spontaneously repeating spatiotemporal patterns of neural activity, using a template-matching algorithm that has been successful both in vivo and in brain slices. The observed patterns in cultured monolayer networks are stable over minutes of extracellular recording, occur throughout the culture's development, and are temporally precise within milliseconds. The identification of these patterns in dissociated cultures opens a powerful methodological avenue for the study of such patterns, and their persistence despite the topological and morphological rearrangements of cellular dissociation is further evidence that precisely timed patterns are a universal emergent feature of self-organizing neuronal networks.

Artificial synaptic modification reveals a dynamical invariant in the pyloric CPG

Marcelo Reyes — 2008-04-08T11:38:34-00:00

European Journal of Applied Physiology, Vol. 102, No. 6. (1 April 2008), pp. 667-675.

Abstract The sequential firing of neurons in central pattern generators (CPGs) is generally thought to be a result of an interaction between intrinsic cellular and synaptic properties of the component neurons. Due to experimental limitations, it is usually difficult to address the role of each of these properties separately. We have done so by using the crustacean stomatogastric CPG and the dynamic clamp technique to measure how the network responds to the selective modification of an individual important synapse. Our results show that the burst periods and the phase lags between the constrictor (LP) and dilator (PD) neurons across preparations showed significant variability during equivalent experimental manipulations. Despite this variability, the ratio between the change in the burst period and the change in the phase lag between the same neurons was tightly preserved in all preparations, revealing a dynamical invariant in the system. This dynamical invariant was preserved despite the individual variability in the period and phase lag measurements, suggesting a tightly regulated constraint between the parameters of the network.

Persistent dynamic attractors in activity patterns of cultured neuronal networks.

DA Wagenaar — 2008-03-14T20:53:39-00:00

Phys Rev E Stat Nonlin Soft Matter Phys, Vol. 73, No. 5 Pt 1. (May 2006)

Three remarkable features of the nervous system--complex spatiotemporal patterns, oscillations, and persistent activity--are fundamental to such diverse functions as stereotypical motor behavior, working memory, and awareness. Here we report that cultured cortical networks spontaneously generate a hierarchical structure of periodic activity with a strongly stereotyped population-wide spatiotemporal structure demonstrating all three fundamental properties in a recurring pattern. During these "superbursts," the firing sequence of the culture periodically converges to a dynamic attractor orbit. Precursors of oscillations and persistent activity have previously been reported as intrinsic properties of the neurons. However, complex spatiotemporal patterns that are coordinated in a large population of neurons and persist over several hours--and thus are capable of representing and preserving information--cannot be explained by known oscillatory properties of isolated neurons. Instead, the complexity of the observed spatiotemporal patterns implies large-scale self-organization of neurons interacting in a precise temporal order even in vitro, in cultures usually considered to have random connectivity.

Neuronal assemblies: single cortical neurons are obedient members of a huge orchestra.

A Grinvald — 2008-01-18T13:54:48-00:00

Biopolymers, Vol. 68, No. 3. (March 2003), pp. 422-436.

Spontaneous cortical activity of single neurons is often either dismissed as noise, or is regarded as carrying no functional significance and hence is ignored. Our findings suggest that such concepts should be revised. We explored the coherent population activity of neuronal assemblies in primary sensory area in the absence of a sensory input. Recent advances in real-time optical imaging based on voltage-sensitive dyes (VSDI) have facilitated exploration of population activity and its intimate relationship to the activity of individual cortical neurons. It has been shown by in vivo intracellular recordings that the dye signal measures the sum of the membrane potential changes in all the neuronal elements in the imaged area, emphasizing subthreshold synaptic potentials and dendritic action potentials in neuronal arborizations originating from neurons in all cortical layers whose dendrites reach the superficial cortical layers. Thus, the VSDI has allowed us to image the rather illusive activity in neuronal dendrites that cannot be readily explored by single unit recordings. Surprisingly, we found that the amplitude of this type of ongoing subthreshold activity is of the same order of magnitude as evoked activity. We also found that this ongoing activity exhibited high synchronization over many millimeters of cortex. We then investigated the influence of ongoing activity on the evoked response, and showed that the two interact strongly. Furthermore, we found that cortical states that were previously associated only with evoked activity can actually be observed also in the absence of stimulation, for example, the cortical representation of a given orientation may appear without any visual input. This demonstration suggests that ongoing activity may also play a major role in other cortical function by providing a neuronal substrate for the dependence of sensory information processing on context, behavior, memory and other aspects of cognitive function.

Spike-timing-dependent plasticity for neurons with recurrent connections

Burkitt — 2007-06-07T22:18:05-00:00

Biological Cybernetics, Vol. 96, No. 5. (May 2007), pp. 533-546.

Dynamical Cell Assembly Hypothesis - Theoretical Possibility of Spatio-temporal Coding in the Cortex.

Minoru Tsukada — 2008-05-06T15:18:57-00:00

Neural networks : the official journal of the International Neural Network Society, Vol. 9, No. 8. (November 1996), pp. 1303-1350.

This paper is an attempt to understand how knowledge and events are represented and processed in the brain. An important point is the question of what carries information in the brain - the mean firing rate or the timing of spikes? The idea we want to pursue is that, contrary to the traditional view, the brain might use higher order statistics, which means in essence that timing of spikes plays a critical role in encoding, representing, and processing knowledge and events in the brain.A recently revealed salient nature of cortical pyramidal cells, i.e., the high variability of inter-spike intervals suggests that a cortical neuron may function effectively as a coincidence detector. At the same time, non-classical experimental phenomena of task-related, short time-scaled dynamical modulations of temporal correlations between neurons suggest a non-classical view on the dynamics working in the brain. In response to contexts or external events, a group of neurons, a dynamical cell assembly, spontaneously organizes, linked temporarily by coincident timing of incident spikes, showing correlated firing with each other. This is an emergent property of neuronal populations in the cortex.We make a theoretical exploration on issues as (1) the description of such emergent dynamics of dynamical cell assemblies based on the working hypothesis that a cortica neuron functions effectively as a coincidence detector, and (2) the principle of spatio-temporal coding based on the hypothetical emergent dynamics. Note that the conventional rate coding hypothesis does not give satisfactory answers to fundamental questions on the representation and processing of knowledge or events in the brain, e.g., the questions of cross-modular integration of information or the binding problem, and representation of hierarchical knowledge etc.The first goal is to give a non-encyclopedic review on (1) the temporal structure of spike sequences, focusing on the question of the basic code in the brain; (2) the paradigms on representation of knowledge and events proposed from a theoretical or experimental basis. The classical paradigms of Hebb and Barlow with their experimental and theoretical critiques, and more recently proposed experiment-based paradigms are reviewed. Also a review is given on (3) the experimentally observed spatio-temporal structure of spike dynamics.The second goal is to give a description of the dynamical cell assembly - the central concept in this paper. Aside from the question of physiological basis, we make a theoretical study, under a working hypothesis that a cortical neuron functions effectively as a coincidence detector, on the emergent dynamics of cell assemblies, and also examine how the observed experimental data could be explained within this theoretical setting.We also try to give the principle of spatio-temporal coding based on the dynamical cell assembly framework. A key concept is the internal mechanism of "dialogue" among neuronal pools in the brain. This provides a dynamical foundation of bi-directional interactions for the linkage of distant modules to create integrated information. We present a simple model in order to illustrate the working principle of coincidence detector systems. Relations with other temporal coding paradigms are also discussed. Copyright 1996 Elsevier Science Ltd.

Formation of temporal-feature maps by axonal propagation of synaptic learning.

R Kempter — 2006-02-12T06:59:05-00:00

Proc Natl Acad Sci U S A, Vol. 98, No. 7. (27 March 2001), pp. 4166-4171.

Computational maps are of central importance to a neuronal representation of the outside world. In a map, neighboring neurons respond to similar sensory features. A well studied example is the computational map of interaural time differences (ITDs), which is essential to sound localization in a variety of species and allows resolution of ITDs of the order of 10 micros. Nevertheless, it is unclear how such an orderly representation of temporal features arises. We address this problem by modeling the ontogenetic development of an ITD map in the laminar nucleus of the barn owl. We show how the owl's ITD map can emerge from a combined action of homosynaptic spike-based Hebbian learning and its propagation along the presynaptic axon. In spike-based Hebbian learning, synaptic strengths are modified according to the timing of pre- and postsynaptic action potentials. In unspecific axonal learning, a synapse's modification gives rise to a factor that propagates along the presynaptic axon and affects the properties of synapses at neighboring neurons. Our results indicate that both Hebbian learning and its presynaptic propagation are necessary for map formation in the laminar nucleus, but the latter can be orders of magnitude weaker than the former. We argue that the algorithm is important for the formation of computational maps, when, in particular, time plays a key role.

Division of labor among distinct subtypes of inhibitory neurons in a cortical microcircuit of working memory.

XJ Wang — 2006-11-30T15:27:29-00:00

Proc Natl Acad Sci U S A, Vol. 101, No. 5. (3 February 2004), pp. 1368-1373.

A conspicuous feature of cortical organization is the wide diversity of inhibitory interneurons; their differential computational functions remain unclear. Here we propose a local cortical circuit in which three major subtypes of interneurons play distinct roles. In a model designed for spatial working memory, stimulus tuning of persistent activity arises from the concerted action of widespread inhibition mediated by perisoma-targeting (parvalbumin-containing) interneurons and localized disinhibition of pyramidal cells via interneuron-targeting (calretinin-containing) interneurons. Moreover, resistance against distracting stimuli (a fundamental property of working memory) is dynamically controlled by dendrite-targeting (calbindin-containing) interneurons. The experimental observation of inverted tuning curves of monkey prefrontal neurons recorded during working memory supports a key model prediction. This work suggests a framework for understanding the division of labor and cooperation among different inhibitory cell types in a recurrent cortical circuit.

Large-scale model of mammalian thalamocortical systems

Eugene Izhikevich — 2008-02-28T20:15:52-00:00

Proceedings of the National Academy of Sciences (21 February 2008), 0712231105.

The understanding of the structural and dynamic complexity of mammalian brains is greatly facilitated by computer simulations. We present here a detailed large-scale thalamocortical model based on experimental measures in several mammalian species. The model spans three anatomical scales. (i) It is based on global (white-matter) thalamocortical anatomy obtained by means of diffusion tensor imaging (DTI) of a human brain. (ii) It includes multiple thalamic nuclei and six-layered cortical microcircuitry based on in vitro labeling and three-dimensional reconstruction of single neurons of cat visual cortex. (iii) It has 22 basic types of neurons with appropriate laminar distribution of their branching dendritic trees. The model simulates one million multicompartmental spiking neurons calibrated to reproduce known types of responses recorded in vitro in rats. It has almost half a billion synapses with appropriate receptor kinetics, short-term plasticity, and long-term dendritic spike-timing-dependent synaptic plasticity (dendritic STDP). The model exhibits behavioral regimes of normal brain activity that were not explicitly built-in but emerged spontaneously as the result of interactions among anatomical and dynamic processes. We describe spontaneous activity, sensitivity to changes in individual neurons, emergence of waves and rhythms, and functional connectivity on different scales. 10.1073/pnas.0712231105

Coding and learning of behavioral sequences.

O Melamed — 2008-01-18T13:52:39-00:00

Trends Neurosci, Vol. 27, No. 1. (January 2004)

Decoding of Temporal Intervals From Cortical Ensemble Activity

Mikhail Lebedev — 2008-01-25T18:05:24-00:00

J Neurophysiol, Vol. 99, No. 1. (1 January 2008), pp. 166-186.

Neurophysiological, neuroimaging, and lesion studies point to a highly distributed processing of temporal information by cortico-basal ganglia-thalamic networks. However, there are virtually no experimental data on the encoding of behavioral time by simultaneously recorded cortical ensembles. We predicted temporal intervals from the activity of hundreds of neurons recorded in motor and premotor cortex as rhesus monkeys performed self-timed hand movements. During the delay periods, when animals had to estimate temporal intervals and prepare hand movements, neuronal ensemble activity encoded both the time that elapsed from the previous hand movement and the time until the onset of the next. The neurons that were most informative of these temporal intervals increased or decreased their rates throughout the delay until reaching a threshold value, at which point a movement was initiated. Variability in the self-timed delays was explainable by the variability of neuronal rates, but not of the threshold. In addition to predicting temporal intervals, the same neuronal ensemble activity was informative for generating predictions that dissociated the delay periods of the task from the movement periods. Left hemispheric areas were the best source of predictions in one bilaterally implanted monkey overtrained to perform the task with the right hand. However, after that monkey learned to perform the task with the left hand, its left hemisphere continued and the right hemisphere started contributing to the prediction. We suggest that decoding of temporal intervals from bilaterally recorded cortical ensembles could improve the performance of neural prostheses for restoration of motor function. 10.1152/jn.00734.2007

Emergence of scaling in random networks

Albert-Laszlo Barabasi — 2005-01-20T00:12:33-00:00

(21 October 1999)

Systems as diverse as genetic networks or the world wide web are best described as networks with complex topology. A common property of many large networks is that the vertex connectivities follow a scale-free power-law distribution. This feature is found to be a consequence of the two generic mechanisms that networks expand continuously by the addition of new vertices, and new vertices attach preferentially to already well connected sites. A model based on these two ingredients reproduces the observed stationary scale-free distributions, indicating that the development of large networks is governed by robust self-organizing phenomena that go beyond the particulars of the individual systems.

The high-conductance state of cortical networks.

A Kumar — 2008-01-30T15:17:41-00:00

Neural Comput, Vol. 20, No. 1. (January 2008), pp. 1-43.

We studied the dynamics of large networks of spiking neurons with conductance-based (nonlinear) synapses and compared them to networks with current-based (linear) synapses. For systems with sparse and inhibition-dominated recurrent connectivity, weak external inputs induced asynchronous irregular firing at low rates. Membrane potentials fluctuated a few millivolts below threshold, and membrane conductances were increased by a factor 2 to 5 with respect to the resting state. This combination of parameters characterizes the ongoing spiking activity typically recorded in the cortex in vivo. Many aspects of the asynchronous irregular state in conductance-based networks could be sufficiently well characterized with a simple numerical mean field approach. In particular, it correctly predicted an intriguing property of conductance-based networks that does not appear to be shared by current-based models: they exhibit states of low-rate asynchronous irregular activity that persist for some period of time even in the absence of external inputs and without cortical pacemakers. Simulations of larger networks (up to 350,000 neurons) demonstrated that the survival time of self-sustained activity increases exponentially with network size.

The Emergence of Up and Down States in Cortical Networks.

David Holcman — 2006-03-31T21:53:22-00:00

PLoS Comput Biol, Vol. 2, No. 3. (24 March 2006)

The cerebral cortex is continuously active in the absence of external stimuli. An example of this spontaneous activity is the voltage transition between an Up and a Down state, observed simultaneously at individual neurons. Since this phenomenon could be of critical importance for working memory and attention, its explanation could reveal some fundamental properties of cortical organization. To identify a possible scenario for the dynamics of Up-Down states, we analyze a reduced stochastic dynamical system that models an interconnected network of excitatory neurons with activity-dependent synaptic depression. The model reveals that when the total synaptic connection strength exceeds a certain threshold, the phase space of the dynamical system contains two attractors, interpreted as Up and Down states. In that case, synaptic noise causes transitions between the states. Moreover, an external stimulation producing a depolarization increases the time spent in the Up state, as observed experimentally. We therefore propose that the existence of Up-Down states is a fundamental and inherent property of a noisy neural ensemble with sufficiently strong synaptic connections.

When pyramidal neurons lock, when they respond chaotically, and when they like to synchronize.

R Stoop — 2007-11-08T13:27:37-00:00

Neurosci Res, Vol. 36, No. 1. (January 2000), pp. 81-91.

We give an overview on the locking properties of perturbed regularly firing pyramidal neurons, as a function of perturbation strength, self-spiking frequency and perturbation frequency. For inhibitory perturbations, instead of locking chaotic response emerges for a whole range of parameters. This suggests that global synchronization on the set of inhibitory connections may easily be achieved.

Persistent Activity in Neural Networks with Dynamic Synapses

Omri Barak — 2007-02-23T07:55:24-00:00

PLoS Computational Biology, Vol. 3, No. 2. (1 February 2007), e35.

Persistent activity states (attractors), observed in several neocortical areas after the removal of a sensory stimulus, are believed to be the neuronal basis of working memory. One of the possible mechanisms that can underlie persistent activity is recurrent excitation mediated by intracortical synaptic connections. A recent experimental study revealed that connections between pyramidal cells in prefrontal cortex exhibit various degrees of synaptic depression and facilitation. Here we analyze the effect of synaptic dynamics on the emergence and persistence of attractor states in interconnected neural networks. We show that different combinations of synaptic depression and facilitation result in qualitatively different network dynamics with respect to the emergence of the attractor states. This analysis raises the possibility that the framework of attractor neural networks can be extended to represent time-dependent stimuli.

Coordination of Motor Neurons by the Leech Heartbeat Central Pattern Generator: Modeling the Role of the Inhibitory Input and Electrical Coupling.

Paul S Garcia — 2008-06-30T13:46:47-00:00

Journal of neurophysiology (25 June 2008)

Previously we presented a quantitative description of the spatiotemporal pattern of inhibitory synaptic input from the heartbeat CPG to segmental motor neurons that drive heartbeat in the medicinal leech and the resultant coordination of CPG interneurons and motor neurons (Norris et al. 2006; Norris et al. 2007a; b). To begin elucidating the mechanisms of coordination, we explore intersegmental and side-to-side coordination in an ensemble model of all heart motor neurons and their known synaptic inputs and electrical coupling. Model motor neuron intrinsic properties were kept simple enabling us to determine the extent to which input and electrical coupling acting together can account for observed coordination in the living system in the absence of a substantive contribution from the motor neurons themselves. The living system produces an asymmetric motor pattern: motor neurons on one side fire nearly in synchrony (synchronous), while on the other they fire in a rear-to-front progression (peristaltic). The model reproduces the general trends of intersegmental and side-to-side phase relations among motor neurons, but the match with the living system is not quantitatively accurate. Thus realistic (experimentally determined) inputs do not produce similarly realistic output in our model suggesting that motor neuron intrinsic properties may contribute to their coordination. By varying parameters that determine electrical coupling, conduction delays, intraburst synaptic plasticity, and motor neuron excitability, we show that the most important determinant of intersegmental and side-to-side phase relations in the model was the spatiotemporal pattern of synaptic inputs, yet phasing was influenced significantly by electrical coupling.

Natural computation measured as a reduction of complexity.

R Stoop — 2007-11-08T13:24:35-00:00

Chaos, Vol. 14, No. 3. (September 2004), pp. 675-679.

We argue that the deeper nature of computation is to reduce the statistical obstruction against prediction. From this, we derive an explicit measure of computation for general, artificial as well as natural, systems (electronic circuits, neurons, mechanical devices, etc.). The applicability and usefulness of this concept is demonstrated using well-studied families of dynamical systems, as well as experimental time series from cortical neurons.

Synfire chains and cortical songs: temporal modules of cortical activity.

Y Ikegaya — 2008-03-05T22:51:56-00:00

Science, Vol. 304, No. 5670. (23 April 2004), pp. 559-564.

How can neural activity propagate through cortical networks built with weak, stochastic synapses? We find precise repetitions of spontaneous patterns of synaptic inputs in neocortical neurons in vivo and in vitro. These patterns repeat after minutes, maintaining millisecond accuracy. Calcium imaging of slices reveals reactivation of sequences of cells during the occurrence of repeated intracellular synaptic patterns. The spontaneous activity drifts with time, engaging different cells. Sequences of active neurons have distinct spatial structures and are repeated in the same order over tens of seconds, revealing modular temporal dynamics. Higher order sequences are replayed with compressed timing.

Beyond Two-Cell Networks: Experimental Measurement of Neuronal Responses to Multiple Synaptic Inputs

Theoden Netoff — 2005-04-19T10:34:07-00:00

Journal of Computational Neuroscience, Vol. 18, No. 3. (June 2005), pp. 287-295.

Odour encoding in olfactory neuronal networks beyond synchronization.

M Christen — 2007-11-08T13:19:09-00:00

Neuroreport, Vol. 17, No. 14. (2 October 2006), pp. 1499-1502.

It has been suggested that odour encoding in olfactory systems occurs by synchronized firing in neuronal populations. Neurons correlated in terms of the Lempel-Ziv distance of spike trains and the sequential superparamagnetic clustering algorithm belong to the same cluster if they show similar, but not necessarily synchronous, firing patterns. Using multielectrode array recordings from the rat olfactory bulb, we have determined cluster incidence and stability in the neuronal network using both the Lempel-Ziv distance and a measure of synchronization. In the Lempel-Ziv paradigm, we found pronounced stabilization and destabilization effects in the neuronal network in response to odour presentation when compared with the synchronization paradigm. This suggests that synchronization alone may be insufficient for understanding olfactory coding.

STDP Provides the Substrate for Igniting Synfire Chains by Spatiotemporal Input Patterns.

Ryosuke Hosaka — 2007-12-10T18:33:12-00:00

Neural Comput (28 November 2007)

Spike-timing-dependent synaptic plasticity (STDP), which depends on the temporal difference between pre- and postsynaptic action potentials, is observed in the cortices and hippocampus Although several theoretical and experimental studies have revealed its fundamental aspects, its functional role remains unclear. To examine how an input spatiotemporal spike pattern is altered by STDP, we observed the output spike patterns of a spiking neural network model with an asymmetrical STDP rule when the input spatiotemporal pattern is repeatedly applied. The spiking neural network comprises excitatory and inhibitory neurons that exhibit local interactions. Numerical experiments show that the spiking neural network generates a single global synchrony whose relative timing depends on the input spatiotemporal pattern and the neural network structure. This result implies that the spiking neural network learns the transformation from spatiotemporal to temporal information. In the literature, the origin of the synfire chain has not been sufficiently focused on. Our results indicate that spiking neural networks with STDP can ignite synfire chains in the cortices.

Synchrony unbound: a critical evaluation of the temporal binding hypothesis.

MN Shadlen — 2005-10-10T20:36:24-00:00

Neuron, Vol. 24, No. 1. (September 1999)