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Wilson–Cowan Equations for Neocortical Dynamics

TLDR
This work describes how the Markov models account for many recent measurements of the resting or spontaneous activity of the neocortex, and shows that the power spectrum of large-scale neocortical activity has a Brownian motion baseline, and that the statistical structure of the random bursts of spiking activity found near the resting state indicates that such a state can be represented as a percolation process on a random graph, called directed percolations.
Abstract
In 1972-1973 Wilson and Cowan introduced a mathematical model of the population dynamics of synaptically coupled excitatory and inhibitory neurons in the neocortex. The model dealt only with the mean numbers of activated and quiescent excitatory and inhibitory neurons, and said nothing about fluctuations and correlations of such activity. However, in 1997 Ohira and Cowan, and then in 2007-2009 Buice and Cowan introduced Markov models of such activity that included fluctuation and correlation effects. Here we show how both models can be used to provide a quantitative account of the population dynamics of neocortical activity.We first describe how the Markov models account for many recent measurements of the resting or spontaneous activity of the neocortex. In particular we show that the power spectrum of large-scale neocortical activity has a Brownian motion baseline, and that the statistical structure of the random bursts of spiking activity found near the resting state indicates that such a state can be represented as a percolation process on a random graph, called directed percolation.Other data indicate that resting cortex exhibits pair correlations between neighboring populations of cells, the amplitudes of which decay slowly with distance, whereas stimulated cortex exhibits pair correlations which decay rapidly with distance. Here we show how the Markov model can account for the behavior of the pair correlations.Finally we show how the 1972-1973 Wilson-Cowan equations can account for recent data which indicates that there are at least two distinct modes of cortical responses to stimuli. In mode 1 a low intensity stimulus triggers a wave that propagates at a velocity of about 0.3 m/s, with an amplitude that decays exponentially. In mode 2 a high intensity stimulus triggers a larger response that remains local and does not propagate to neighboring regions.

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Cognitive task information is transferred between brain regions via resting-state network topology.

TL;DR: It is reported that the transfer of diverse, task- rule information in distributed brain regions can be predicted based on estimated activity flow through resting-state network connections, and that these task-rule information transfers are coordinated by global hub regions within cognitive control networks.
Journal ArticleDOI

Network dynamics of coupled oscillators and phase reduction techniques

TL;DR: This report shows in detail how the concepts of phase reduction for single oscillators can be extended and applied to oscillator networks and, hence, may allow for an improved derivation of the phase dynamics of coupled oscillators.
Journal ArticleDOI

Firing rate equations require a spike synchrony mechanism to correctly describe fast oscillations in inhibitory networks.

TL;DR: The meanfield equations represent an extension of the standard Wilson-Cowan equations in which spike synchrony is also correctly described and even in this limit synchronous spiking can be elicited by inputs which fluctuate on a time-scale of the membrane time-constant of the neurons.
Posted ContentDOI

Cognitive task information is transferred between brain regions via resting-state network topology

TL;DR: It is reported that the transfer of diverse, task-rule information in distributed brain regions can be predicted based on estimated activity flow through resting-state network connections, and it is found that these task- rule information transfers are coordinated by global hub regions within cognitive control networks.
References
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Exact Stochastic Simulation of Coupled Chemical Reactions

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TL;DR: In this article, the authors introduce the Fokker-planck equation, the Langevin approach, and the diffusion type of the master equation, as well as the statistics of jump events.

Stochastic Processes in Physics and Chemistry

Abstract: Preface to the first edition. Preface to the second edition. Abbreviated references. I. Stochastic variables. II. Random events. III. Stochastic processes. IV. Markov processes. V. The master equation. VI. One-step processes. VII. Chemical reactions. VIII. The Fokker-Planck equation. IX. The Langevin approach. X. The expansion of the master equation. XI. The diffusion type. XII. First-passage problems. XIII. Unstable systems. XIV. Fluctuations in continuous systems. XV. The statistics of jump events. XVI. Stochastic differential equations. XVII. Stochastic behavior of quantum systems.
Journal ArticleDOI

Impulses and Physiological States in Theoretical Models of Nerve Membrane

TL;DR: Van der Pol's equation for a relaxation oscillator is generalized by the addition of terms to produce a pair of non-linear differential equations with either a stable singular point or a limit cycle, which qualitatively resembles Bonhoeffer's theoretical model for the iron wire model of nerve.
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