Mean field dynamics of a Wilson-Cowan neuronal network with nonlinear coupling term

James MacLaurin, Jamil Salhi, Salwa Toumi

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

In this paper we prove the propagation of chaos property for an ensemble of interacting neurons subject to independent Brownian noise. The propagation of chaos property means that in the large network size limit, the neurons behave as if they are probabilistically independent. The model for the internal dynamics of the neurons is taken to be that of Wilson and Cowan, and we consider there to be multiple different populations. The synaptic connections are modeled with a nonlinear "electrical" model. The nonlinearity of the synaptic connections means that our model lies outside the scope of classical propagation of chaos results. We obtain the propagation of chaos result by taking advantage of the fact that the mean-field equations are Gaussian, which allows us to use Borell's Inequality to prove that its tails decay exponentially.

Original languageEnglish (US)
Article number1850046
JournalStochastics and Dynamics
Volume18
Issue number6
DOIs
StatePublished - Dec 1 2018
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Modeling and Simulation

Keywords

  • Neural network
  • Wilson-Cowan model
  • electrical synapse
  • mean fields
  • nonlinear coupling term
  • propagation of chaos
  • stochastic differential equation

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