Propagation of chaos in mean field networks of FitzHugh-Nagumo neurons

TL;DR

This paper proves uniform in time propagation of chaos for a large network of FitzHugh-Nagumo neurons with non-linear dynamics, using reflection coupling to handle non-convex potentials and establish mean-field limits.

Contribution

It introduces a novel coupling method to demonstrate uniform propagation of chaos in FitzHugh-Nagumo neuron networks with non-linearities.

Findings

Established uniform in time propagation of chaos.

Provided explicit bounds for the convergence.

Handled non-convex potentials using reflection coupling.

Abstract

In this article, we are interested in the behavior of a fully connected network of $N$ neurons, where $N$ tends to infinity. We assume that the neurons follow the stochastic FitzHugh-Nagumo model, whose specificity is the non-linearity with a cubic term. We prove a result of uniform in time propagation of chaos of this model in a mean-field framework. We also exhibit explicit bounds. We use a coupling method initially suggested by A. Eberle (arXiv:1305.1233), and recently extended in (1805.11387), known as the reflection coupling. We simultaneously construct a solution of the $N$-particle system and $N$ independent copies of the non-linear McKean-Vlasov limit in such a way that, considering an appropriate semi-metric that takes into account the various possible behaviors of the processes, the two solutions tend to get closer together as $N$ increases, uniformly in time. The reflection coupling allows us to deal with the non-convexity of the underlying potential in the dynamics of the quantities defining our network, and show independence at the limit for the system in mean field interaction with sufficiently small Lipschitz continuous interactions.