Mean field system of a two-layers neural model in a diffusive regime

TL;DR

This paper models a two-layer neural system inspired by the visual cortex, proving the convergence of the stochastic processes representing neuron potentials as the number of neurons grows large, with explicit convergence speed.

Contribution

It establishes a mean field limit for a two-layer neural model in a diffusive regime, including convergence rates, which was not previously demonstrated.

Findings

Convergence of neuron potential processes as N approaches infinity

Explicit convergence speed derived using semigroup and generator analysis

Model applicable to visual cortex neural structures

Abstract

We study a model of interacting neurons. The structure of this neural system is composed of two layers of neurons such that the neurons of the first layer send their spikes to the neurons of the second one: if $N$ is the number of neurons of the first layer, at each spiking time of the first layer, every neuron of both layers receives an amount of potential of the form $U/\sqrt{N},$ where $U$ is a centered random variable. This kind of structure of neurons can model a part of the structure of the visual cortex: the first layer represents the primary visual cortex V1 and the second one the visual area V2. The model consists of two stochastic processes, one modelling the membrane potential of the neurons of the first layer, and the other the membrane potential of the neurons of the second one. We prove the convergence of these processes as the number of neurons~$N$ goes to infinity and obtain a convergence speed. The proofs rely on similar arguments as those used in [Erny, L\"ocherbach, Loukianova (2022)]: the convergence speed of the semigroups of the processes is obtained from the convergence speed of their infinitesimal generators using a Trotter-Kato formula, and from the regularity of the limit semigroup.