# Mean-field limit of interacting 2D nonlinear stochastic spiking neurons

## TL;DR

This paper develops a mean-field model for a network of stochastic spiking neurons, derives its limit, and validates it through numerical simulations showing propagation of chaos and bifurcation phenomena.

## Contribution

It introduces a new nonlinear stochastic model of neuron networks, derives its mean-field limit, and provides numerical methods to analyze its properties.

## Key findings

- Propagation of chaos observed numerically
- Convergence from microscopic to mean-field model
- Evidence of Hopf bifurcation at high connectivity

## Abstract

In this work, we propose a nonlinear stochastic model of a network of stochastic spiking neurons. We heuristically derive the mean-field limit of this system. We then design a Monte Carlo method for the simulation of the microscopic system, and a finite volume method (based on an upwind implicit scheme) for the mean-field model. The finite volume method respects numerical versions of the two main properties of the mean-field model, conservation and positivity, leading to existence and uniqueness of a numerical solution. As the size of the network tends to infinity, we numerically observe propagation of chaos and convergence from an individual description to a mean-field description. Numerical evidences for the existence of a Hopf bifurcation (synonym of synchronised activity) for a sufficiently high value of connectivity, are provided.