Mean-field dynamics of a random neural network with noise

TL;DR

This paper develops a stochastic mean-field model for large random neural networks with noise, analyzing stability, bifurcations, and fluctuations, and compares theoretical predictions with simulations.

Contribution

It introduces a second-order stochastic mean-field model that captures the effects of internal and external noise on network dynamics, including finite-size fluctuations.

Findings

External noise influences stationary activity levels in the thermodynamic limit.

Both noise types cause fluctuations in finite networks.

Theoretical predictions match simulations for large networks away from bifurcations.

Abstract

We consider a network of randomly coupled rate-based neurons influenced by external and internal noise. We derive a second-order stochastic mean-field model for the network dynamics and use it to analyze the stability and bifurcations in the thermodynamic limit, as well as to study the fluctuations due to the finite-size effect. It is demonstrated that the two types of noise have substantially different impact on the network dynamics. While both sources of noise give rise to stochastic fluctuations in the case of the finite-size network, only the external noise affects the stationary activity levels of the network in the thermodynamic limit.We compare the theoretical predictions with the direct simulation results and show that they agree for large enough network sizes and for parameter domains sufficiently away from bifurcations.