Some recent results on a mean field of a network of 2d spiking neurons
De Romain Veltz
In this talk, I will present some results regarding the dynamics of a network of stochastic spiking neurons akin to the "generalized linear model" connected with a mean-field coupling. This network is an elaboration of the one introduced in [De Masi et al. 2014] by generalizing the dynamics of the individual neurons. This allows to capture most of the known intrinsic neuronal spiking, like bursting for example, and thus to study the effect of the intrinsic neuron dynamics on the macroscopic one.
I will study the property of the mean-field limit. In effect, it is a nonlinear Piecewise Deterministic Markov process with explosive flow and unbounded total rate function. I will first present some theoretical results regarding the solution of the linearized SDE which is shown to be regular and has mean firing rate characterized by an integral equation. I will then show that the solution is ergodic and give many quantitative properties of its invariant distribution. I will then extend some of these results (well posedness, stationary distributions) to the nonlinear case.