Hopf bifurcation in a Mean-Field model of spiking neurons

Abstract

We study a family of non-linear McKean-Vlasov SDEs driven by a Poisson measure, modelling the mean-field asymptotic of a network of generalized Integrate-and-Fire neurons. We give sufficient conditions to have periodic solutions through a Hopf bifurcation. Our spectral conditions involve the location of the roots of an explicit holomorphic function. The proof relies on two main ingredients. First, we introduce a discrete time Markov Chain modeling the phases of the successive spikes of a neuron. The invariant measure of this Markov Chain is related to the shape of the periodic solutions. Secondly, we use the Lyapunov-Schmidt method to obtain self-consistent oscillations. We illustrate the result with a toy model for which all the spectral conditions can be analytically checked.

References in corpus (2)

• A discrete time neural network model with spiking neurons II. Dynamics with noise
• A mean-field model of Integrate-and-Fire neurons: non-linear stability of the stationary solutions

Cited by in corpus (3)

• Investigating the integrate and fire model as the limit of a random discharge model: a stochastic analysis perspective
• Investigating the integrate and fire model as the limit of a random discharge model: a stochastic analysis perspective
• Thermodynamic Formalism in Neuronal Dynamics and Spike Train Statistics