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

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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.

Dates and versions

hal-02170948 , version 1 (02-07-2019)

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Benjamin Aymard, Fabien Campillo, Romain Veltz. Mean-field limit of interacting 2D nonlinear stochastic spiking neurons. 2019. ⟨hal-02170948⟩
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