A mean-field model of Integrate-and-Fire neurons: non-linear stability of the stationary solutions

We investigate a stochastic network composed of Integrate-and-Fire spiking neurons, focusing on its mean-field asymptotics. We consider an invariant probability measure of the McKean-Vlasov equation and establish an explicit sufficient condition to ensure the local stability of this invariant distribution. Furthermore, we prove a conjecture proposed initially by J. Touboul and P. Robert regarding the bistable nature of a specific instance of this neuronal model.

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Publication Details

Journal
Mathematical Neuroscience and Applications
Published
2024-09-27
DOI
https://doi.org/10.46298/mna.12583
Citations
3
Primary Topic
Neural dynamics and brain function
Type
article
Field-Weighted Citation Impact
0.34
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article

A mean-field model of Integrate-and-Fire neurons: non-linear stability of the stationary solutions

Quentin Cormier
3 citations
Mathematical Neuroscience and Applications
Neural dynamics and brain function
0.34
article

A mean-field model of Integrate-and-Fire neurons: non-linear stability of the stationary solutions

Quentin Cormier
article en
3 citations

Abstract

We investigate a stochastic network composed of Integrate-and-Fire spiking neurons, focusing on its mean-field asymptotics. We consider an invariant probability measure of the McKean-Vlasov equation and establish an explicit sufficient condition to ensure the local stability of this invariant distribution. Furthermore, we prove a conjecture proposed initially by J. Touboul and P. Robert regarding the bistable nature of a specific instance of this neuronal model.

Mathematical Neuroscience and ApplicationsVol. Volume 4
Centre National de la Recherche Scientifique (FR), École Polytechnique (FR)
Openalex Percentile: Top 48%
Neural dynamics and brain function
0.34
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