Investigating the integrate and fire model as the limit of a random discharge model: a stochastic analysis perspective

TL;DR

This paper rigorously analyzes the convergence of a regularized stochastic integrate-and-fire neuron model to its original form, establishing polynomial-order convergence and validating findings through numerical experiments.

Contribution

It provides a mathematical proof of convergence between regularized and original models, including rate estimates and numerical validation.

Findings

Established convergence between regularized and original models.

Quantified the convergence rate as polynomial order.

Validated theoretical results with numerical experiments.

Abstract

In the mean field integrate-and-fire model, the dynamics of a typical neuron within a large network is modeled as a diffusion-jump stochastic process whose jump takes place once the voltage reaches a threshold. In this work, the main goal is to establish the convergence relationship between the regularized process and the original one where in the regularized process, the jump mechanism is replaced by a Poisson dynamic, and jump intensity within the classically forbidden domain goes to infinity as the regularization parameter vanishes. On the macroscopic level, the Fokker-Planck equation for the process with random discharges (i.e. Poisson jumps) are defined on the whole space, while the equation for the limit process is on the half space. However, with the iteration scheme, the difficulty due to the domain differences has been greatly mitigated and the convergence for the stochastic process and the firing rates can be established. Moreover, we find a polynomial-order convergence for the distribution by a re-normalization argument in probability theory. Finally, by numerical experiments, we quantitatively explore the rate and the asymptotic behavior of the convergence for both linear and nonlinear models.