Mean field analysis of large-scale interacting populations of stochastic conductance-based spiking neurons using the Klimontovich method

TL;DR

This paper develops a mean field framework using the Klimontovich method to analyze large-scale networks of conductance-based spiking neurons, deriving equations that match rigorous probability theory results and validating them with numerical simulations.

Contribution

It introduces a Klimontovich-based mean field approach for neural populations, deriving exact nonlinear integro-partial differential equations and demonstrating their accuracy through numerical solutions.

Findings

Derived a nonlinear system of equations for neural population probability distributions.

Numerical solutions agree with direct simulations for large networks.

Applicable to various neuron models, including Hodgkin-Huxley type neurons.

Abstract

We investigate the dynamics of large-scale interacting neural populations, composed of conductance based, spiking model neurons with modifiable synaptic connection strengths, which are possibly also subjected to external noisy currents. The network dynamics is controlled by a set of neural population probability distributions ($\mathrm{PPD}$)which are constructed along the same lines as in the Klimontovich approach to the kinetic theory of plasmas. An exact non-closed, nonlinear, system of integro-partial differential equations is derived for the $\mathrm{PPD}$s. As is customary, a closing procedure leads to a mean field limit. The equations we have obtained are of the same type as those which have been recently derived using rigorous techniques of probability theory. The numerical solutions of these so called McKean-Vlasov-Fokker-Planck equations, which are only valid in the limit of infinite size networks, actually shows that the statistical measures as obtained from $\mathrm{PPD}$s are in good agreement with those obtained through direct integration of the stochastic dynamical system for large but finite size networks. Although numerical solutions have been obtained in the case of Fitzhugh-Nagumo model neurons, the theory can be readily applied to systems of Hodgkin-Huxley type model neurons of arbitrary dimension.