Statistical Field Theory and Networks of Spiking Neurons

TL;DR

This paper introduces a statistical field theory framework to model large networks of interacting spiking neurons, bridging individual neuron dynamics with macro-scale neural field models.

Contribution

It adapts statistical field theory methods to neural networks, integrating detailed neuron interactions with large-scale neural field modeling.

Findings

Successfully models neuron interactions and macro-scale neural activity

Bridges microscopic neuron dynamics with neural field theory

Provides a new theoretical framework for large neural networks

Abstract

This paper models the dynamics of a large set of interacting neurons within the framework of statistical field theory. We use a method initially developed in the context of statistical field theory [44] and later adapted to complex systems in interaction [45][46]. Our model keeps track of individual interacting neurons dynamics but also preserves some of the features and goals of neural field dynamics, such as indexing a large number of neurons by a space variable. Thus, this paper bridges the scale of individual interacting neurons and the macro-scale modelling of neural field theory.