Statistical Field Theory and Networks of Spiking Neurons
Neurons and Cognition
2022-05-25 v2 Disordered Systems and Neural Networks
High Energy Physics - Theory
Mathematical Physics
math.MP
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.
Cite
@article{arxiv.2009.14744,
title = {Statistical Field Theory and Networks of Spiking Neurons},
author = {Pierre Gosselin and Aïleen Lotz and Marc Wambst},
journal= {arXiv preprint arXiv:2009.14744},
year = {2022}
}