Statistical Field Theory and Networks of Spiking Neurons

Pierre Gosselin (1) , Aïleen Lotz (2) , Marc Wambst (3)

1 IF - Institut Fourier
2 Cerca Trova
3 IRMA - Institut de Recherche Mathématique Avancée

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.

Domaines: Neurosciences; Matière Condensée [cond-mat]

Pierre Gosselin, Aïleen Lotz, Marc Wambst. Statistical Field Theory and Networks of Spiking Neurons. 2020. ⟨hal-02953819v1⟩