Statistical Field Theory and Networks of Spiking Neurons

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

1 IF - Institut Fourier
2 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.

Mots clés: field theory: statistical; network; tracks

Domaines: Physique [physics]; Physique Générale [physics.gen-ph]; Physique des Hautes Energies - Théorie [hep-th]; Physique mathématique [math-ph]

Pierre Gosselin, Aïleen Lotz, Marc Wambst. Statistical Field Theory and Networks of Spiking Neurons. 2020. ⟨hal-02973182⟩