  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.
