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 Kl and later adapted to complex systems in interaction GL1GL2. 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. This paper thus bridges the scale of individual interacting neurons and the macro-scale modelling of neural field theory.