  Several differential equation models have been proposed to explain the
formation of patterns characteristic of the grid cell network. Understanding
the robustness of these patterns with respect to noise is one of the key open
questions in computational neuroscience. In the present work, we analyze a
family of stochastic differential systems modelling grid cell networks.
Furthermore, the well-posedness of the associated McKean--Vlasov and
Fokker--Planck equations, describing the average behavior of the networks, is
established. Finally, we rigorously prove the mean field limit of these systems
and provide a sharp rate of convergence for their empirical measures.
