McKean-Vlasov Equations for Large Networks of Neurons with Adaptive Asymmetric Edges

Abstract

Classical Mckean-Vlasov theory concerns high-dimensional particle systems for which the effect of one particle on another is instantaneous. However in many applications, particularly neuroscience, the effect of one particle on another is not instantaneous but delayed. For biophysically-accurate neuroscientific models, for each j to k one can introduce an additional stochastic process that describes the propagation of the signal from j to k. In this paper we determine a self-consistent autonomous SDE that describe the high-dimensional limit of such neural networks. We apply this system to a model of the visual cortex. Performing a bifurcation analysis, we determine conditions under which hopf bifurcations occur in the network.