Mean Field Limits for Discrete Time Stochastic Neural Network Models
摘要
We apply our newly introduced formalism to explore an important question: how can we describe a large system of similarly behaving neurons by a simple dynamic? The notion of mean field limits is introduced and the convergence to it is rigorously proven. We then study the longtime behavior of the mean field limit in a particular framework and provide explicit formulae for the associated invariant measure.