Correlation structure of stochastic neural networks with generic connectivity matrices

Abstract

Using a perturbative expansion for weak synaptic weights and weak sources of randomness, we calculate the correlation structure of neural networks with generic connectivity matrices. In detail, the perturbative parameters are the mean and the standard deviation of the synaptic weights, together with the standard deviations of the background noise of the membrane potentials and of their initial conditions. We also show how to determine the correlation structure of the system when the synaptic connections have a random topology. This analysis is performed on rate neurons described by Wilson and Cowan equations, since this allows us to find analytic results. Moreover, the perturbative expansion can be developed at any order and for a generic connectivity matrix. We finally show an example of application of this technique for a particular case of biologically relevant topology of the synaptic connections.