Graph Rules for Recurrent Neural Network Dynamics

Abstract

We describe a class of inhibition-dominated neural networks corresponding to directed graphs, and introduce some of the theory that has been developed to study them. The heart of the theory is a set of parameter-independent graph rules that enables us to directly predict features of the dynamics from combinatorial properties of the graph.