Analysis of ising spin neural network with time -dependent mexican-hat-type interaction

Abstract

We analyzed the equilibrium states of an Ising spin neural network model in which both spins and interactions evolve simultaneously over time. The interactions are Mexican-hat-type, which are used for lateral inhibition models. The model shows a bump activity, which is the locally activated network state. The time-dependent interactions are driven by Langevin noise and Hebbian learning. The analysis results reveal that Hebbian learning expands the bistable regions of the ferromagnetic and local excitation phases. © 2009 Springer Berlin Heidelberg.