Load capacity of a neural network model with spatially and temporally structured connectivity

Abstract

In this work we consider a neural network model with spatially and temporally structured synapses whose dynamics may depend on more than one time step. This model is capable of storing and recovering temporal sequences or cycles. Hebb-like learning rules are used to store the temporal sequences of patterns and Hamming-like distance for cycles is defined to measure the distance between two different cycles. We perform a signal-to-noise analysis of the system and numerically determine the critical capacity of the network, basins of attractions size, stability of recovery states and investigate the effects of spurious states in the performance of the net. We show that the performance of the net is enhanced when information is stored in temporally longer sequences.