A Neural Network with Low Symmetric Connectivity

Abstract

We present the complete, equilibrium solution of a neural-network model in which each neuron is connected to a small fraction of the others by symmetric, Hebb-rule synapses. At first replica symmetry is assumed, but the results are then corrected for full symmetry breaking, which leads to a substantial increase in storage capacity.