Power law decay of stored pattern stability in sparse Hopfield neural networks

Abstract

Hopfield neural networks on scale-free networks display the power law relation between the stability of patterns and the number of patterns. The stability is measured by the overlap between the output state and the stored pattern which is presented to a neural network. In simulations the overlap declines to a constant by a power law decay. Here we provide the explanation for the power law behavior through the signal-to-noise ratio analysis. We show that on sparse networks storing a plenty of patterns the stability of stored patterns can be approached by a power law function with the exponent −0.5. There is a difference between analytic and simulation results that the analytic results of overlap decay to 0. The difference exists because the signal and noise term of nodes diverge from the mean-field approach in the sparse finite size networks.