Robustness to Noise of Associative Memory Using Nonmonotonic Analogue Neurons

摘要

In this paper, dependence of storage capacity of an analogue associative memory model using nonmonotonic neu- rons on static synaptic noise and static threshold noise is shown. This dependence is analytically calculated by means of the self- consistent signal-to-noise analysis (SCSNA) proposed by Shiino and Fukai. It is known that the storage capacity of an asso- ciative memory model can be improved markedly by replacing the usual sigmoid neurons with nonmonotonic ones, and the Hopfield model has theoretically been shown to be fairly robust Against introducing the static synaptic noise.