Robustness to Noise of Associative Memory Using Nonmonotonic Analogue Neurons

Kazushi MIMURA  Masato OKADA  Koji KURATA  

IEICE TRANSACTIONS on Information and Systems   Vol.E81-D   No.8   pp.928-932
Publication Date: 1998/08/25
Online ISSN: 
Print ISSN: 0916-8532
Type of Manuscript: PAPER
Category: Bio-Cybernetics and Neurocomputing
nonmonotonic neuron,  static synaptic noise,  static noise in the threshold,  storage capacity,  SCSNA,  

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In this paper, dependence of storage capacity of an analogue associative memory model using nonmonotonic neurons 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 associative 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. In this paper, it is shown that when the monotonicity of neuron is high, the storage capacity decreases rapidly according to an increase of the static synaptic noise. It is also shown that the reduction of the storage capacity is more sensitive to an increase in the static threshold noise than to the increase in the static synaptic noise.