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Stochastic Resonance in an Array of LocallyCoupled McCullochPitts Neurons with Population Heterogeneity
Akira UTAGAWA Tohru SAHASHI Tetsuya ASAI Yoshihito AMEMIYA
Publication
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences
Vol.E92A
No.10
pp.25082513 Publication Date: 2009/10/01
Online ISSN: 17451337
DOI: 10.1587/transfun.E92.A.2508
Print ISSN: 09168508 Type of Manuscript: Special Section PAPER (Special Section on Nonlinear Theory and its Applications) Category: Nonlinear Problems Keyword: stochastic resonance, image processing, neural networks,
Full Text: PDF(833.4KB)>>
Summary:
We found a new class of stochastic resonance (SR) in a simple neural network that consists of i) photoreceptors generating nonuniform outputs for common inputs with random offsets, ii) an ensemble of noisy McCullochPitts (MP) neurons each of which has random threshold values in the temporal domain, iii) local coupling connections between the photoreceptors and the MP neurons with variable receptive fields (RFs), iv) output cells, and v) local coupling connections between the MP neurons and the output cells. We calculated correlation values between the inputs and the outputs as a function of the RF size and intensities of the random components in photoreceptors and the MP neurons. We show the existence of "optimal noise intensities" of the MP neurons under the nonidentical photoreceptors and "nonzero optimal RF sizes," which indicated that optimal correlation values of this SR model were determined by two critical parameters; noise intensities (wellknown) and RF sizes as a new parameter.

