IEICE TRANSACTIONS on Fundamentals
Stochastic Resonance in an Array of Locally-Coupled McCulloch-Pitts Neurons with Population Heterogeneity
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 McCulloch-Pitts (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 (well-known) and RF sizes as a new parameter.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E92-A No.10 pp.2508-2513
- Publication Date
- 2009/10/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.E92.A.2508
- Type of Manuscript
- Special Section PAPER (Special Section on Nonlinear Theory and its Applications)
- Category
- Nonlinear Problems
Authors
Keyword
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Akira UTAGAWA, Tohru SAHASHI, Tetsuya ASAI, Yoshihito AMEMIYA, "Stochastic Resonance in an Array of Locally-Coupled McCulloch-Pitts Neurons with Population Heterogeneity" in IEICE TRANSACTIONS on Fundamentals,
vol. E92-A, no. 10, pp. 2508-2513, October 2009, doi: 10.1587/transfun.E92.A.2508.
Abstract: 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 McCulloch-Pitts (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 (well-known) and RF sizes as a new parameter.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E92.A.2508/_p
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@ARTICLE{e92-a_10_2508,
author={Akira UTAGAWA, Tohru SAHASHI, Tetsuya ASAI, Yoshihito AMEMIYA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Stochastic Resonance in an Array of Locally-Coupled McCulloch-Pitts Neurons with Population Heterogeneity},
year={2009},
volume={E92-A},
number={10},
pages={2508-2513},
abstract={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 McCulloch-Pitts (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 (well-known) and RF sizes as a new parameter.},
keywords={},
doi={10.1587/transfun.E92.A.2508},
ISSN={1745-1337},
month={October},}
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TY - JOUR
TI - Stochastic Resonance in an Array of Locally-Coupled McCulloch-Pitts Neurons with Population Heterogeneity
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 2508
EP - 2513
AU - Akira UTAGAWA
AU - Tohru SAHASHI
AU - Tetsuya ASAI
AU - Yoshihito AMEMIYA
PY - 2009
DO - 10.1587/transfun.E92.A.2508
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E92-A
IS - 10
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - October 2009
AB - 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 McCulloch-Pitts (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 (well-known) and RF sizes as a new parameter.
ER -
English
