The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Nous avons trouvé une nouvelle classe de résonance stochastique (SR) dans un réseau neuronal simple composé i) de photorécepteurs générant des sorties non uniformes pour des entrées communes avec des décalages aléatoires, ii) d'un ensemble de neurones McCulloch-Pitts (MP) bruyants dont chacun a des caractéristiques aléatoires. valeurs de seuil dans le domaine temporel, iii) connexions de couplage locales entre les photorécepteurs et les neurones MP avec des champs récepteurs variables (RF), iv) cellules de sortie, et v) connexions de couplage locales entre les neurones MP et les cellules de sortie. Nous avons calculé les valeurs de corrélation entre les entrées et les sorties en fonction de la taille RF et des intensités des composants aléatoires dans les photorécepteurs et les neurones MP. Nous montrons l'existence d'"intensités de bruit optimales" des neurones MP sous les photorécepteurs non identiques et de "tailles RF optimales non nulles", ce qui indique que les valeurs de corrélation optimales de ce modèle SR ont été déterminées par deux paramètres critiques ; les intensités de bruit (bien connues) et les tailles RF comme nouveau paramètre.
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Copier
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
Copier
@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},}
Copier
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 -