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
L'amélioration de la résonance est démontrée par le couplage et la sommation de réseaux neuronaux chaotiques à commande sinusoïdale. Ce phénomène de résonance a un pic à une fréquence d'attaque similaire à la résonance stochastique (SR) induite par le bruit. Cependant, le mécanisme est différent de la SR induite par le bruit. Nous étudions numériquement les propriétés de résonance dans les réseaux neuronaux chaotiques en phase turbulente avec sommation et couplage homogène, en tenant particulièrement compte de l'amélioration du rapport signal sur bruit (SNR) par couplage et sommation. Les réseaux de sommation peuvent améliorer le SNR d'un champ moyen sur la base de la loi des grands nombres. Le couplage global peut améliorer le SNR d'un champ moyen et d'un neurone du réseau. Cependant, l'amélioration n'est pas garantie et dépend des paramètres. Une combinaison de couplage et de sommation améliore le SNR, mais la sommation pour fournir un champ moyen est plus efficace que le couplage au niveau neuronal pour promouvoir le SNR. Le réseau de couplage global présente une corrélation négative entre le SNR du champ moyen et l'entropie de Kolmogorov-Sinaï (KS), et entre le SNR d'un neurone du réseau et l'entropie KS. Cette corrélation négative est similaire aux résultats du modèle à neurone unique piloté. Le SNR est saturé à mesure qu'une augmentation de l'amplitude de commande augmente, et des augmentations supplémentaires changent l'état en un état non chaotique. Le SNR est amélioré autour de quelques fréquences et la dépendance à la fréquence est plus claire et plus douce que les résultats du modèle de neurone unique piloté. Une telle dépendance à l’amplitude et à la fréquence de commande présente des similitudes avec les résultats du modèle de neurone unique piloté. Le réseau de couplage voisin le plus proche avec une frontière périodique ou libre peut également améliorer le SNR d'un neurone en fonction des paramètres. Le réseau présente également une corrélation négative entre le SNR d'un neurone et l'entropie KS chaque fois que la frontière est périodique ou libre. Le réseau avec une frontière libre n’a pas d’effet significatif sur le SNR des deux bords des frontières libres.
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Shin MIZUTANI, Takuya SANO, Katsunori SHIMOHARA, "Enhanced Resonance by Coupling and Summing in Sinusoidally Driven Chaotic Neural Networks" in IEICE TRANSACTIONS on Fundamentals,
vol. E82-A, no. 4, pp. 648-657, April 1999, doi: .
Abstract: Enhancement of resonance is shown by coupling and summing in sinusoidally driven chaotic neural networks. This resonance phenomenon has a peak at a drive frequency similar to noise-induced stochastic resonance (SR), however, the mechanism is different from noise-induced SR. We numerically study the properties of resonance in chaotic neural networks in the turbulent phase with summing and homogeneous coupling, with particular consideration of enhancement of the signal-to-noise ratio (SNR) by coupling and summing. Summing networks can enhance the SNR of a mean field based on the law of large numbers. Global coupling can enhance the SNR of a mean field and a neuron in the network. However, enhancement is not guaranteed and depends on the parameters. A combination of coupling and summing enhances the SNR, but summing to provide a mean field is more effective than coupling on a neuron level to promote the SNR. The global coupling network has a negative correlation between the SNR of the mean field and the Kolmogorov-Sinai (KS) entropy, and between the SNR of a neuron in the network and the KS entropy. This negative correlation is similar to the results of the driven single neuron model. The SNR is saturated as an increase in the drive amplitude, and further increases change the state into a nonchaotic one. The SNR is enhanced around a few frequencies and the dependence on frequency is clearer and smoother than the results of the driven single neuron model. Such dependence on the drive amplitude and frequency exhibits similarities to the results of the driven single neuron model. The nearest neighbor coupling network with a periodic or free boundary can also enhance the SNR of a neuron depending on the parameters. The network also has a negative correlation between the SNR of a neuron and the KS entropy whenever the boundary is periodic or free. The network with a free boundary does not have a significant effect on the SNR from both edges of the free boundaries.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e82-a_4_648/_p
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@ARTICLE{e82-a_4_648,
author={Shin MIZUTANI, Takuya SANO, Katsunori SHIMOHARA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Enhanced Resonance by Coupling and Summing in Sinusoidally Driven Chaotic Neural Networks},
year={1999},
volume={E82-A},
number={4},
pages={648-657},
abstract={Enhancement of resonance is shown by coupling and summing in sinusoidally driven chaotic neural networks. This resonance phenomenon has a peak at a drive frequency similar to noise-induced stochastic resonance (SR), however, the mechanism is different from noise-induced SR. We numerically study the properties of resonance in chaotic neural networks in the turbulent phase with summing and homogeneous coupling, with particular consideration of enhancement of the signal-to-noise ratio (SNR) by coupling and summing. Summing networks can enhance the SNR of a mean field based on the law of large numbers. Global coupling can enhance the SNR of a mean field and a neuron in the network. However, enhancement is not guaranteed and depends on the parameters. A combination of coupling and summing enhances the SNR, but summing to provide a mean field is more effective than coupling on a neuron level to promote the SNR. The global coupling network has a negative correlation between the SNR of the mean field and the Kolmogorov-Sinai (KS) entropy, and between the SNR of a neuron in the network and the KS entropy. This negative correlation is similar to the results of the driven single neuron model. The SNR is saturated as an increase in the drive amplitude, and further increases change the state into a nonchaotic one. The SNR is enhanced around a few frequencies and the dependence on frequency is clearer and smoother than the results of the driven single neuron model. Such dependence on the drive amplitude and frequency exhibits similarities to the results of the driven single neuron model. The nearest neighbor coupling network with a periodic or free boundary can also enhance the SNR of a neuron depending on the parameters. The network also has a negative correlation between the SNR of a neuron and the KS entropy whenever the boundary is periodic or free. The network with a free boundary does not have a significant effect on the SNR from both edges of the free boundaries.},
keywords={},
doi={},
ISSN={},
month={April},}
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TY - JOUR
TI - Enhanced Resonance by Coupling and Summing in Sinusoidally Driven Chaotic Neural Networks
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 648
EP - 657
AU - Shin MIZUTANI
AU - Takuya SANO
AU - Katsunori SHIMOHARA
PY - 1999
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E82-A
IS - 4
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - April 1999
AB - Enhancement of resonance is shown by coupling and summing in sinusoidally driven chaotic neural networks. This resonance phenomenon has a peak at a drive frequency similar to noise-induced stochastic resonance (SR), however, the mechanism is different from noise-induced SR. We numerically study the properties of resonance in chaotic neural networks in the turbulent phase with summing and homogeneous coupling, with particular consideration of enhancement of the signal-to-noise ratio (SNR) by coupling and summing. Summing networks can enhance the SNR of a mean field based on the law of large numbers. Global coupling can enhance the SNR of a mean field and a neuron in the network. However, enhancement is not guaranteed and depends on the parameters. A combination of coupling and summing enhances the SNR, but summing to provide a mean field is more effective than coupling on a neuron level to promote the SNR. The global coupling network has a negative correlation between the SNR of the mean field and the Kolmogorov-Sinai (KS) entropy, and between the SNR of a neuron in the network and the KS entropy. This negative correlation is similar to the results of the driven single neuron model. The SNR is saturated as an increase in the drive amplitude, and further increases change the state into a nonchaotic one. The SNR is enhanced around a few frequencies and the dependence on frequency is clearer and smoother than the results of the driven single neuron model. Such dependence on the drive amplitude and frequency exhibits similarities to the results of the driven single neuron model. The nearest neighbor coupling network with a periodic or free boundary can also enhance the SNR of a neuron depending on the parameters. The network also has a negative correlation between the SNR of a neuron and the KS entropy whenever the boundary is periodic or free. The network with a free boundary does not have a significant effect on the SNR from both edges of the free boundaries.
ER -