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
O aprimoramento da ressonância é mostrado pelo acoplamento e soma em redes neurais caóticas acionadas senoidalmente. Este fenômeno de ressonância tem um pico em uma frequência de acionamento semelhante à ressonância estocástica induzida por ruído (SR), no entanto, o mecanismo é diferente da SR induzida por ruído. Estudamos numericamente as propriedades de ressonância em redes neurais caóticas na fase turbulenta com soma e acoplamento homogêneo, com particular consideração do aumento da relação sinal-ruído (SNR) por acoplamento e soma. As redes somadoras podem melhorar o SNR de um campo médio com base na lei dos grandes números. O acoplamento global pode melhorar o SNR de um campo médio e de um neurônio na rede. No entanto, o aprimoramento não é garantido e depende dos parâmetros. Uma combinação de acoplamento e soma aumenta a SNR, mas a soma para fornecer um campo médio é mais eficaz do que o acoplamento no nível do neurônio para promover a SNR. A rede de acoplamento global tem uma correlação negativa entre o SNR do campo médio e a entropia Kolmogorov-Sinai (KS), e entre o SNR de um neurônio na rede e a entropia KS. Esta correlação negativa é semelhante aos resultados do modelo de neurônio único acionado. O SNR é saturado à medida que um aumento na amplitude do drive, e aumentos adicionais mudam o estado para um estado não caótico. O SNR é aprimorado em torno de algumas frequências e a dependência da frequência é mais clara e suave do que os resultados do modelo de neurônio único acionado. Tal dependência da amplitude e frequência do acionamento apresenta semelhanças com os resultados do modelo de neurônio único acionado. A rede de acoplamento do vizinho mais próximo com um limite periódico ou livre também pode melhorar o SNR de um neurônio dependendo dos parâmetros. A rede também possui uma correlação negativa entre o SNR de um neurônio e a entropia KS sempre que a fronteira é periódica ou livre. A rede com fronteira livre não tem efeito significativo no SNR de ambas as bordas das fronteiras livres.
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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 -