Enhanced Resonance by Coupling and Summing in Sinusoidally Driven Chaotic Neural Networks

スポンサーリンク

概要

• 論文の詳細を見る

• 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.

• 社団法人電子情報通信学会の論文
• 1999-04-25

著者

• Sano Takuya

  Ntt Network Innovation Laboratories

• Sano T

  Ntt Network Innovation Laboratories

• Mizutani S

  Ntt Communication Science Laboratories

• Mizutani Shin

  Ntt Communication Science Laboratories

• SHIMOHARA Katsunori

  Network Informatics Labs., ATR International

• Shimohara Katsunori

  Network Informatics Labs. Atr International

• SHIMOHARA Katsunori

  NTT Communication Science Laboratories

関連論文

• A Multi-Agent Learning Mechanism Based On Collective Knowledge Exchange
• Comparing Learning Classifier System and Reinforcement Learning with Function Approximation
• Enhanced Resonance by Coupling and Summing in Sinusoidally Driven Chaotic Neural Networks
• Resonance in a Chaotic Neuron Model Driven by a Weak Sinusoid

スポンサーリンク