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arXiv nlin/0003003nlin.CDnlin.AOq-bio
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混沌神经网络的相干响应

Coherent Response in a Chaotic Neural Network

Haruhiko Nishimura, Naofumi Katada, Kazuyuki Aihara

摘要:

建立了一种信号驱动的混沌神经网络方案,其耦合常数对应于一定的信息,并研究了其确定性动力学下的类随机共振效应,与传统的具有随机噪声的Hopfield网络进行了比较.结果表明,混沌神经网络可以增强弱阈下信号,并具有更高的一致性之间的刺激和响应的能力比传统的随机模型所获得的。

英文摘要:

We set up a signal-driven scheme of the chaotic neural network with the coupling constants corresponding to certain information, and investigate the stochastic resonance-like effects under its deterministic dynamics, comparing with the conventional case of Hopfield network with stochastic noise. It is shown that the chaotic neural network can enhance weak subthreshold signals and have higher coherence abilities between stimulus and response than those attained by the conventional stochastic model.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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