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弱复用诱导相干共振。

Weak multiplexing induces coherence resonance.

作者信息

Semenova Nadezhda, Zakharova Anna

机构信息

Department of Physics, Saratov State University, Astrakhanskaya Str. 83, 410012 Saratov, Russia.

Institut für Theoretische Physik, Technische Universität Berlin, Hardenbergstr. 36, 10623 Berlin, Germany.

出版信息

Chaos. 2018 May;28(5):051104. doi: 10.1063/1.5037584.

DOI:10.1063/1.5037584
PMID:29857656
Abstract

Using the model of a FitzHugh-Nagumo system in the excitable regime, we study the impact of multiplexing on coherence resonance in a two-layer network. We show that multiplexing allows for the control of the noise-induced dynamics. In particular, we find that multiplexing induces coherence resonance in networks that do not demonstrate this phenomenon in isolation. Examples are provided by deterministic networks and networks where the strength of interaction between the elements is not optimal for coherence resonance. In both cases, we show that the control strategy based on multiplexing can be successfully applied even for weak coupling between the layers. Moreover, for the case of deterministic networks, we obtain a counter-intuitive result: the multiplex-induced coherence resonance in the layer which is deterministic in isolation manifests itself even more strongly than that in the noisy layer.

摘要

利用处于可激发状态的FitzHugh-Nagumo系统模型,我们研究了复用对双层网络中相干共振的影响。我们表明,复用能够控制噪声诱导的动力学。特别地,我们发现复用会在孤立状态下不表现出这种现象的网络中引发相干共振。确定性网络以及元件间相互作用强度对于相干共振并非最优的网络就是例证。在这两种情况下,我们都表明,即使层间耦合较弱,基于复用的控制策略也能成功应用。此外,对于确定性网络的情况,我们得到了一个反直觉的结果:在孤立状态下为确定性的层中,复用诱导的相干共振比在有噪声的层中表现得更为强烈。

相似文献

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Optimal Self-Induced Stochastic Resonance in Multiplex Neural Networks: Electrical vs. Chemical Synapses.多重神经网络中的最优自激随机共振:电突触与化学突触
Front Comput Neurosci. 2020 Aug 7;14:62. doi: 10.3389/fncom.2020.00062. eCollection 2020.