Stochastic resonance in discrete excitable dynamics on graphs
Stochastic resonance in discrete excitable dynamics on graphs
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DOI：
10.1016/j.chaos.2011.12.011
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发表时间：
2012-05-01
影响因子：
7.8
通讯作者：
Lesne, Annick
中科院分区：
文献类型：
--
作者：
Huett, Marc-Thorsten;Jain, Mitul K.;Lesne, Annick
信号如何在网络结构的作用下以及在噪声的影响下在网络中传播是处理信号处理的广泛领域的一个基本问题-从神经科学到电气工程和通信技术。本文采用数值模拟和平均场方法来分析信号传播的最小动态模型。通过标记和跟踪随机网络中从单个输入节点传播到远程输出节点的激励，我们表明噪声（由自发节点激励提供）可以导致信号传播增强，在中等噪声强度下信噪比达到峰值。这种随机共振的网络模拟没有被平均场描述所捕获，平均场描述仅在平均度的水平上包含拓扑，这表明详细的网络拓扑在信号传播中起着重要作用。(C) 2011 Elsevier Ltd.版权所有。
How signals propagate through a network as a function of the network architecture and under the influence of noise is a fundamental question in a broad range of areas dealing with signal processing - from neuroscience to electrical engineering and communication technology. Here we use numerical simulations and a mean-field approach to analyze a minimal dynamic model for signal propagation. By labeling and tracking the excitations propagating from a single input node to remote output nodes in random networks, we show that noise (provided by spontaneous node excitations) can lead to an enhanced signal propagation, with a peak in the signal-to-noise ratio at intermediate noise intensities. This network analog of stochastic resonance is not captured by a mean-field description that incorporates topology only on the level of the average degree, indicating that the detailed network topology plays a significant role in signal propagation. (C) 2011 Elsevier Ltd. All rights reserved.