Can distributed delays perfectly stabilize dynamical networks?
Can distributed delays perfectly stabilize dynamical networks?
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DOI：
10.1103/physreve.77.046214
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发表时间：
2007-10
期刊：
影响因子：
--
通讯作者：
T. Omi;S. Shinomoto
中科院分区：
文献类型：
--
作者：
T. Omi;S. Shinomoto
信号传输延迟往往会使动态网络不稳定，导致振荡，但它们的分散性对稳定性的贡献相反。我们分析了一个积分微分方程，它描述了一个神经网络与分布式信号延迟的集体动力学。当Gamma分布时滞的离散度小于指数分布时滞时，系统表现出可重入现象，即系统的稳定性随着平均时滞的增加而一度丧失，但随后又恢复.由于时滞的离散度高于指数，系统永远不会不稳定。
Signal transmission delays tend to destabilize dynamical networks leading to oscillation, but their dispersion contributes oppositely toward stabilization. We analyze an integrodifferential equation that describes the collective dynamics of a neural network with distributed signal delays. With the Gamma distributed delays less dispersed than exponential distribution, the system exhibits reentrant phenomena, in which the stability is once lost but then recovered as the mean delay is increased. With delays dispersed more highly than exponential, the system never destabilizes.