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Spiking regularity in a noisy small-world neuronal network

机译:嘈杂的小世界神经元网络中的尖峰规律

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摘要

The regularity of spiking oscillations is studied in the networks with different topological structures. The network is composed of coupled Fitz-Hugh-Nagumo neurons driven by colored noise. The investigation illustrates that the spike train in both the regular and the Watts-Strogatz small-world neuronal networks can show the best regularity at a moderate noise intensity, indicating the existence of coherence resonance. Moreover, the temporal coherence of the spike train in the small-world network is superior to that in a regular network due to the increase of the randomness of the network topology. Besides the noise intensity, the spiking regularity can be optimized by tuning the randomness of the network topological structure or by tuning the correlation time of the colored noise. In particular, under the cooperation of the small-world topology and the correlation time, the spike train with good regularity could sustain a large magnitude of the local noise. (c) 2007 Elsevier B.V. All rights reserved.
机译:在具有不同拓扑结构的网络中研究了尖峰振荡的规律性。该网络由有色噪声驱动的Fitz-Hugh-Nagumo耦合神经元组成。研究表明,常规和Watts-Strogatz小世界神经元网络中的尖峰序列在适度的噪声强度下可以显示最佳规律性,表明存在相干共振。而且,由于网络拓扑的随机性增加,小世界网络中的峰值序列的时间相干性优于常规网络中的。除了噪声强度,可以通过调整网络拓扑结构的随机性或通过调整有色噪声的相关时间来优化尖峰规律。特别地,在小世界拓扑和相关时间的配合下,具有良好规律性的尖峰序列可以承受较大的局部噪声。 (c)2007 Elsevier B.V.保留所有权利。

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