期刊
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
卷 387, 期 8-9, 页码 2155-2160出版社
ELSEVIER
DOI: 10.1016/j.physa.2007.11.042
关键词
complex networks; Hindmarsh-Rose neural networks; activity
In this paper, we investigate how activity of complex neural networks depends on random long-range connections. Network elements are described by Hindmarsh-Rose (HR) neurons assumed to be inactive. It is found that for a given coupling strength, when the number of random connections (or randomness) is greater than a threshold, the spiking neurons, which are absent in the nearest-neighbor neural network, occur. The spiking activity becomes stronger in intensity and higher in frequency as the randomness is further increased. These phenomena imply that random long-range connections can induce and enhance the activity of neural networks. Furthermore, the possible mechanism behind the action of random long-range connections is also addressed. Our results may provide a useful hint for understanding the properties of collective dynamics in coupled real neurons. (c) 2007 Elsevier B.V. All rights reserved.
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