Random long-range connections induce activity of complex Hindmarsh–Rose neural networks

Du Qu Wei, Xiao Shu Luo and Ying Hua Qin

Physica A: Statistical Mechanics and its Applications, 2008, vol. 387, issue 8, 2155-2160

Abstract: 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.

Keywords: Complex networks; Hindmarsh–Rose neural networks; Activity (search for similar items in EconPapers)
Date: 2008
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:387:y:2008:i:8:p:2155-2160

DOI: 10.1016/j.physa.2007.11.042

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