Delay-induced synchronization transitions in small-world neuronal networks with hybrid electrical and chemical synapses

被引:22
|
作者
Yu, Haitao [1 ]
Wang, Jiang [1 ]
Liu, Chen [1 ]
Deng, Bin [1 ]
Wei, Xile [1 ]
机构
[1] Tianjin Univ, Sch Elect Engn & Automat, Tianjin 300072, Peoples R China
基金
中国国家自然科学基金;
关键词
Neuronal network; Synchronization; Time delay; Hybrid synapses; GRAPH-THEORETICAL ANALYSIS; COHERENCE RESONANCE; SPIRAL WAVES; CONNECTIVITY; ORGANIZATION; OSCILLATIONS;
D O I
10.1016/j.physa.2013.06.052
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
We study the dependence of synchronization transitions in small-world networks of bursting neurons with hybrid electrical chemical synapses on the information transmission delay, the probability of electrical synapses, and the rewiring probability. It is shown that, irrespective of the probability of electrical synapses, the information transmission delay can always induce synchronization transitions in small-world neuronal networks, i.e., regions of synchronization and nonsynchronization appear intermittently as the delay increases. In particular, all these transitions to burst synchronization occur approximately at integer multiples of the bursting period of individual neurons. In addition, for larger probability of electrical synapses, the intermittent synchronization transition is more profound, due to the stronger synchronization ability of electrical synapses compared with chemical ones. More importantly, chemical and electrical synapses can perform complementary roles in the synchronization of hybrid small-world neuronal networks: the larger the electrical synapse strength is, the smaller the chemical synapse strength needed to achieve burst synchronization. Furthermore, the small-world topology has a significant effect on the synchronization transition in hybrid neuronal networks. It is found that increasing the rewiring probability can always enhance the synchronization of neuronal activity. The results obtained are instructive for understanding the synchronous behavior of neural systems. (C) 2013 Elsevier B.V. All rights reserved.
引用
收藏
页码:5473 / 5480
页数:8
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