同步(交流)
航程(航空)
复杂网络
理论(学习稳定性)
计算机科学
网络模型
拓扑(电路)
统计物理学
物理
数学
人工智能
工程类
组合数学
机器学习
万维网
航空航天工程
作者
Sarbendu Rakshit,Soumen Majhi,Jürgen Kurths,Dibakar Ghosh
出处
期刊:Chaos
[American Institute of Physics]
日期:2021-07-01
卷期号:31 (7)
被引量:26
摘要
We study synchronization in neuronal ensembles subject to long-range electrical gap junctions which are time-varying. As a representative example, we consider Hindmarsh–Rose neurons interacting based upon temporal long-range connections through electrical couplings. In particular, we adopt the connections associated with the direct 1-path network to form a small-world network and follow-up with the corresponding long-range network. Further, the underlying direct small-world network is allowed to temporally change; hence, all long-range connections are also temporal, which makes the model much more realistic from the neurological perspective. This time-varying long-range network is formed by rewiring each link of the underlying 1-path network stochastically with a characteristic rewiring probability pr, and accordingly all indirect k(>1)-path networks become temporal. The critical interaction strength to reach complete neuronal synchrony is much lower when we take up rapidly switching long-range interactions. We employ the master stability function formalism in order to characterize the local stability of the state of synchronization. The analytically derived stability condition for the complete synchrony state agrees well with the numerical results. Our work strengthens the understanding of time-varying long-range interactions in neuronal ensembles.
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