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• W3178106768 abstract "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(&gt;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." @default.
• W3178106768 created "2021-07-19" @default.
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• W3178106768 date "2021-07-01" @default.
• W3178106768 modified "2023-10-16" @default.
• W3178106768 title "Neuronal synchronization in long-range time-varying networks" @default.
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• W3178106768 doi "https://doi.org/10.1063/5.0057276" @default.
• W3178106768 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/34340354" @default.
• W3178106768 hasPublicationYear "2021" @default.
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