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Dependence of inter-neuronal effective connectivity on synchrony dynamics in neuronal network motifs

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  • Deng, Bin
  • Deng, Yun
  • Yu, Haitao
  • Guo, Xinmeng
  • Wang, Jiang

Abstract

Effective connectivity, characterized as directional causal influences among neural units, is functionally significant to be reconstructed. Various dynamic regimes have been considered to underlie reshaping of the effective connections. In this work, the impact of zero-lag synchronization on the reconstruction of effective connectivity in neuronal network motifs is investigated. The synchronization analysis and effective connectivity estimation by using Granger causality (GC) method are performed. It is shown that the synchronization of the neurons at zero lag contributes to the reconstruction of reciprocal effective connections without synaptic connections. In addition, delay-induced zero-lag synchronous transition facilitates dynamic transformation of the causal interactions. With the increase of synaptic coupling strength, the causal interplay undergoes the transition to be statistically significant at a critical value. Furthermore, it can be found that multiple effective motifs are extracted from different synchronization states of the underlying structural motifs. GC measures of effective connectivity are proved to be reliable compared with the Information Flow for causal analysis. The obtained results may be helpful to future research about information processes.

Suggested Citation

  • Deng, Bin & Deng, Yun & Yu, Haitao & Guo, Xinmeng & Wang, Jiang, 2016. "Dependence of inter-neuronal effective connectivity on synchrony dynamics in neuronal network motifs," Chaos, Solitons & Fractals, Elsevier, vol. 82(C), pages 48-59.
    • Handle: RePEc:eee:chsofr:v:82:y:2016:i:c:p:48-59
    DOI: 10.1016/j.chaos.2015.10.034
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    Cited by:

    1. Shan, Bonan & Wang, Jiang & Deng, Bin & Zhang, Zhen & Wei, Xile, 2017. "Estimate the effective connectivity in multi-coupled neural mass model using particle swarm optimization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 89-101.
    2. Wang, Guowei & Wu, Yong & Xiao, Fangli & Ye, Zhiqiu & Jia, Ya, 2022. "Non-Gaussian noise and autapse-induced inverse stochastic resonance in bistable Izhikevich neural system under electromagnetic induction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 598(C).

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