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The power law statistics of the spiking timing in a neuronal network

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  • Yao, Chenggui
  • Sun, JianQiang
  • Jin, Jun
  • Shuai, Jianwei
  • Li, Xiang
  • Yao, Yuangen
  • Xu, Xufan

Abstract

Information encoding and decoding by neurons is a fundamental process in neuroscience. Herein, we present a statistical investigation of the first spike timing arising from the neuronal population in a small-world network with the Hodgkin–Huxley model after a neuron has received current stimulation, including a transient or a continuous stimulus. Regardless of how the interaction between neurons in the network was implemented, via electrical coupling or chemical synapses, we found the same power-law statistics for the first spike timing, independent of the topological structure of the neuronal network. We further suggest that such power-law statistics can be a generalized feature for the first spike timing in the small-world and scale-free neuronal networks. Our findings provide new insight into the coding mechanism for the first spike timing and improve the understanding of the power-law behavior in nature.

Suggested Citation

  • Yao, Chenggui & Sun, JianQiang & Jin, Jun & Shuai, Jianwei & Li, Xiang & Yao, Yuangen & Xu, Xufan, 2023. "The power law statistics of the spiking timing in a neuronal network," Chaos, Solitons & Fractals, Elsevier, vol. 172(C).
    • Handle: RePEc:eee:chsofr:v:172:y:2023:i:c:s096007792300499x
    DOI: 10.1016/j.chaos.2023.113598
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    References listed on IDEAS

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