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Phase dynamics in the biological neural networks

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  • Kim, Seunghwan
  • Lee, Sang Gui

Abstract

The simplified models of neural networks based on biophysical Hodgkin–Huxley neurons are studied with a focus on coherent-phase dynamics. In our approach, each neuron is considered as a nonlinear oscillator, and collective dynamics of a mesoscopic network of neural oscillators are studied using the methods of nonlinear dynamics. We explore the mechanisms for synchrony, clustering and their breakup in the synaptic parameter space and discuss implications to temporal aspects of neural-information processing.

Suggested Citation

  • Kim, Seunghwan & Lee, Sang Gui, 2000. "Phase dynamics in the biological neural networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 288(1), pages 380-396.
    • Handle: RePEc:eee:phsmap:v:288:y:2000:i:1:p:380-396
    DOI: 10.1016/S0378-4371(00)00435-0
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    Cited by:

    1. Lin, Qianjin & Wang, Jiang & Yang, Shuangming & Yi, Guosheng & Deng, Bin & Wei, Xile & Yu, Haitao, 2017. "The dynamical analysis of modified two-compartment neuron model and FPGA implementation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 484(C), pages 199-214.

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