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Firing activity of complex space-clamped FitzHugh-Nagumo neural networks

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  • D. Q. Wei
  • X. S. Luo
  • Y. L. Zou

Abstract

We investigate how firing activity of complex neural networks depends on the random long-range connections and coupling strength. Network elements are described by excitable space-clamped FitzHugh-Nagumo (SCFHN) neurons with the values of parameters at which no firing activity occurs. It is found that for a given appropriate coupling strength C, there exists a critical fraction of random connections (or randomness) p * , such that if p > p * the firing neurons, which are absent in the nearest-neighbor network, occur. The firing activity becomes more frequent as randomness p is further increased. On the other hand, when the p is smaller, there are no active neurons in network, no matter what the value of C is. For a given larger p, there exist optimal coupling strength levels, where firing activity reaches its maximum. To the best of our knowledge, this is a novel mechanism for the emergence of firing activity in neurons. Copyright EDP Sciences/Società Italiana di Fisica/Springer-Verlag 2008

Suggested Citation

  • D. Q. Wei & X. S. Luo & Y. L. Zou, 2008. "Firing activity of complex space-clamped FitzHugh-Nagumo neural networks," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 63(2), pages 279-282, May.
    • Handle: RePEc:spr:eurphb:v:63:y:2008:i:2:p:279-282
    DOI: 10.1140/epjb/e2008-00227-5
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    1. Dorogovtsev, S.N. & Mendes, J.F.F., 2003. "Evolution of Networks: From Biological Nets to the Internet and WWW," OUP Catalogue, Oxford University Press, number 9780198515906.
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    Cited by:

    1. Ge, Mengyan & Jia, Ya & Xu, Ying & Lu, Lulu & Wang, Huiwen & Zhao, Yunjie, 2019. "Wave propagation and synchronization induced by chemical autapse in chain Hindmarsh–Rose neural network," Applied Mathematics and Computation, Elsevier, vol. 352(C), pages 136-145.
    2. Skrzypek, Leslaw & You, Yuncheng, 2021. "Dynamics and synchronization of boundary coupled FitzHugh-Nagumo neural networks," Applied Mathematics and Computation, Elsevier, vol. 388(C).

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