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Synchronization of interacted spiking neuronal networks with inhibitory coupling

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  • Andreev, Andrey V.
  • Maksimenko, Vladimir A.
  • Pisarchik, Alexander N.
  • Hramov, Alexander E.

Abstract

The development of mathematical models to describe neuronal interaction processes in the brain is a challenging task of nonlinear dynamics. Recent advances in biochemistry and neuroscience allow better understanding of biological mechanisms underlying the neuron functioning and synaptic connections between neurons. Moreover, significant progress in brain imaging sheds light on the structure of the brain network and certain aspects of neuronal dynamics. However, dynamical mechanisms leading to synchronization between different brain areas still remain unknown and require further investigation. To shed light on this issue, we consider two small-world networks of Hodgkin-Huxley neurons interacting via inhibitory coupling. We found that synchronization indices (SI) in both networks oscillate periodically in time, so that time intervals of high SI alternate with time intervals of low SI. Depending on the coupling strength, the two coupled networks can be in the regime of either in-phase or anti-phase synchronization. We suppose that the inherent mechanism behind such a behavior lies in the cognitive resource redistribution between neuronal ensembles of the brain.

Suggested Citation

  • Andreev, Andrey V. & Maksimenko, Vladimir A. & Pisarchik, Alexander N. & Hramov, Alexander E., 2021. "Synchronization of interacted spiking neuronal networks with inhibitory coupling," Chaos, Solitons & Fractals, Elsevier, vol. 146(C).
    • Handle: RePEc:eee:chsofr:v:146:y:2021:i:c:s0960077921001648
    DOI: 10.1016/j.chaos.2021.110812
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    Cited by:

    1. Irina A. Bashkirtseva & Alexander N. Pisarchik & Lev B. Ryashko, 2023. "Coexisting Attractors and Multistate Noise-Induced Intermittency in a Cycle Ring of Rulkov Neurons," Mathematics, MDPI, vol. 11(3), pages 1-12, January.
    2. Othman Abdullah Almatroud & Viet-Thanh Pham & Karthikeyan Rajagopal, 2024. "A Rectified Linear Unit-Based Memristor-Enhanced Morris–Lecar Neuron Model," Mathematics, MDPI, vol. 12(19), pages 1-10, September.

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