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Chemical synaptic multiplexing enhances rhythmicity in neuronal networks

Nonlinear Dynamics volume 98, pages 1659–1668 (2019)Cite this article

Abstract

Real-world networks are rarely isolated; rather, they constitute a large number of elements interacting through complex topologies and oscillation is essential for their proper functioning. But degradation may come up naturally in such large systems that can severely affect the dynamical activity of the entire network. This mandates us to prescribe some remedies to overcome such deterioration. In this work, we demonstrate this scenario using a neuronal model organized in a framework of multiplex structure composed of a mixture of active and inactive neurons, while interacting via both electrical gap junction and chemical synapse. Multiplex architecture being very much prominent in cortical networks, we explore the simultaneous effect of the electrical and chemical synapses in the persistence of global rhythmicity of a multiplex neuronal network. Our results suggest that although electrical synapse reduces the dynamical performance of the network, chemical synapse through interlayer connection is highly efficient in reviving the rhythmicity of the network. Moreover, we investigate the effect of demultiplexing on the resilience of the network and show that chemical synaptic coupling can revive global rhythmicity under progressive demultiplexing as well. We also demonstrate this effectiveness of the chemical synapses for the case of reverse transition from global rest state to dynamism.

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Authors and Affiliations

  1. Physics and Applied Mathematics Unit, Indian Statistical Institute, 203 B. T. Road, Kolkata, 700108, India

    Srilena Kundu, Soumen Majhi & Dibakar Ghosh

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  1. Srilena Kundu
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  2. Soumen Majhi
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  3. Dibakar Ghosh
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Correspondence to Dibakar Ghosh.

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Kundu, S., Majhi, S. & Ghosh, D. Chemical synaptic multiplexing enhances rhythmicity in neuronal networks. Nonlinear Dyn 98, 1659–1668 (2019). https://doi.org/10.1007/s11071-019-05277-y

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Keywords

  • Neuronal systems
  • Multiplex networks
  • Dynamical robustness