Optimal Self-Induced Stochastic Resonance in Multiplex Neural Networks: Electrical vs. Chemical Synapses.

Marius E. Yamakou,Erik A. Martens,Poul G. Hjorth

doi:10.3389/fncom.2020.00062

Abstract

Electrical and chemical synapses shape the dynamics of neural networks, and their functional roles in information processing have been a longstanding question in neurobiology. In this paper, we investigate the role of synapses on the optimization of the phenomenon of self-induced stochastic resonance in a delayed multiplex neural network by using analytical and numerical methods. We consider a two-layer multiplex network in which, at the intra-layer level, neurons are coupled either by electrical synapses or by inhibitory chemical synapses. For each isolated layer, computations indicate that weaker electrical and chemical synaptic couplings are better optimizers of self-induced stochastic resonance. In addition, regardless of the synaptic strengths, shorter electrical synaptic delays are found to be better optimizers of the phenomenon than shorter chemical synaptic delays, while longer chemical synaptic delays are better optimizers than longer electrical synaptic delays; in both cases, the poorer optimizers are, in fact, worst. It is found that electrical, inhibitory, or excitatory chemical multiplexing of the two layers having only electrical synapses at the intra-layer levels can each optimize the phenomenon. Additionally, only excitatory chemical multiplexing of the two layers having only inhibitory chemical synapses at the intra-layer levels can optimize the phenomenon. These results may guide experiments aimed at establishing or confirming to the mechanism of self-induced stochastic resonance in networks of artificial neural circuits as well as in real biological neural networks.

Highlights

Noise is an inherent part of neuronal dynamics, and its effects can be observed experimentally in neuronal activity at different spatiotemporal scales, e.g., at the level of ion channels, neuronal membrane potentials, local field potentials, and electroencephalographic or magnetoencephalographic measurements (Guo et al, 2018)
Some mechanisms for optimal information processing are provided via the well-known and extensively studied phenomena of stochastic resonance (SR) (Benzi et al, 1981; Longtin, 1993; Gammaitoni et al, 1998; Lindner et al, 2004; Zhang et al, 2015) and coherence resonance (CR) (Hu and MacDonald, 1993; Neiman et al, 1997; Pikovsky and Kurths, 1997; Lindner and Schimansky-Geier, 1999; Lindner et al, 2004; Beato et al, 2007; Hizanidis and Schöll, 2008; Liu et al, 2010; Bing et al, 2011; Gu et al, 2011) or via the lesser-known phenomenon of selfinduced stochastic resonance (SISR) (Freidlin, 2001; Muratov et al, 2005; DeVille and Vanden-Eijnden, 2007; DeVille et al, 2007; Yamakou and Jost, 2017, 2018) whose mechanism remains to be confirmed experimentally in real neural systems
We focus on self-induced stochastic resonance (SISR)

Summary

Introduction

Noise is an inherent part of neuronal dynamics, and its effects can be observed experimentally in neuronal activity at different spatiotemporal scales, e.g., at the level of ion channels, neuronal membrane potentials, local field potentials, and electroencephalographic or magnetoencephalographic measurements (Guo et al, 2018). Some mechanisms for optimal information processing are provided via the well-known and extensively studied phenomena of stochastic resonance (SR) (Benzi et al, 1981; Longtin, 1993; Gammaitoni et al, 1998; Lindner et al, 2004; Zhang et al, 2015) and coherence resonance (CR) (Hu and MacDonald, 1993; Neiman et al, 1997; Pikovsky and Kurths, 1997; Lindner and Schimansky-Geier, 1999; Lindner et al, 2004; Beato et al, 2007; Hizanidis and Schöll, 2008; Liu et al, 2010; Bing et al, 2011; Gu et al, 2011) or via the lesser-known phenomenon of selfinduced stochastic resonance (SISR) (Freidlin, 2001; Muratov et al, 2005; DeVille and Vanden-Eijnden, 2007; DeVille et al, 2007; Yamakou and Jost, 2017, 2018) whose mechanism remains to be confirmed experimentally in real neural systems. It has been shown that hybrid synapses and autapses (i.e., those characterized by both electrical and chemical coupling) could be effectively used to control SR and CR (Yilmaz et al, 2013, 2016)

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Methods

Conclusion