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Electrical and chemical synapses shape the dynamics of neural networks andtheir functional roles in information processing have been a longstandingquestion in neurobiology. In this paper, we investigate the role of synapses onthe optimization of the phenomenon of self-induced stochastic resonance in adelayed multiplex neural network by using analytical and numerical methods. Weconsider a two-layer multiplex network, in which at the intra-layer levelneurons are coupled either by electrical synapses or by inhibitory chemicalsynapses. For each isolated layer, computations indicate that weaker electricaland chemical synaptic couplings are better optimizers of self-inducedstochastic resonance. In addition, regardless of the synaptic strengths,shorter electrical synaptic delays are found to be better optimizers of thephenomenon than shorter chemical synaptic delays, while longer chemicalsynaptic delays are better optimizers than longer electrical synaptic delays --in both cases, the poorer optimizers are in fact worst. It is found thatelectrical, inhibitory, or excitatory chemical multiplexing of the two layershaving only electrical synapses at the intra-layer levels can each optimize thephenomenon. And only excitatory chemical multiplexing of the two layers havingonly inhibitory chemical synapses at the intra-layer levels can optimize thephenomenon. These results may guide experiments aimed at establishing orconfirming the mechanism of self-induced stochastic resonance in networks ofartificial neural circuits, as well as in real biological neural networks.

Optimal self-induced stochastic resonance in multiplex neural networks: electrical versus chemical synapses