Original Research ARTICLE
Optimal self-induced stochastic resonance in multiplex neural networks: electrical versus chemical synapses
- 1Max Planck Institute for Mathematics in the Sciences, Germany
- 2Technical University of Denmark, Denmark
- 3University of Copenhagen, Denmark
Electrical and chemical synapses shape the dynamics of neural networks and their functional roles in information processing has 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. And 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 the mechanism of self-induced stochastic resonance in networks of artificial neural circuits, as well as in real biological neural networks.
Keywords: optimization, Self-induced stochastic resonance, Synapses, multiplex neural network, community structure
Received: 01 Apr 2020;
Accepted: 28 May 2020.
Copyright: © 2020 Yamakou, Hjorth and Martens. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Dr. Marius E. Yamakou, Max Planck Institute for Mathematics in the Sciences, Leipzig, Germany, firstname.lastname@example.org