Optimal Self-Induced Stochastic Resonance in Multiplex Neural Networks : Electrical vs. Chemical Synapses
Yamakou, Marius E. ; Hjorth, Poul G. and Martens, Erik A. LU
(2020)
In Frontiers in Computational Neuroscience
14.
- 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... (More)
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.
(Less)
- author
- Yamakou, Marius E.
; Hjorth, Poul G.
and Martens, Erik A.
LU
- publishing date
- 2020-08-07
- type
- Contribution to journal
- publication status
- published
- keywords
- community structure, multiplex neural network, optimization, self-induced stochastic resonance, synapses
- in
- Frontiers in Computational Neuroscience
- volume
- 14
- article number
- 62
- publisher
- Frontiers Media S. A.
- external identifiers
-
- scopus:85090004431
- pmid:32848683
- ISSN
- 1662-5188
- DOI
- 10.3389/fncom.2020.00062
- language
- English
- LU publication?
- no
- additional info
- Funding Information: MY would like to acknowledge the warm hospitality at Department of Applied Mathematics and Computer Science of the Technical University of Denmark and for financial support. Publisher Copyright: © Copyright © 2020 Yamakou, Hjorth and Martens. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.
- id
- efed39b0-2955-4737-a3ec-142bcf49e39a
- date added to LUP
- 2021-03-19 21:19:27
- date last changed
- 2024-06-28 13:10:08
@article{efed39b0-2955-4737-a3ec-142bcf49e39a,
abstract = {{<p>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.</p>}},
author = {{Yamakou, Marius E. and Hjorth, Poul G. and Martens, Erik A.}},
issn = {{1662-5188}},
keywords = {{community structure; multiplex neural network; optimization; self-induced stochastic resonance; synapses}},
language = {{eng}},
month = {{08}},
publisher = {{Frontiers Media S. A.}},
series = {{Frontiers in Computational Neuroscience}},
title = {{Optimal Self-Induced Stochastic Resonance in Multiplex Neural Networks : Electrical vs. Chemical Synapses}},
url = {{http://dx.doi.org/10.3389/fncom.2020.00062}},
doi = {{10.3389/fncom.2020.00062}},
volume = {{14}},
year = {{2020}},
}