Optimal Self-Induced Stochastic Resonance in Multiplex Neural Networks: Electrical vs. Chemical Synapses
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Optimal Self-Induced Stochastic Resonance in Multiplex Neural Networks : Electrical vs. Chemical Synapses. / Yamakou, Marius E.; Hjorth, Poul G.; Martens, Erik A.
In: Frontiers in Computational Neuroscience, Vol. 14, 62, 2020.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Optimal Self-Induced Stochastic Resonance in Multiplex Neural Networks
T2 - Electrical vs. Chemical Synapses
AU - Yamakou, Marius E.
AU - Hjorth, Poul G.
AU - Martens, Erik A.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - community structure
KW - multiplex neural network
KW - optimization
KW - self-induced stochastic resonance
KW - synapses
U2 - 10.3389/fncom.2020.00062
DO - 10.3389/fncom.2020.00062
M3 - Journal article
C2 - 32848683
AN - SCOPUS:85090004431
VL - 14
JO - Frontiers in Computational Neuroscience
JF - Frontiers in Computational Neuroscience
SN - 1662-5188
M1 - 62
ER -
ID: 248456038