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Tytuł:
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Spike signal transmission between modules and the predictability of spike activity in modular neuronal networks.
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Autorzy:
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Yuan Y; Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China; Key Laboratory of System Control and Information Processing, Ministry of Education, Shanghai 200240, China. Electronic address: yuanye_.
Liu J; Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China; Key Laboratory of System Control and Information Processing, Ministry of Education, Shanghai 200240, China. Electronic address: .
Zhao P; Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China; Key Laboratory of System Control and Information Processing, Ministry of Education, Shanghai 200240, China. Electronic address: zhaopeng_.
Huo H; Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China; Key Laboratory of System Control and Information Processing, Ministry of Education, Shanghai 200240, China. Electronic address: .
Fang T; Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China; Key Laboratory of System Control and Information Processing, Ministry of Education, Shanghai 200240, China. Electronic address: .
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Źródło:
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Journal of theoretical biology [J Theor Biol] 2021 Oct 07; Vol. 526, pp. 110811. Date of Electronic Publication: 2021 Jun 13.
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Typ publikacji:
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Journal Article; Research Support, Non-U.S. Gov't
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Język:
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English
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Imprint Name(s):
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Publication: Amsterdam : Elsevier
Original Publication: London.
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MeSH Terms:
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Models, Neurological*
Nerve Net*
Action Potentials ; Learning ; Neuronal Plasticity ; Neurons ; Synaptic Transmission
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Contributed Indexing:
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Keywords: Modular neuronal networks; Modularity; Resting-state functional connectivity matrix; STDP learning rule; Spike signal transmission
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Entry Date(s):
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Date Created: 20210616 Date Completed: 20210809 Latest Revision: 20210809
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Update Code:
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20240104
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DOI:
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10.1016/j.jtbi.2021.110811
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PMID:
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34133949
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Modularity is a common feature of the nervous system across species and scales. Although it has been qualitatively investigated in network science, very little is known about how it affects spike signal transmission in neuronal networks at the mesoscopic level. Here, a neuronal network model is built to simulate dynamic interactions among different modules of neuronal networks. This neuronal network model follows the organizational principle of modular structure. The neurons can generate spikes like biological neurons, and changes in the strength of synaptic connections conform to the STDP learning rule. Based on this neuronal network model, we first quantitatively studied whether and to what extent the connectivity within and between modules can affect spike signal transmission, and found that spike signal transmission heavily depends on the connectivity between modules, but has little to do with the connectivity within modules. More importantly, we further found that the spike activity of a module can be predicted according to the spike activities of its adjacent modules through building a resting-state functional connectivity matrix.
Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2021 Elsevier Ltd. All rights reserved.)
