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  Emergence of Neuronal Synchronisation in Coupled Areas

Protachevicz, P. R., Hansen, M., Iarosz, K. C., Caldas, I. L., Batista, A. M., Kurths, J. (2021): Emergence of Neuronal Synchronisation in Coupled Areas. - Frontiers in Computational Neuroscience, 15, 663408.
https://doi.org/10.3389/fncom.2021.663408

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Protachevicz, Paulo R.1, Author
Hansen, Matheus1, Author
Iarosz, Kelly C.1, Author
Caldas, Iberê L.1, Author
Batista, Antonio M.1, Author
Kurths, Jürgen2, Author              
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1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Abstract: One of the most fundamental questions in the field of neuroscience is the emergence of synchronous behaviour in the brain, such as phase, anti-phase, and shift-phase synchronisation. In this work, we investigate how the connectivity between brain areas can influence the phase angle and the neuronal synchronisation. To do this, we consider brain areas connected by means of excitatory and inhibitory synapses, in which the neuron dynamics is given by the adaptive exponential integrate-and-fire model. Our simulations suggest that excitatory and inhibitory connections from one area to another play a crucial role in the emergence of these types of synchronisation. Thus, in the case of unidirectional interaction, we observe that the phase angles of the neurons in the receiver area depend on the excitatory and inhibitory synapses which arrive from the sender area. Moreover, when the neurons in the sender area are synchronised, the phase angle variability of the receiver area can be reduced for some conductance values between the areas. For bidirectional interactions, we find that phase and anti-phase synchronisation can emerge due to excitatory and inhibitory connections. We also verify, for a strong inhibitory-to-excitatory interaction, the existence of silent neuronal activities, namely a large number of excitatory neurons that remain in silence for a long time.

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 Dates: 2021-04-222021-04-22
 Publication Status: Finally published
 Pages: -
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 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.3389/fncom.2021.663408
MDB-ID: No data to archive
PIKDOMAIN: RD4 - Complexity Science
Organisational keyword: RD4 - Complexity Science
Research topic keyword: Complex Networks
Research topic keyword: Health
Research topic keyword: Nonlinear Dynamics
OATYPE: Gold Open Access
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Title: Frontiers in Computational Neuroscience
Source Genre: Journal, SCI, Scopus, oa
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Pages: - Volume / Issue: 15 Sequence Number: 663408 Start / End Page: - Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/frontiers-computational-neuroscience
Publisher: Frontiers