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  Influence of Autapses on Synchronization in Neural Networks With Chemical Synapses

Protachevicz, P. R., Iarosz, K. C., Caldas, I. L., Antonopoulos, C. G., Batista, A. M., Kurths, J. (2020): Influence of Autapses on Synchronization in Neural Networks With Chemical Synapses. - Frontiers in Systems Neuroscience, 14, 604563.
https://doi.org/10.3389/fnsys.2020.604563

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 Creators:
Protachevicz, Paulo R.1, Author
Iarosz, Kelly C.1, Author
Caldas, Iberê L.1, Author
Antonopoulos, Chris G.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: A great deal of research has been devoted on the investigation of neural dynamics in various network topologies. However, only a few studies have focused on the influence of autapses, synapses from a neuron onto itself via closed loops, on neural synchronization. Here, we build a random network with adaptive exponential integrate-and-fire neurons coupled with chemical synapses, equipped with autapses, to study the effect of the latter on synchronous behavior. We consider time delay in the conductance of the pre-synaptic neuron for excitatory and inhibitory connections. Interestingly, in neural networks consisting of both excitatory and inhibitory neurons, we uncover that synchronous behavior depends on their synapse type. Our results provide evidence on the synchronous and desynchronous activities that emerge in random neural networks with chemical, inhibitory and excitatory synapses where neurons are equipped with autapses.

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 Dates: 2020-11-052020-11-302020-11-30
 Publication Status: Finally published
 Pages: -
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 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.3389/fnsys.2020.604563
MDB-ID: No data to archive
PIKDOMAIN: RD4 - Complexity Science
Organisational keyword: RD4 - Complexity Science
Research topic keyword: Health
Research topic keyword: Nonlinear Dynamics
Research topic keyword: Complex Networks
Working Group: Network- and machine-learning-based prediction of extreme events
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Title: Frontiers in Systems Neuroscience
Source Genre: Journal, SCI, Scopus, oa
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Pages: - Volume / Issue: 14 Sequence Number: 604563 Start / End Page: - Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/frontiers-systems-neuroscience
Publisher: Frontiers