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  Switching from active to non-active states in a birhythmic conductance-based neuronal model under electromagnetic induction

Tagne Nkounga, I. B., Messee Goulefack, L., Yamapi, R., Kurths, J. (2023): Switching from active to non-active states in a birhythmic conductance-based neuronal model under electromagnetic induction. - Nonlinear Dynamics, 111, 771-788.
https://doi.org/10.1007/s11071-022-07842-4

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 Creators:
Tagne Nkounga, I. B.1, Author
Messee Goulefack, L.1, Author
Yamapi, R.1, Author
Kurths, Jürgen2, Author              
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Abstract: We consider a conductance-based neuronal model under the action of electromagnetic induction on the membrane potential. We focus on the impact of the magnetic flux on the membrane potential using theoretical methods (such as the harmonic and energy balance methods) and numerical methods (such as the bifurcation diagram and Lyapunov exponent). The strength of the electromagnetic induction is considered as the control parameter. Thus, the system can switch from bistable to monostable behavior at the first critical value of the control parameter. This is done by suppressing the active mode of the neuron and maintaining subthreshold mode until it achieved a second critical value of the control parameter for a quiescent mode. Improving the conductance-based neuronal model by adding electromagnetic induction effects relates different steps in the generation of complex forms of action potential (depolarization) such as spiking, bursting, chaos; and the regulation of the system by the switching to subthreshold oscillations (repolarization) or to a stable state (quiescent state) after a brief phase of the dynamic below the quiescent state (hyperpolarization).

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Language(s): eng - English
 Dates: 2022-09-292023-01
 Publication Status: Finally published
 Pages: 18
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1007/s11071-022-07842-4
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
Working Group: Network- and machine-learning-based prediction of extreme events
 Degree: -

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Title: Nonlinear Dynamics
Source Genre: Journal, SCI, Scopus
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Pages: - Volume / Issue: 111 Sequence Number: - Start / End Page: 771 - 788 Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/nonlinear-dynamics
Publisher: Springer