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  Basins of attraction of chimera states on networks

Li, Q., Larosz, K. C., Han, D., Ji, P., Kurths, J. (2022): Basins of attraction of chimera states on networks. - Frontiers in Physiology, 13, 959431.
https://doi.org/10.3389/fphys.2022.959431

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 Urheber:
Li, Qiang1, Autor
Larosz, Kelly C.1, Autor
Han, Dingding1, Autor
Ji, Peng1, Autor
Kurths, Jürgen2, Autor              
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Zusammenfassung: Networks of identical coupled oscillators display a remarkable spatiotemporal pattern, the chimera state, where coherent oscillations coexist with incoherent ones. In this paper we show quantitatively in terms of basin stability that stable and breathing chimera states in the original two coupled networks typically have very small basins of attraction. In fact, the original system is dominated by periodic and quasi-periodic chimera states, in strong contrast to the model after reduction, which can not be uncovered by the Ott-Antonsen ansatz. Moreover, we demonstrate that the curve of the basin stability behaves bimodally after the system being subjected to even large perturbations. Finally, we investigate the emergence of chimera states in brain network, through inducing perturbations by stimulating brain regions. The emerged chimera states are quantified by Kuramoto order parameter and chimera index, and results show a weak and negative correlation between these two metrics.

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Sprache(n): eng - Englisch
 Datum: 2022-09-082022-09-08
 Publikationsstatus: Final veröffentlicht
 Seiten: 13
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: Expertenbegutachtung
 Identifikatoren: DOI: 10.3389/fphys.2022.959431
MDB-ID: No data to archive
PIKDOMAIN: RD4 - Complexity Science
Organisational keyword: RD4 - Complexity Science
Research topic keyword: Complex Networks
Research topic keyword: Nonlinear Dynamics
Research topic keyword: Health
Working Group: Network- and machine-learning-based prediction of extreme events
OATYPE: Gold Open Access
 Art des Abschluß: -

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Titel: Frontiers in Physiology
Genre der Quelle: Zeitschrift, SCI, Scopus, oa
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Seiten: - Band / Heft: 13 Artikelnummer: 959431 Start- / Endseite: - Identifikator: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/frontiers-in-physiology
Publisher: Frontiers