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Spreading and mitigation of extreme events among multilayer neuronal networks: Role of time-varying interaction
Chaos, Solitons and Fractals. 2026. Vol. 213. No. 2. P. 1–16.
In press
The complex network approach is widely used to uncover the propagation of information or events, such as disease outbreaks, rumors, and cascade failures, across several disciplines. In this study, we explore the spreading of extreme events between the multiplex neuronal networks and their mitigation process using time-varying interactions. Our analysis illuminates the propagation of extreme events from one layer to another with the influence of interlayer coupling strength and time-varying interaction parameters in global-to-global coupled driven FitzHugh–Nagumo neurons. In addition, the precise selection of time-varying interaction param eters leads to the elimination of adverse large-intensity events in the systems. The uniqueness of our observation is also evaluated by using small-world-to-small-world and global-to-small world networks. Our investigations reveal the unusual occurrence of extreme events in different parameter regions and how they can be mitigated in multilayer networks through time-varying interactions.
Language:
English
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