ArticleScientific reports2019
A two-layered brain network model and its chimera state.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed.
- Amplitude entropy captures chimera resembling behavior in the altered brain dynamics during seizures.Scientific reports · 2025Article
- Emergence of metastability in frustrated oscillatory networks: the key role of hierarchical modularity.Frontiers in network physiology · 2024Article
- Fractal basins as a mechanism for the nimble brain.Scientific reports · 2023Article
- Chimera state in a feed-forward neuronal network.Cognitive neurodynamics · 2023Article
- The fundamental benefits of multiplexity in ecological networks.Journal of the Royal Society, Interface · 2022Article
- Basins of attraction of chimera states on networks.Frontiers in physiology · 2022Article
- Regulating heat conduction of complex networks by distributed nodes masses.Scientific reports · 2021Article
- A Brief Review of Chimera State in Empirical Brain Networks.Frontiers in physiology · 2020Review
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4 authors.
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Abstract
Based on the data of cerebral cortex, we present a two-layered brain network model of coupled neurons where the two layers represent the left and right hemispheres of cerebral cortex, respectively, and the links between the two layers represent the inter-couplings through the corpus callosum. By this model we show that abundant patterns of synchronization can be observed, especially the chimera state, depending on the parameters of system such as the coupling strengths and coupling phase. Further, we extend the model to a more general two-layered network to better understand the mechanism of the observed patterns, where each hemisphere of cerebral cortex is replaced by a highly clustered subnetwork. We find that the number of inter-couplings is another key parameter for the emergence of chimera states. Thus, the chimera states come from a matching between the structure parameters such as the number of inter-couplings and clustering coefficient etc and the dynamics parameters such as the intra-, inter-coupling strengths and coupling phase etc. A brief theoretical analysis is provided to explain the borderline of synchronization. These findings may provide helpful clues to understand the mechanism of brain functions.
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