Physical review. E · 2022 · 40 citations · 43 references
Life SciencesNeuroscienceComplex SystemsComputational NeuroscienceDynamical SystemsBiological OscillationsComplex Adaptive SystemsNetwork DynamicsNeural Circuit DynamicsNeural OscillationsNeuronal DynamicsCoupled Oscillator ModelsHebbian Learning RulesNeuronal SynchronizationOscillatory DynamicsOscillatory SynchronySimplicial ComplexesSynchronization DynamicsSynchronization Transitions
This Letter investigates the transition to synchronization of oscillator ensembles encoded by simplicial complexes in which pairwise and higher-order coupling weights alter with time through a rate-based adaptive mechanism inspired by the Hebbian learning rule. These simultaneously evolving disparate adaptive coupling weights lead to a phenomenon in that the in-phase synchronization is completely obliterated; instead, the antiphase synchronization is originated. In addition, the onsets of antiphase synchronization and desynchronization are manageable through both dyadic and triadic learning rates. The theoretical validation of these numerical assessments is delineated thoroughly by employing Ott-Antonsen dimensionality reduction. The framework and results of the Letter would help understand the underlying synchronization behavior of a range of real-world systems, such as the brain functions and social systems where interactions evolve with time.
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