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• W2135234927 startingPage "62" @default.
• W2135234927 abstract "In a network of dynamical systems, concurrent synchronization is a regime where multiple groups of fully synchronized elements coexist. In the brain, concurrent synchronization may occur at several scales, with multiple rhythms interacting and functional assemblies combining neural oscillators of many different types. Mathematically, stable concurrent synchronization corresponds to convergence to a flow-invariant linear subspace of the global state space. We derive a general condition for such convergence to occur globally and exponentially. We also show that, under mild conditions, global convergence to a concurrently synchronized regime is preserved under basic system combinations such as negative feedback or hierarchies, so that stable concurrently synchronized aggregates of arbitrary size can be constructed. Robustnesss of stable concurrent synchronization to variations in individual dynamics is also quantified. Simple applications of these results to classical questions in systems neuroscience and robotics are discussed." @default.
• W2135234927 created "2016-06-24" @default.
• W2135234927 creator A5044026747 @default.
• W2135234927 creator A5081279690 @default.
• W2135234927 date "2007-01-01" @default.
• W2135234927 modified "2023-09-26" @default.
• W2135234927 title "Stable concurrent synchronization in dynamic system networks" @default.
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• W2135234927 doi "https://doi.org/10.1016/j.neunet.2006.07.008" @default.
• W2135234927 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/17029881" @default.
• W2135234927 hasPublicationYear "2007" @default.
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