Understanding the enhanced synchronization of delay-coupled networks with fluctuating topology

We study the dynamics of networks with coupling delay, from which the connectivity changes over time. The synchronization properties are shown to depend on the interplay of three time scales: the internal time scale of the dynamics, the coupling delay along the network links and time scale at which...

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Bibliographic Details
Published inarXiv.org
Main Authors D'Huys, Otti, Rodríguez-Laguna, Javier, Jiménez, Manuel, Korutcheva, Elka, Kinzel, Wolfgang
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 08.05.2018
Subjects
Computer simulation
Coupling
Delay
Dependence
Mathematical models
Network topologies
Networks
Stability analysis
Switching theory
Synchronism
Time
Time synchronization
Variation
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Summary:We study the dynamics of networks with coupling delay, from which the connectivity changes over time. The synchronization properties are shown to depend on the interplay of three time scales: the internal time scale of the dynamics, the coupling delay along the network links and time scale at which the topology changes. Concentrating on a linearized model, we develop an analytical theory for the stability of a synchronized solution. In two limit cases the system can be reduced to an "effective" topology: In the fast switching approximation, when the network fluctuations are much faster than the internal time scale and the coupling delay, the effective network topology is the arithmetic mean over the different topologies. In the slow network limit, when the network fluctuation time scale is equal to the coupling delay, the effective adjacency matrix is the geometric mean over the adjacency matrices of the different topologies. In the intermediate regime the system shows a sensitive dependence on the ratio of time scales, and specific topologies, reproduced as well by numerical simulations. Our results are shown to describe the synchronization properties of fluctuating networks of delay-coupled chaotic maps.
ISSN:2331-8422