title: Hierarchy and polysynchrony in an adaptive network.
abstract: We describe a simple adaptive network of coupled chaotic maps. The network reaches a stationary state (frozen topology) for all values of the coupling parameter, although the dynamics of the maps at the nodes of the network can be nontrivial. The structure of the network shows interesting hierarchical properties and in certain parameter regions the dynamics is polysynchronous: Nodes can be divided in differently synchronized classes but, contrary to cluster synchronization, nodes in the same class need not be connected to each other. These complicated synchrony patterns have been conjectured to play roles in systems biology and circuits. The adaptive system we study describes ways whereby this behavior can evolve from undifferentiated nodes.
authors.affiliation: IST Austria (Institute of Science and Technology Austria), Am Campus 1, A-3400 Klosterneuburg, Austria.
authors.forename: V
authors.initials: V
authors.lastname: Botella-Soler
authors.affiliation: School of Mathematics and Centre for Interdisciplinary Computational and Dynamical Analysis (CICADA),University of Manchester, Manchester M13 9PL, United Kingdom.
authors.forename: P
authors.initials: P
authors.lastname: Glendinning
mesh_terms: D000222:Adaptation, Physiological / Q000502:physiology*; D000818:Animals; D003198:Computer Simulation; D006801:Humans; D008959:Models, Neurological; D015233:Models, Statistical; D009415:Nerve Net / Q000502:physiology*; D009473:Neuronal Plasticity / Q000502:physiology*