title: Estimating phase synchronization in dynamical systems using cellular nonlinear networks.
abstract: We propose a method for estimating phase synchronization between time series using the parallel computing architecture of cellular nonlinear networks (CNN's). Applying this method to time series of coupled nonlinear model systems and to electroencephalographic time series from epilepsy patients, we show that an accurate approximation of the mean phase coherence R--a bivariate measure for phase synchronization--can be achieved with CNN's using polynomial-type templates.
authors.affiliation: Department of Epileptology, Neurophysics Group, University of Bonn, Sigmund-Freud-Strasse 25, D-53105 Bonn, Germany.
authors.forename: Robert
authors.initials: R
authors.lastname: Sowa
authors.forename: Anton
authors.initials: A
authors.lastname: Chernihovskyi
authors.forename: Florian
authors.initials: F
authors.lastname: Mormann
authors.forename: Klaus
authors.initials: K
authors.lastname: Lehnertz
mesh_terms: D000200:Action Potentials; D001921:Brain / Q000503:physiopathology*; D003198:Computer Simulation; D004569:Electroencephalography / Q000379:methods*; D004827:Epilepsy / Q000503:physiopathology*; D006801:Humans; D008959:Models, Neurological; D009415:Nerve Net / Q000503:physiopathology*; D009474:Neurons; D017711:Nonlinear Dynamics; D009435:Synaptic Transmission