title: Detecting subthreshold events in noisy data by symbolic dynamics.
abstract: We show that a symmetric threshold crossing detector can be described by a symbolic dynamics of a static three-symbol encoding which is highly efficient to detect subthreshold events in noisy nonstationary data. After computing instantaneous word statistics and running cylinder entropies, we introduce a mean-field transformation of the three-symbol dynamics considered as a Potts-spin lattice onto a distribution of two symbols. This transformed word statistics enables one to derive an estimator of the signal-to-noise ratio (SNR). Subthreshold events are then proven by a prominent peak of the SNR estimator as a function of the noise intensity.
authors.affiliation: Institute of Linguistics, Universität Potsdam, P.O. Box 601553, 14415 Potsdam, Germany. peter@ling.uni-potsdam.de
authors.forename: Peter
authors.initials: P
authors.lastname: Beim Graben
authors.forename: Jürgen
authors.initials: J
authors.lastname: Kurths
mesh_terms: D019277:Entropy; D007473:Ion Channels / Q000502:physiology; D009474:Neurons / Q000502:physiology; D017711:Nonlinear Dynamics; D013269:Stochastic Processes
chemical_list: D007473:Ion Channels