title: Detecting determinism from point processes.
abstract: The detection of a nonrandom structure from experimental data can be crucial for the classification, understanding, and interpretation of the generating process. We here introduce a rank-based nonlinear predictability score to detect determinism from point process data. Thanks to its modular nature, this approach can be adapted to whatever signature in the data one considers indicative of deterministic structure. After validating our approach using point process signals from deterministic and stochastic model dynamics, we show an application to neuronal spike trains recorded in the brain of an epilepsy patient. While we illustrate our approach in the context of temporal point processes, it can be readily applied to spatial point processes as well.
authors.affiliation: Universitat Pompeu Fabra, Department of Information and Communication Technologies, E-08018 Barcelona, Spain.
authors.forename: Ralph G
authors.initials: RG
authors.lastname: Andrzejak
authors.affiliation: Department of Epileptology, University of Bonn, D-53105 Bonn, Germany.
authors.forename: Florian
authors.initials: F
authors.lastname: Mormann
authors.affiliation: Institute for Complex Systems, CNR, I-50119 Sesto Fiorentino, Italy.
authors.forename: Thomas
authors.initials: T
authors.lastname: Kreuz
mesh_terms: D000200:Action Potentials; D001921:Brain / Q000166:cytology / Q000473:pathology; D004827:Epilepsy / Q000473:pathology; D006801:Humans; D008959:Models, Neurological; D009474:Neurons / Q000166:cytology / Q000473:pathology; D017711:Nonlinear Dynamics; D013269:Stochastic Processes