title: Classification of temporal patterns in dynamic biological networks.
abstract: A general method is presented to classify temporal patterns generated by rhythmic biological networks when synaptic connections and cellular properties are known. The method is discrete in nature and relies on algebraic properties of state transitions and graph theory. Elements of the set of rhythms generated by a network are compared using a metric that quantifies the functional differences among them. The rhythms are then classified according to their location in a metric space. Examples are given, and biological implications are discussed.
authors.affiliation: Neurological Sciences Institute, Oregon Health Sciences University, Portland 97209, USA.
authors.forename: P D
authors.initials: PD
authors.lastname: Roberts
mesh_terms: D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D010507:Periodicity; D013997:Time Factors