title: Dynamic phenotypes: time series analysis techniques for characterizing neuronal and behavioral dynamics.
abstract: We consider quantitative measures of behavioral and neuronal dynamics as a means of characterizing phenotypes. Such measures are important from a scientific perspective; because understanding brain function is contingent on understanding the link between the dynamics of the nervous system and behavioral dynamics. They are also important from a biomedical perspective because they provide a contrast to purely psychological characterizations of phenotype or characterizations via static brain images or maps, and are a potential means for differential diagnoses of neuropsychiatric illnesses. After a brief presentation of background work and some current advances, we suggest that more attention needs to be paid to dynamic characterizations of phenotypes. We will discuss some of the relevant time series analysis tools.
authors.affiliation: Cold Spring Harbor Laboratory, 1 Bungtown Road, Cold Spring Harbor, NY 11724, USA. bokil@cshl.edu
authors.forename: Hemant
authors.initials: H
authors.lastname: Bokil
authors.forename: Ofer
authors.initials: O
authors.lastname: Tchernichovsky
authors.forename: Partha P
authors.initials: PP
authors.lastname: Mitra
mesh_terms: D000200:Action Potentials / Q000502:physiology; D000818:Animals; D001522:Behavior, Animal / Q000502:physiology; D001921:Brain / Q000502:physiology*; D004569:Electroencephalography / Q000379:methods / Q000639:trends; D004594:Electrophysiology / Q000379:methods* / Q000639:trends; D006801:Humans; D015225:Magnetoencephalography / Q000379:methods / Q000639:trends; D009474:Neurons / Q000502:physiology*; D010641:Phenotype; D012815:Signal Processing, Computer-Assisted; D013997:Time Factors