title: Modeling spiking behavior of neurons with time-dependent Poisson processes.
abstract: Three kinds of interval statistics, as represented by the coefficient of variation, the skewness coefficient, and the correlation coefficient of consecutive intervals, are evaluated for three kinds of time-dependent Poisson processes: pulse regulated, sinusoidally regulated, and doubly stochastic. Among these three processes, the sinusoidally regulated and doubly stochastic Poisson processes, in the case when the spike rate varies slowly compared with the mean interval between spikes, are found to be consistent with the three statistical coefficients exhibited by data recorded from neurons in the prefrontal cortex of monkeys.
authors.affiliation: Department of Physics, Graduate School of Science, Kyoto University, Sakyo-ku, Kyoto 606-8502, Japan. shinomoto@scphys.kyoto-u.ac.jp
authors.forename: S
authors.initials: S
authors.lastname: Shinomoto
authors.forename: Y
authors.initials: Y
authors.lastname: Tsubo
mesh_terms: D000818:Animals; D055592:Biophysical Phenomena; D001703:Biophysics; D000882:Haplorhini; D008953:Models, Anatomic; D015233:Models, Statistical; D009415:Nerve Net; D009474:Neurons / Q000473:pathology / Q000502:physiology*; D016012:Poisson Distribution; D017397:Prefrontal Cortex / Q000473:pathology; D013997:Time Factors