title: Direct estimation of inhomogeneous Markov interval models of spike trains.
abstract: A necessary ingredient for a quantitative theory of neural coding is appropriate "spike kinematics": a precise description of spike trains. While summarizing experiments by complete spike time collections is clearly inefficient and probably unnecessary, the most common probabilistic model used in neurophysiology, the inhomogeneous Poisson process, often seems too crude. Recently a more general model, the inhomogeneous Markov interval model (Berry & Meister, 1998 ; Kass & Ventura, 2001 ), was considered, which takes into account both the current experimental time and the time from the last spike. Several techniques were proposed to estimate the parameters of these models from data. Here we propose a direct method of estimation that is easy to implement, fast, and conceptually simple. The method is illustrated with an analysis of sample data from the cat's superior colliculus.
authors.affiliation: Department of Neurophysiology, Nencki Institute of Experimental Biology, Warsaw 02-093, Poland. d.wojcik@nencki.gov.pl
authors.forename: Daniel K
authors.initials: DK
authors.lastname: Wójcik
authors.forename: Gabriela
authors.initials: G
authors.lastname: Mochol
authors.forename: Wit
authors.initials: W
authors.lastname: Jakuczun
authors.forename: Marek
authors.initials: M
authors.lastname: Wypych
authors.forename: Wioletta J
authors.initials: WJ
authors.lastname: Waleszczyk
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000818:Animals; D008390:Markov Chains; D008959:Models, Neurological; D009474:Neurons / Q000502:physiology*; D013997:Time Factors