Skellam process with resetting: a neural spike train model.

Autor: Ramezan R; Department of Mathematics, California State University, Fullerton, 800 N. State College Blvd., Fullerton, CA 92831, U.S.A., Marriott P; Department of Statistics and Actuarial Science, University of Waterloo, 200 University Ave. W., Waterloo, ON, N2L 3G1, Canada., Chenouri S; Department of Statistics and Actuarial Science, University of Waterloo, 200 University Ave. W., Waterloo, ON, N2L 3G1, Canada.
Jazyk: angličtina
Zdroj: Statistics in medicine [Stat Med] 2016 Dec 30; Vol. 35 (30), pp. 5717-5729. Date of Electronic Publication: 2016 Sep 26.
DOI: 10.1002/sim.7127
Abstrakt: This paper introduces the Skellam process with resetting. Resetting is a modification that accommodates the modeling of neural spike trains. We show this as a biologically plausible model, which codes the information content of neural spike trains with three, potentially, time-varying functions. We show that the interspike interval distribution under this model follows a mixture of gamma distributions, a flexible class covering a wide range of commonly used models. Through simulation studies and the analyses of connected retinal ganglion and lateral geniculate nucleus cells, we evaluate the performance of this model. Copyright © 2016 John Wiley & Sons, Ltd.
(Copyright © 2016 John Wiley & Sons, Ltd.)
Databáze: MEDLINE
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