Skellam process with resetting: a neural spike train model.
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.
Department of Mathematics, California State University, Fullerton, 800 N. State College Blvd., Fullerton, CA 92831, U.S.A.
Reza
R
Ramezan
Department of Statistics and Actuarial Science, University of Waterloo, 200 University Ave. W., Waterloo, ON, N2L 3G1, Canada.
Paul
P
Marriott
Department of Statistics and Actuarial Science, University of Waterloo, 200 University Ave. W., Waterloo, ON, N2L 3G1, Canada.
Shojaeddin
S
Chenouri
D000200:Action Potentials; D005829:Geniculate Bodies; D008959:Models, Neurological; D015233:Models, Statistical; D012165:Retinal Ganglion Cells
ISI distribution; mixture of gamma distributions; neural spike trains; records; retinal ganglion cells; skellam process; skellam process with resetting