Skellam process with resetting: a neural spike train model |
| |
Authors: | Reza Ramezan Paul Marriott Shojaeddin Chenouri |
| |
Affiliation: | 1. Department of Mathematics, California State University, Fullerton, 800 N. State College Blvd., Fullerton, CA 92831, U.S.A.;2. Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, ON, Canada |
| |
Abstract: | 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. |
| |
Keywords: | ISI distribution mixture of gamma distributions neural spike trains records retinal ganglion cells skellam process skellam process with resetting |
|
|