Premium
Skellam process with resetting: a neural spike train model
Author(s) -
Ramezan Reza,
Marriott Paul,
Chenouri Shojaeddin
Publication year - 2016
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.7127
Subject(s) - spike (software development) , computer science , train , process (computing) , range (aeronautics) , spike train , interval (graph theory) , artificial intelligence , mathematics , materials science , cartography , software engineering , combinatorics , composite material , geography , operating system
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.