COLLOQUIUM
Cleveland State University
Department of Mathematics
Multivariate Analysis of Neural Spike Trains:
Skellam Process with Resetting and Its Applications
Dr. Reza Ramezan
(Candidate for the position of Assistant Professor, BioStatistics)
Monday, February 3, 2014
3:00 PM
Rhodes Tower 1516
Nerve cells (a.k.a. neurons) communicate via electrochemical waves (action
potentials), which are usually called spikes as they are very localized in time. A
sequence of consecutive spikes from one neuron is called a spike train. The exact
mechanism of information coding in spike trains is still an open problem; however,
one popular approach is to model spikes as realizations of an inhomogeneous
Poisson process. In this talk, we highlight the limitations of the Poisson model, and
introduce the Skellam Process with Resetting (SPR) as an alternative model for the
analysis of neural spike trains. SPR is biologically justified, and the parameter
estimation algorithm which we have developed for it is computationally efficient.
To allow for the modelling of neural ensembles, we generalize this process to the
multivariate case, where Multivariate Skellam Process with Resetting (MSPR), as
well as the multivariate Skellam distribution are introduced. We show that
simulation and real data studies confirm the promising results of the Skellam
model in the statistical modelling and analysis of neural spike trains.
Refreshments at 2:30PM in RT 1517