Date: 2014-02-21

Time: 15:30-16:30

Location: BURN 1205

Abstract:

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, the limitations of the Poisson model are highlighted , and the Skellam Process with Resetting (SPR) is introduced as an alternative model for the analysis of neural spike trains. SPR is biologically justified, and the parameter estimation algorithm developed for it is computationally efficient. To allow for the modelling of neural ensembles, this process is generalized to the multivariate case, where Multivariate Skellam Process with Resetting (MSPR), as well as the multivariate Skellam distribution are introduced. Simulation and real data studies confirm the promising results of the Skellam model in the statistical analysis of neural spike trains.

Speaker

Reza Ramezan is a fresh PhD graduate from the University of Waterloo.