[Comp-neuro] Multiscale analysis of neural spike trains

Reza Ramezan rramezan at uwaterloo.ca
Thu Jul 10 00:08:20 CEST 2014

Dear Colleagues,

I would like to highlight the following paper from our group published 
in Statistics in Medicine (not a mainstream neuroscience journal). 
Interested people can receive a free (electronic) copy upon request.

Kindest Regards,
Reza Ramezan


*Multiscale analysis of neural spike trains* 
Ramezan R, Marriott P, Chenouri S (2014)

This paper studies the multiscale analysis of neural spike trains, 
through both graphical and Poisson process approaches. We introduce the 
interspike interval plot, which simultaneously visualizes 
characteristics of neural spiking activity at different time scales. 
Using an inhomogeneous Poisson process framework, we discuss multiscale 
estimates of the intensity functions of spike trains. We also introduce 
the windowing effect for two multiscale methods. Using quasi-likelihood, 
we develop bootstrap confidence intervals for the multiscale intensity 
function. We provide a cross-validation scheme, to choose the tuning 
parameters, and study its unbiasedness. Studying the relationship 
between the spike rate and the stimulus signal, we observe that 
adjusting for the first spike latency is important in cross-validation. 
We show, through examples, that the correlation between spike trains and 
spike count variability can be multiscale phenomena. Furthermore, we 
address the modeling of the periodicity of the spike trains caused by a 
stimulus signal or by brain rhythms. Within the multiscale framework, we 
introduce intensity functions for spike trains with multiplicative and 
additive periodic components. Analyzing a dataset from the 
retinogeniculate synapse, we compare the fit of these models with the 
Bayesian adaptive regression splines method and discuss the limitations 
of the methodology. Computational efficiency, which is usually a 
challenge in the analysis of spike trains, is one of the highlights of 
these new models. In an example, we show that the reconstruction quality 
of a complex intensity function demonstrates the ability of the 
multiscale methodology to crack the neural code.

*inhomogeneous Poisson process; multiscale analysis; periodogram; almost 
periodic intensity function; retinogeniculate synapse; spike train


Reza Ramezan
Department of Statistics and Actuarial Science
University of Waterloo
Waterloo, Ontario N2L 3G1
Office: M3 3018
Phone: (519) 888-4567, ext. 39358
E-mail: rramezan at uwaterloo.ca
Web: http://www.neuroinformatics.ca

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