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)
Summary:
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.
Keywords:
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
Canada
Office: M3 3018
Phone: (519) 888-4567, ext. 39358
E-mail: rramezan@uwaterloo.ca
Web: http://www.neuroinformatics.ca