Abstract
In this paper we summarize and comment on different techniques presented for analyzing spike train data.
Highlights
More general nonlinear warpings are not allowed in this setup
Wu and Srivastava [5] describe a metric-based framework for alignment of signals using nonlinear warpings, and apply it to spike train data in two ways. (This metric is presented as an extension of the Fisher-Rao metric from the space of probability densities to larger function spaces.) First, they obtain phaseamplitude separation within each class and display the differences in components across classes
The authors use functional principal component analysis (FPCA) and display top two coefficients for different classes. They show that the four classes are well separated in both the phase and amplitude space
Summary
More general nonlinear warpings are not allowed in this setup. The criterion for pairwise alignment is the coefficient of correlation between the given functions. They provide a novel perspective on the problem by considering the data
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