Analysis of Spike Train Data: an Application of K-mean Alignment
Keywords
Statistical learning
Code:
43/2013
Title:
Analysis of Spike Train Data: an Application of K-mean Alignment
Date:
Sunday 6th October 2013
Author(s):
Patriarca, M.; Sangalli, L.M.; Secchi, P.; Vantini, S.
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Abstract:
We analyze the spike train data by means of the k-mean alignment algorithm in a double perspective: data as non periodic and data as periodic. In the first analysis, we show that alignment is not needed to identify paths. Indeed, without allowing for warping, we
detect four clusters strongly associated to the four possible paths. In the second analysis, by exploiting the circular nature of data and allowing for shifts, we detect two clusters distinguishing between spike trains presenting higher or lower neuronal activity during the bottom-left/bottom-right movement respectively. In this latter case, the alignment procedure is able to match the four movements across paths.
This report, or a modified version of it, has been also submitted to, or published on
Mirco Patriarca, Laura M. Sangalli, Piercesare Secchi, Simone Vantini (2014), Analysis of Spike Train Data: an Application of K-mean Alignment, Electronic Journal of Statistics, Special Section on "Statistics of Time Warpings and Phase Variations", Vol. 8, No. 2, 1769-1775.
Mirco Patriarca, Laura M. Sangalli, Piercesare Secchi, Simone Vantini (2014), Analysis of Spike Train Data: an Application of K-mean Alignment, Electronic Journal of Statistics, Special Section on "Statistics of Time Warpings and Phase Variations", Vol. 8, No. 2, 1769-1775.