Open Access
2014 Analysis of spike train data: An application of $k$-mean alignment
Mirco Patriarca, Laura M. Sangalli, Piercesare Secchi, Simone Vantini
Electron. J. Statist. 8(2): 1769-1775 (2014). DOI: 10.1214/14-EJS865A


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.


Download Citation

Mirco Patriarca. Laura M. Sangalli. Piercesare Secchi. Simone Vantini. "Analysis of spike train data: An application of $k$-mean alignment." Electron. J. Statist. 8 (2) 1769 - 1775, 2014.


Published: 2014
First available in Project Euclid: 29 October 2014

zbMATH: 1305.62331
MathSciNet: MR3273593
Digital Object Identifier: 10.1214/14-EJS865A

Keywords: $k$-mean alignment , functional clustering , registration , spike trains

Rights: Copyright © 2014 The Institute of Mathematical Statistics and the Bernoulli Society

Vol.8 • No. 2 • 2014
Back to Top