Electronic Journal of Statistics

Analysis of spike train data: An application of $k$-mean alignment

Mirco Patriarca, Laura M. Sangalli, Piercesare Secchi, and Simone Vantini

Full-text: Open access

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.

Article information

Source
Electron. J. Statist., Volume 8, Number 2 (2014), 1769-1775.

Dates
First available in Project Euclid: 29 October 2014

Permanent link to this document
https://projecteuclid.org/euclid.ejs/1414588161

Digital Object Identifier
doi:10.1214/14-EJS865A

Mathematical Reviews number (MathSciNet)
MR3273593

Zentralblatt MATH identifier
1305.62331

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

Citation

Patriarca, Mirco; Sangalli, Laura M.; Secchi, Piercesare; Vantini, Simone. Analysis of spike train data: An application of $k$-mean alignment. Electron. J. Statist. 8 (2014), no. 2, 1769--1775. doi:10.1214/14-EJS865A. https://projecteuclid.org/euclid.ejs/1414588161


Export citation

References

  • Lu, X. and Marron, J. S. (2014). Analysis of spike train data: Comparison between the real and the simulated data., Electronic Journal of Statistics 8 1793–1796, Special Section on Statistics of Time Warpings and Phase Variations.
  • Parodi, A., Patriarca, M., Sangalli, L., Secchi, P., Vantini, S. and Vitelli, V. (2014). fdakma: Functional data analysis: $k$-mean alignment, R package version, 1.1.1.
  • Sangalli, L. M., Secchi, P. and Vantini, S. (2014). Analysis of AneuRisk65 data: $k$-mean alignment., Electronic Journal of Statistics 8 1891–1904, Special Section on Statistics of Time Warpings and Phase Variations.
  • Sangalli, L. M., Secchi, P., Vantini, S. and Vitelli, V. (2010). $k$-mean alignment for curve clustering., Computational Statistics and Data Analysis 54 1219–1233.
  • Wu, W., Hatsopoulos, N. and Srivastava, A. (2014). Introduction to neural spike train data for phase-amplitude analysis., Electronic Journal of Statistics 8 1759–1768, Special Section on Statistics of Time Warpings and Phase Variations.
  • Wu, W. and Srivastava, A. (2014). Analysis of spike train data: Alignment and comparisons using the extended Fisher-Rao metric., Electronic Journal of Statistics 8 1776–1785, Special Section on Statistics of Time Warpings and Phase Variations.

See also

  • Related item: Wu, W., Hatsopoulos, N. G. and Srivastava, A. (2014). Introduction to neural spike train data for phase-amplitude analysis. Electron. J. Statist. 8 1759–1768.