Electronic Journal of Statistics

Analysis of spike train data: Comparison between the real and the simulated data

Xiaosun Lu and J. S. Marron

Full-text: Open access

Abstract

This paper compares the real experimental data with the data simulated by Wu and Srivastava, 2011. It turns out that the real data exhibit a different composition of the phase and the amplitude variation from the simulated data, where these two types of variation are separated using the Fisher Rao curve registration. As a result, for the real data the original functions are a better choice of data objects for path discrimination, while for the simulated data the domain warping functions are better.

Article information

Source
Electron. J. Statist., Volume 8, Number 2 (2014), 1793-1796.

Dates
First available in Project Euclid: 29 October 2014

Permanent link to this document
https://projecteuclid.org/euclid.ejs/1414588164

Digital Object Identifier
doi:10.1214/14-EJS865D

Mathematical Reviews number (MathSciNet)
MR3273596

Zentralblatt MATH identifier
1305.62330

Keywords
Curve registration distance weighted discrimination functional data analysis

Citation

Lu, Xiaosun; Marron, J. S. Analysis of spike train data: Comparison between the real and the simulated data. Electron. J. Statist. 8 (2014), no. 2, 1793--1796. doi:10.1214/14-EJS865D. https://projecteuclid.org/euclid.ejs/1414588164


Export citation

References

  • Lu, X. (2013). Object oriented data analysis of cell images and analysis of elastic functions., Ph.D. Dissertation, University of North Carolina, Chapel Hill, NC, USA.
  • Lu, X. and Marron, J. S. (2013). Principal nested spheres for time warped functional data analysis., arXiv:1304.6789.
  • Marron, J. S., Todd, M. J., and Ahn, J. (2007). Distance weighted discrimination., Journal of the ASA, 102(480):1267–1271.
  • Srivastava, A., Wu, W., Kurtek, S., Klassen, E., and Marron, J. S. (2011). Statistical analysis and modeling of elastic functions., arXiv:1103.3817.
  • Wang, H. and Marron, J. S. (2007). Object oriented data analysis: Sets of trees., The Annals of Statistics, 35(5):1849–1873.
  • Wei, S., Lee, C., Wichers, L., Li, G., and Marron, J. S. (2013). Direction-projection-permutation for high dimensional hypothesis tests., arXiv:1304.0796.
  • Wu, W., Hatsopoulos, N. G., 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. (2011). An information-geometric framework for statistical inferences in the neural spike train space., Journal of Computational Neuroscience, 31(3):725–748.

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.