Electronic Journal of Statistics

Analysis of spike train data: Classification and Bayesian alignment

Wen Cheng, Ian L. Dryden, David B. Hitchcock, and Huiling Le

Full-text: Open access

Abstract

We analyze a data set of spike trains obtained under four different experimental conditions. We model the data curves via mixtures of normal densities. The peak locations in the fitted curves are modeled via a non-homogeneous Poisson process and classification of the spike trains into groups may be done based on the estimated spacings between peaks. We employ a Bayesian, MCMC-based registration method to align the fitted curves and summarize the data using meaningful functional statistics and posterior intervals.

Article information

Source
Electron. J. Statist., Volume 8, Number 2 (2014), 1786-1792.

Dates
First available in Project Euclid: 29 October 2014

Permanent link to this document
https://projecteuclid.org/euclid.ejs/1414588163

Digital Object Identifier
doi:10.1214/14-EJS865C

Mathematical Reviews number (MathSciNet)
MR3273595

Zentralblatt MATH identifier
1305.62328

Keywords
Markov chain Monte Carlo Poisson process registration time warping

Citation

Cheng, Wen; Dryden, Ian L.; Hitchcock, David B.; Le, Huiling. Analysis of spike train data: Classification and Bayesian alignment. Electron. J. Statist. 8 (2014), no. 2, 1786--1792. doi:10.1214/14-EJS865C. https://projecteuclid.org/euclid.ejs/1414588163


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References

  • Baíllo, A., Cuevas, A., and Fraiman, R. (2011). Classification methods for functional data. In Ferraty, F. and Romain, Y., editors, The Oxford Handbook of Functional Data Analysis. Oxford University Press, Oxford.
  • Browne, W. J., Dryden, I. L., Handley, K., Mian, S., and Schadendorf, D. (2010). Mixed effect modelling of proteomic mass spectrometry data by using Gaussian mixtures., J. R. Stat. Soc. Ser. C. Appl. Stat., 59(4):617–633.
  • Cheng, W., Dryden, I. L., and Huang, X. (2013). Bayesian registration of functions and curves. Technical report, University of Nottingham., http://arxiv.org/abs/1311.2105.
  • Cheng, W., Dryden, I. L., Hitchcock, D. B., and Le, H. (2014). Analysis of proteomics data: Bayesian alignment of functions., Electronic Journal of Statistics 8 1734–1741, Special Section on Statistics of Time Warpings and Phase Variations.
  • Jones, M. C. and Henderson, D. A. (2007). Kernel-type density estimation on the unit interval., Biometrika, 94(4):977–984.
  • Srivastava, A., Klassen, E., Joshi, S. H., and Jermyn, I. H. (2011). Shape analysis of elastic curves in Euclidean spaces., IEEE Trans. Pattern Anal. Mach. Intell, 33(7):1415–1428.
  • 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, 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.