Electronic Journal of Statistics

Introduction to neural spike train data for phase-amplitude analysis

Wei Wu, Nicholas G. Hatsopoulos, and Anuj Srivastava

Full-text: Open access

Abstract

Statistical analysis of spike trains is one of the central problems in neural coding, and can be pursued in several ways. One option is model-based, i.e. assume a parametric or semi-parametric model, such as the Poisson model, for spike train data and use it in decoding spike trains. The other option is metric-based, i.e. choose a metric for comparing the numbers and the placements of spikes in different trains, and does not need a model. A prominent idea in the latter approach is to derive metrics that are based on measurements of time-warpings of spike trains needed in the alignments of corresponding spikes. We propose the use of ideas developed in functional data analysis, namely the definition and separation of phase-amplitude components, as a novel tool for analyzing spike trains and decoding underlying neural signals. For concreteness, we introduce a real spike train dataset taken from experimental recordings of the primary motor cortex of a monkey while performing certain arm movements. To facilitate functional data analysis, one needs to smooth the observed discrete spike trains with Gaussian kernels.

Article information

Source
Electron. J. Statist., Volume 8, Number 2 (2014), 1759-1768.

Dates
First available in Project Euclid: 29 October 2014

Permanent link to this document
https://projecteuclid.org/euclid.ejs/1414588160

Digital Object Identifier
doi:10.1214/14-EJS865

Mathematical Reviews number (MathSciNet)
MR3273592

Zentralblatt MATH identifier
1305.62332

Keywords
Neuroscience spike train neural decoding motor cortex spike train metrics spike train alignment

Citation

Wu, Wei; Hatsopoulos, Nicholas G.; Srivastava, Anuj. Introduction to neural spike train data for phase-amplitude analysis. Electron. J. Statist. 8 (2014), no. 2, 1759--1768. doi:10.1214/14-EJS865. https://projecteuclid.org/euclid.ejs/1414588160


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See also

  • Related item: Patriarca, M., Sangalli, L. M., Secchi, P., and Vantini, S. (2014). Analysis of spike train data: An application of $-mean alignment. Electron. J. Statist. 8 1769–1775.
  • Related item: Wu, W. and Srivastava, A. (2014). Analysis of spike train data: Alignment and comparisons using the extended Fisher-Rao metric. Electron. J. Statist. 8 1776–1785.
  • Related item: Cheng, W., Dryden, I. L., Hitchcock, D. B., and Le, H. (2014). Analysis of spike train data: Classification and Bayesian alignment. Electron. J. Statist. 8 1786–1792.
  • Related item: Lu, X. and Marron, J. S. (2014). Analysis of spike train data: Comparison between the real and the simulated data. Electron. J. Statist. 8 1793–1796.
  • Related item: Hadjipantelis, P. Z., Aston, J. A. D., Müller, H.-G., and Moriarty, J. (2014). Analysis of spike train data: A multivariate mixed effects model for phase and amplitude. Electron. J. Statist. 8 1797–1807.
  • Related item: Wu, W., Hatsopoulos, N. G. and Srivastava, A. (2014). Analysis of spike train data: Discussion of results. Electron. J. Statist. 8 1808–1810.