Electronic Journal of Statistics

Introduction to neural spike train data for phase-amplitude analysis

Wei Wu, Nicholas G. Hatsopoulos, and Anuj Srivastava

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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

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

First available in Project Euclid: 29 October 2014

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


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

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  • 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.
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