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Neuronal Spike Train Analysis Using Gaussian Process Models

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Nonparametric Bayesian Inference in Biostatistics

Part of the book series: Frontiers in Probability and the Statistical Sciences ((FROPROSTAS))

Abstract

Statistical analysis of simultaneously recorded neurons plays an important role in understanding complex behaviors, decision making process, and neurophysiological disorders. Here, we briefly review several statistical methods specifically developed for analysis of neuronal spike trains. We then focus on application of Gaussian process models for estimating time-varying firing rates of neurons and show how this approach can be extended for modeling synchrony among multiple neurons. We finish this chapter by discussing some possible future directions where more advanced nonparametric Bayesian methods can be utilized to improve existing models.

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Notes

  1. 1.

    Their data model is somewhat different from (13.1), as the spike times are assumed to follow a conditionally inhomogeneous gamma-interval process instead of a Poisson process.

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Authors and Affiliations

  1. UC Irvine, CA, USA

    Babak Shahbaba & Alexander Vandenberg-Rodes

  2. CSUF, Fullerton, CA, USA

    Sam Behseta

Authors
  1. Babak Shahbaba
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  2. Sam Behseta
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Correspondence to Babak Shahbaba .

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Editors and Affiliations

  1. Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, Kentucky, USA

    Riten Mitra

  2. Department of Mathematics, University of Texas, Austin, Texas, USA

    Peter Müller

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Shahbaba, B., Behseta, S., Vandenberg-Rodes, A. (2015). Neuronal Spike Train Analysis Using Gaussian Process Models. In: Mitra, R., Müller, P. (eds) Nonparametric Bayesian Inference in Biostatistics. Frontiers in Probability and the Statistical Sciences. Springer, Cham. https://doi.org/10.1007/978-3-319-19518-6_13

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