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. 2010 Aug;29(1-2):149-169.
doi: 10.1007/s10827-009-0157-3. Epub 2009 May 16.

Feature extraction from spike trains with Bayesian binning: 'latency is where the signal starts'

Dominik Endres  1 , Mike Oram  2
Affiliations

Feature extraction from spike trains with Bayesian binning: 'latency is where the signal starts'

Dominik Endres et al. J Comput Neurosci. 2010 Aug.

Abstract

The peristimulus time histogram (PSTH) and its more continuous cousin, the spike density function (SDF) are staples in the analytic toolkit of neurophysiologists. The former is usually obtained by binning spike trains, whereas the standard method for the latter is smoothing with a Gaussian kernel. Selection of a bin width or a kernel size is often done in an relatively arbitrary fashion, even though there have been recent attempts to remedy this situation (DiMatteo et al., Biometrika 88(4):1055-1071, 2001; Shimazaki and Shinomoto 2007a, Neural Comput 19(6):1503-1527, 2007b, c; Cunningham et al. 2008). We develop an exact Bayesian, generative model approach to estimating PSTHs. Advantages of our scheme include automatic complexity control and error bars on its predictions. We show how to perform feature extraction on spike trains in a principled way, exemplified through latency and firing rate posterior distribution evaluations on repeated and single trial data. We also demonstrate using both simulated and real neuronal data that our approach provides a more accurate estimates of the PSTH and the latency than current competing methods. We employ the posterior distributions for an information theoretic analysis of the neural code comprised of latency and firing rate of neurons in high-level visual area STSa. A software implementation of our method is available at the machine learning open source software repository ( www.mloss.org , project 'binsdfc').

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References

    1. J Neurosci. 2005 Aug 24;25(34):7876-86 - PubMed
    1. Proc Natl Acad Sci U S A. 2007 Jan 2;104(1):347-52 - PubMed
    1. Biol Cybern. 2003 Nov;89(5):341-9 - PubMed
    1. Stat Med. 2007 Sep 20;26(21):4032-8 - PubMed
    1. Vis Neurosci. 2002 May-Jun;19(3):381-8 - PubMed

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