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Modeling short-term noise dependence of spike counts in macaque prefrontal cortex (2009)
| Venue: | Advances in Neural Information Processing Systems 21 |
| Citations: | 2 - 1 self |
BibTeX
@INPROCEEDINGS{Onken09modelingshort-term,
author = {Arno Onken and Technische Universität Berlin and Matthias Munk and Klaus Obermayer},
title = {Modeling short-term noise dependence of spike counts in macaque prefrontal cortex},
booktitle = {Advances in Neural Information Processing Systems 21},
year = {2009},
pages = {1233--1240}
}
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Abstract
Correlations between spike counts are often used to analyze neural coding. The noise is typically assumed to be Gaussian. Yet, this assumption is often inappro-priate, especially for low spike counts. In this study, we present copulas as an alternative approach. With copulas it is possible to use arbitrary marginal distri-butions such as Poisson or negative binomial that are better suited for modeling noise distributions of spike counts. Furthermore, copulas place a wide range of dependence structures at the disposal and can be used to analyze higher order in-teractions. We develop a framework to analyze spike count data by means of cop-ulas. Methods for parameter inference based on maximum likelihood estimates and for computation of mutual information are provided. We apply the method to our data recorded from macaque prefrontal cortex. The data analysis leads to three findings: (1) copula-based distributions provide significantly better fits than discretized multivariate normal distributions; (2) negative binomial margins fit the data significantly better than Poisson margins; and (3) the dependence structure carries 12 % of the mutual information between stimuli and responses. 1
Keyphrases
spike count macaque prefrontal cortex short-term noise dependence mutual information dependence structure maximum likelihood estimate wide range copula place negative binomial margin parameter inference discretized multivariate normal distribution copula-based distribution low spike count data analysis noise distribution arbitrary marginal distri-butions poisson margin neural coding spike count data alternative approach order in-teractions negative binomial
