Characterizing neural dependencies with copula models

Pietro Berkes, Frank Wood, Jonathan Pillow

Research output: Chapter in Book/Report/Conference proceedingConference contribution

29 Scopus citations

Abstract

The coding of information by neural populations depends critically on the statistical dependencies between neuronal responses. However, there is no simple model that can simultaneously account for (1) marginal distributions over single-neuron spike counts that are discrete and non-negative; and (2) joint distributions over the responses of multiple neurons that are often strongly dependent. Here, we show that both marginal and joint properties of neural responses can be captured using copula models. Copulas are joint distributions that allow random variables with arbitrary marginals to be combined while incorporating arbitrary dependencies between them. Different copulas capture different kinds of dependencies, allowing for a richer and more detailed description of dependencies than traditional summary statistics, such as correlation coefficients. We explore a variety of copula models for joint neural response distributions, and derive an efficient maximum likelihood procedure for estimating them. We apply these models to neuronal data collected in macaque pre-motor cortex, and quantify the improvement in coding accuracy afforded by incorporating the dependency structure between pairs of neurons. We find that more than one third of neuron pairs shows dependency concentrated in the lower or upper tails for their firing rate distribution.

Original languageEnglish (US)
Title of host publicationAdvances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference
Pages129-136
Number of pages8
StatePublished - Dec 1 2009
Externally publishedYes
Event22nd Annual Conference on Neural Information Processing Systems, NIPS 2008 - Vancouver, BC, Canada
Duration: Dec 8 2008Dec 11 2008

Publication series

NameAdvances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference

Other

Other22nd Annual Conference on Neural Information Processing Systems, NIPS 2008
CountryCanada
CityVancouver, BC
Period12/8/0812/11/08

All Science Journal Classification (ASJC) codes

  • Information Systems

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  • Cite this

    Berkes, P., Wood, F., & Pillow, J. (2009). Characterizing neural dependencies with copula models. In Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference (pp. 129-136). (Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference).