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An evaluation of different copula models for the short-term noise dependence of spike counts

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Munk,  M
Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Onken, A., Grünewälder, S., Munk, M., & Obermayer, K. (2009). An evaluation of different copula models for the short-term noise dependence of spike counts. Poster presented at 8th Göttingen Meeting of the German Neuroscience Society, 32nd Göttingen Neurobiology Conference, Göttingen, Germany.


Cite as: https://hdl.handle.net/21.11116/0000-0003-17E2-4
Abstract
Correlations between spike counts are often used to analyze neural coding. Traditionally, multivariate
Gaussian distributions are frequently used to model the correlation structure of these spike-counts [1].
However, this approximation is not realistic for short time intervals.
In this study, as an alternative approach we introduce dependencies by means of copulas of several
families. Copulas are functions that can be used to couple marginal cumulative distribution functions to
form a joint distribution function with the same margins [2]. We can thus use arbitrary marginal
distributions 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 our disposal and can be
used to analyze higher order interactions.
We develop a framework to analyze spike count data by means of such copulas. Methods for parameter
inference based on maximum likelihood estimates and for computation of Shannon entropy are provided.
The methods are evaluated on a data set of simultaneously measured spike-counts on 100 ms intervals of
up to three neurons in macaque MT responding to stochastic dot stimuli [3] and of up to six neurons
recorded from macaque prefrontal cortex. Parameters are estimated by the inference-for margins method:
first the margin likelihoods are separately maximized and then the coupling parameters are estimated given
the parameterized margins. Resulting parameters are close to the maximum likelihood estimation with the
advantage that the approach is also tractable for moderate dimensions. Goodness-of-fit is evaluated by
cross-validation for the likelihoods.
The data analysis leads to three significant findings: (1) copula-based distributions provide better fits than
discretized multivariate normal distributions; (2) negative binomial margins fit the data better than Poisson
margins; and (3) a dependence model that includes only pairwise interactions overestimates the information
entropy by at least 19% compared to the model with higher order interactions.