direkt zum Inhalt springen

direkt zum Hauptnavigationsmenü

Sie sind hier

TU Berlin

Inhalt des Dokuments

Search in All Publications

Search for publications




All Publications by GRK Members

Modeling Short-Term Noise Dependence of Spike Counts in Macaque Prefrontal Cortex
Citation key onken2008a
Author Onken, A., Grünewälder, S., Munk, M., and Obermayer, K.
Pages 1233–1240
Year 2008
Journal Advances in Neural Information Processing Systems
Volume 21
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 inappropriate, especially for low spike counts. In this study, we present copulas as an alternative approach. With copulas it is possible to 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 the disposal and can be used to analyze higher order interactions. We develop a framework to analyze spike count data by means of copulas. 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.
Link to original publication Download Bibtex entry

To top

Import Publication

Upload BibTeX

Export all entries to BibTex

Zusatzinformationen / Extras

Quick Access:

Schnellnavigation zur Seite über Nummerneingabe

Auxiliary Functions