Mixed vine copulas as joint models of spike counts and local field potentials

Arno Onken, Stefano Panzeri

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

Abstract / Description of output

Concurrent measurements of neural activity at multiple scales, sometimes performed with multimodal techniques, become increasingly important for studying brain function. However, statistical methods for their concurrent analysis are currently lacking. Here we introduce such techniques in a framework based on vine copulas with mixed margins to construct multivariate stochastic models. These models can describe detailed mixed interactions between discrete variables such as neural spike counts, and continuous variables such as local field potentials. We propose efficient methods for likelihood calculation, inference, sampling and mutual information estimation within this framework. We test our methods on simulated data and demonstrate applicability on mixed data generated by a biologically realistic neural network. Our methods hold the promise to considerably improve statistical analysis of neural data recorded simultaneously at different scales.
Original languageEnglish
Title of host publicationAdvances in Neural Information Processing Systems 29
EditorsD Lee, M Sugiyama, U Luxburg, I Guyon, R Garnett
Place of PublicationBarcelona, Spain
PublisherNeural Information Processing Systems
Number of pages9
Publication statusPublished - 5 Dec 2016
Event30th Annual Conference on Neural Information Processing Systems - Barcelona, Spain
Duration: 5 Dec 201610 Dec 2016

Publication series

ISSN (Electronic)1049-5258


Conference30th Annual Conference on Neural Information Processing Systems
Abbreviated titleNIPS 2016
Internet address


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