title: Data on copula modeling of mixed discrete and continuous neural time series.
abstract: Copula is an important tool for modeling neural dependence. Recent work on copula has been expanded to jointly model mixed time series in neuroscience ("Hu et al., 2016, Joint Analysis of Spikes and Local Field Potentials using Copula" [1]). Here we present further data for joint analysis of spike and local field potential (LFP) with copula modeling. In particular, the details of different model orders and the influence of possible spike contamination in LFP data from the same and different electrode recordings are presented. To further facilitate the use of our copula model for the analysis of mixed data, we provide the Matlab codes, together with example data.
authors.affiliation: School of Biomedical Engineering, Drexel University, Philadelphia, PA 19104, United States.
authors.forename: Meng
authors.initials: M
authors.lastname: Hu
authors.affiliation: Department of Biostatistics and Epidemiology, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, United States.
authors.forename: Mingyao
authors.initials: M
authors.lastname: Li
authors.affiliation: State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China.
authors.forename: Wu
authors.initials: W
authors.lastname: Li
authors.affiliation: School of Biomedical Engineering, Drexel University, Philadelphia, PA 19104, United States.
authors.forename: Hualou
authors.initials: H
authors.lastname: Liang