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Journal article
Data on copula modeling of mixed discrete and continuous neural time series
Data in brief, v 7, pp 1364-1369
Jun 2016
PMCID: PMC4845150
PMID: 27158651
Featured in Collection : UN Sustainable Development Goals @ Drexel
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.
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Details
- Title
- Data on copula modeling of mixed discrete and continuous neural time series
- Creators
- Meng Hu - Drexel UniversityMingyao Li - University of PennsylvaniaWu Li - Beijing Normal UniversityHualou Liang - Drexel University
- Publication Details
- Data in brief, v 7, pp 1364-1369
- Publisher
- Elsevier
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- School of Biomedical Engineering, Science, and Health Systems
- Web of Science ID
- WOS:000453166200209
- Scopus ID
- 2-s2.0-84963930450
- Other Identifier
- 991019169534204721
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Neurosciences