Data on copula modeling of mixed discrete and continuous neural time series

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 a...

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Detalles Bibliográficos
Autores principales: Hu, Meng, Li, Mingyao, Li, Wu, Liang, Hualou
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2016
Materias:
Data Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4845150/
https://www.ncbi.nlm.nih.gov/pubmed/27158651
http://dx.doi.org/10.1016/j.dib.2016.04.020
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author Hu, Meng
Li, Mingyao
Li, Wu
Liang, Hualou
author_facet Hu, Meng
Li, Mingyao
Li, Wu
Liang, Hualou
author_sort Hu, Meng
collection PubMed
description 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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spelling pubmed-48451502016-05-06 Data on copula modeling of mixed discrete and continuous neural time series Hu, Meng Li, Mingyao Li, Wu Liang, Hualou Data Brief Data Article 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. Elsevier 2016-04-13 /pmc/articles/PMC4845150/ /pubmed/27158651 http://dx.doi.org/10.1016/j.dib.2016.04.020 Text en © 2016 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Data Article
Hu, Meng
Li, Mingyao
Li, Wu
Liang, Hualou
Data on copula modeling of mixed discrete and continuous neural time series
title Data on copula modeling of mixed discrete and continuous neural time series
title_full Data on copula modeling of mixed discrete and continuous neural time series
title_fullStr Data on copula modeling of mixed discrete and continuous neural time series
title_full_unstemmed Data on copula modeling of mixed discrete and continuous neural time series
title_short Data on copula modeling of mixed discrete and continuous neural time series
title_sort data on copula modeling of mixed discrete and continuous neural time series
topic Data Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4845150/
https://www.ncbi.nlm.nih.gov/pubmed/27158651
http://dx.doi.org/10.1016/j.dib.2016.04.020
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