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

Meng Hu; Mingyao Li; Wu Li; Hualou Liang

doi:10.1016/j.dib.2016.04.020

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

Meng Hu
, Mingyao Li
, Wu Li
, Hualou Liang (Corresponding Author)

Research output: Journal article publication › Journal article › Academic research › peer-review

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.

Original language	English
Pages (from-to)	1364-1369
Number of pages	6
Journal	Data in Brief
Volume	7
DOIs	https://doi.org/10.1016/j.dib.2016.04.020
Publication status	Published - 13 Apr 2016
Externally published	Yes

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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.",

author = "Meng Hu and Mingyao Li and Wu Li and Hualou Liang",

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AU - Liang, Hualou

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N2 - 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.

AB - 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.

UR - https://www.scopus.com/pages/publications/84963930450

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DO - 10.1016/j.dib.2016.04.020

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JO - Data in Brief

JF - Data in Brief

ER -

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

Abstract

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