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

被引：0
|
作者：
Hu, Meng [1 ]
Li, Mingyao [2 ]
Li, Wu [3 ,4 ]
Liang, Hualou [1 ]
机构：
[1] Drexel Univ, Sch Biomed Engn, Philadelphia, PA 19104 USA
[2] Univ Penn, Sch Med, Dept Biostat & Epidemiol, Philadelphia, PA 19104 USA
[3] Beijing Normal Univ, State Key Lab Cognit Neurosci & Learning, Beijing 100875, Peoples R China
[4] Beijing Normal Univ, McGovem Inst Brain Res, IDG, Beijing 100875, Peoples R China
来源：
DATA IN BRIEF | 2016年 / 7卷
基金：
中国国家自然科学基金;
关键词：
D O I：
10.1016/j.dib.2016.04.020
中图分类号：
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号：
07 ; 0710 ; 09 ;
摘要：
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. (C) 2016 The Authors. Published by Elsevier Inc.
引用
收藏
页码：1364 / 1369
页数：6
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