On directed information theory and Granger causality graphs

被引：93

作者：

Amblard, Pierre-Olivier ^{[1
]}

Michel, Olivier J. J. ^{[1
]}

机构：

[1] CNRS, UMR 5216, Dept Images & Signals, GIPSAlab, F-38402 St Martin Dheres, France

来源：

JOURNAL OF COMPUTATIONAL NEUROSCIENCE | 2011年 / 30卷 / 01期

关键词：

Directed information theory; Feedback; Granger causality; Networks; Transfer entropy; Instantaneous information exchange; TIME-SERIES ANALYSIS; MULTIDIMENSIONAL DENSITIES; MUTUAL INFORMATION; NEURAL SYSTEMS; MODELS; CONNECTIVITY; DEPENDENCE; DISTORTION; NETWORKS; FEEDBACK;

D O I：

10.1007/s10827-010-0231-x

中图分类号：

Q [生物科学];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

Directed information theory deals with communication channels with feedback. When applied to networks, a natural extension based on causal conditioning is needed. We show here that measures built from directed information theory in networks can be used to assess Granger causality graphs of stochastic processes. We show that directed information theory includes measures such as the transfer entropy, and that it is the adequate information theoretic framework needed for neuroscience applications, such as connectivity inference problems.

引用

页码：7 / 16

页数：10

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