Amblard Pierre-Olivier, Michel Olivier J J
GIPSAlab, Department of Images and Signals, CNRS UMR 5216, BP46, 38402, Saint Martin d'Hères Cedex, France.
J Comput Neurosci. 2011 Feb;30(1):7-16. doi: 10.1007/s10827-010-0231-x. Epub 2010 Mar 24.
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.
有向信息理论处理带有反馈的通信信道。当应用于网络时，需要基于因果条件进行自然扩展。我们在此表明，从网络中的有向信息理论构建的度量可用于评估随机过程的格兰杰因果关系图。我们表明，有向信息理论包括转移熵等度量，并且它是神经科学应用（如连通性推断问题）所需的适当信息理论框架。
