Montalto Alessandro, Faes Luca, Marinazzo Daniele
Data Analysis Department, Ghent University, Ghent, Belgium.
BIOtech, Department of Industrial Engineering, University of Trento, and IRCS-PAT FBK, Trento, Italy.
PLoS One. 2014 Oct 14;9(10):e109462. doi: 10.1371/journal.pone.0109462. eCollection 2014.
A challenge for physiologists and neuroscientists is to map information transfer between components of the systems that they study at different scales, in order to derive important knowledge on structure and function from the analysis of the recorded dynamics. The components of physiological networks often interact in a nonlinear way and through mechanisms which are in general not completely known. It is then safer that the method of choice for analyzing these interactions does not rely on any model or assumption on the nature of the data and their interactions. Transfer entropy has emerged as a powerful tool to quantify directed dynamical interactions. In this paper we compare different approaches to evaluate transfer entropy, some of them already proposed, some novel, and present their implementation in a freeware MATLAB toolbox. Applications to simulated and real data are presented.
生理学家和神经科学家面临的一个挑战是,绘制他们在不同尺度上研究的系统各组成部分之间的信息传递图谱,以便通过对记录的动力学进行分析,获得有关结构和功能的重要知识。生理网络的组成部分通常以非线性方式相互作用,且其作用机制一般并不完全清楚。因此,用于分析这些相互作用的首选方法最好不依赖于关于数据性质及其相互作用的任何模型或假设。转移熵已成为量化定向动态相互作用的有力工具。在本文中,我们比较了评估转移熵的不同方法,其中一些方法已经有人提出,一些是新颖的,并展示了它们在一个免费的MATLAB工具箱中的实现。文中还介绍了这些方法在模拟数据和实际数据中的应用。