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MuTE: A MATLAB toolbox to compare established and novel estimators of the multivariate transfer entropy

(2014) PLOS ONE. 9(10).
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Abstract
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.
Keywords
CAUSALITY, INFORMATION-TRANSFER, MODEL

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Please use this url to cite or link to this publication:

MLA
Montalto, Alessandro, Luca Faes, and Daniele Marinazzo. “MuTE: A MATLAB Toolbox to Compare Established and Novel Estimators of the Multivariate Transfer Entropy.” PLOS ONE 9.10 (2014): n. pag. Print.
APA
Montalto, A., Faes, L., & Marinazzo, D. (2014). MuTE: A MATLAB toolbox to compare established and novel estimators of the multivariate transfer entropy. PLOS ONE, 9(10).
Chicago author-date
Montalto, Alessandro, Luca Faes, and Daniele Marinazzo. 2014. “MuTE: A MATLAB Toolbox to Compare Established and Novel Estimators of the Multivariate Transfer Entropy.” Plos One 9 (10).
Chicago author-date (all authors)
Montalto, Alessandro, Luca Faes, and Daniele Marinazzo. 2014. “MuTE: A MATLAB Toolbox to Compare Established and Novel Estimators of the Multivariate Transfer Entropy.” Plos One 9 (10).
Vancouver
1.
Montalto A, Faes L, Marinazzo D. MuTE: A MATLAB toolbox to compare established and novel estimators of the multivariate transfer entropy. PLOS ONE. 2014;9(10).
IEEE
[1]
A. Montalto, L. Faes, and D. Marinazzo, “MuTE: A MATLAB toolbox to compare established and novel estimators of the multivariate transfer entropy,” PLOS ONE, vol. 9, no. 10, 2014.
@article{5730682,
  abstract     = {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.},
  articleno    = {e109462},
  author       = {Montalto, Alessandro and Faes, Luca and Marinazzo, Daniele},
  issn         = {1932-6203},
  journal      = {PLOS ONE},
  keywords     = {CAUSALITY,INFORMATION-TRANSFER,MODEL},
  language     = {eng},
  number       = {10},
  title        = {MuTE: A MATLAB toolbox to compare established and novel estimators of the multivariate transfer entropy},
  url          = {http://dx.doi.org/10.1371/journal.pone.0109462},
  volume       = {9},
  year         = {2014},
}

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