Transfer Entropy in Neuroscience

  • Michael WibralEmail author
  • Raul Vicente
  • Michael Lindner
Part of the Understanding Complex Systems book series (UCS)


Information transfer is a key component of information processing, next to information storage and modification. Information transfer can be measured by a variety of directed informationmeasures of which transfer entropy is themost popular, andmost principled one. This chapter presents the basic concepts behind transfer entropy in an intuitive fashion, including graphical depictions of the key concepts. It also includes a special section devoted to the correct interpretation of the measure, especially with respect to concepts of causality. The chapter also provides an overview of estimation techniques for transfer entropy and pointers to popular open source toolboxes. It also introduces recent extensions of transfer entropy that serve to estimate delays involved in information transfer in a network. By touching upon alternative measures of information transfer, such as Massey’s directed information transfer and Runge’s momentary information transfer, it may serve as a frame of reference for more specialised treatments and as an overview over the field of studies in information transfer in general.


Mutual Information Information Transfer Granger Causality Surrogate Data Target Process 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.MEG Unit, Brain Imaging CenterGoethe UniversityFrankfurt am MainGermany
  2. 2.Max-Planck Institute for Brain ResearchFrankfurt am MainGermany
  3. 3.School of Psychology and Clinical Language ScienceUniversity of ReadingReadingUK

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