Journal of Computational Neuroscience

, Volume 36, Issue 2, pp 119–140

Synergy, redundancy, and multivariate information measures: an experimentalist’s perspective


    • Department of PhysicsIndiana University,
  • Wesley Alford
    • Department of PhysicsIndiana University,
  • Benjamin Flecker
    • Department of PhysicsIndiana University,
  • John M. Beggs
    • Department of PhysicsIndiana University,

DOI: 10.1007/s10827-013-0458-4

Cite this article as:
Timme, N., Alford, W., Flecker, B. et al. J Comput Neurosci (2014) 36: 119. doi:10.1007/s10827-013-0458-4


Information theory has long been used to quantify interactions between two variables. With the rise of complex systems research, multivariate information measures have been increasingly used to investigate interactions between groups of three or more variables, often with an emphasis on so called synergistic and redundant interactions. While bivariate information measures are commonly agreed upon, the multivariate information measures in use today have been developed by many different groups, and differ in subtle, yet significant ways. Here, we will review these multivariate information measures with special emphasis paid to their relationship to synergy and redundancy, as well as examine the differences between these measures by applying them to several simple model systems. In addition to these systems, we will illustrate the usefulness of the information measures by analyzing neural spiking data from a dissociated culture through early stages of its development. Our aim is that this work will aid other researchers as they seek the best multivariate information measure for their specific research goals and system. Finally, we have made software available online which allows the user to calculate all of the information measures discussedwithin this paper.


Information theoryMultivariate information measuresComplex systemsNeural codingDissociated neuronal culturesMultielectrode array

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© Springer Science+Business Media New York 2013