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The integrative neuroscience of behavioral control (Neuroscience)
Abstract
We analyze by means of Granger causality the effect of synergy and redundancy in the inference (from time series data) of the information flow between subsystems of a complex network. Whilst fully conditioned Granger causality is not affected by synergy, the pairwise analysis fails to put in evidence synergetic effects. We show that maximization of the total Granger causality to a given target, over all the possible partitions of the set of driving variables, puts in evidence redundant multiplets of variables influencing the target, provided that an unnormalized definition of Granger causality is adopted. Along the same lines we also introduce a pairwise index of synergy (w.r.t. to information flow to a third variable) which is zero when two independent sources additively influence a common target; thus, this definion differs from previous definitions of synergy.

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Citation

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

Chicago
Stramaglia , Sebastiano , Leonardo Angelini , Jesus Cortes , and Daniele Marinazzo. 2015. “Synergy, Redundancy and Unnormalized Granger Causality.” In IEEE Engineering in Medicine and Biology Society Conference Proceedings, 4037–4040.
APA
Stramaglia , S., Angelini , L., Cortes , J., & Marinazzo, D. (2015). Synergy, redundancy and unnormalized Granger causality. IEEE Engineering in Medicine and Biology Society Conference Proceedings (pp. 4037–4040). Presented at the 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).
Vancouver
1.
Stramaglia S, Angelini L, Cortes J, Marinazzo D. Synergy, redundancy and unnormalized Granger causality. IEEE Engineering in Medicine and Biology Society Conference Proceedings. 2015. p. 4037–40.
MLA
Stramaglia , Sebastiano , Leonardo Angelini , Jesus Cortes , et al. “Synergy, Redundancy and Unnormalized Granger Causality.” IEEE Engineering in Medicine and Biology Society Conference Proceedings. 2015. 4037–4040. Print.
@inproceedings{8032586,
  abstract     = {We analyze by means of Granger causality the effect of synergy and redundancy in the inference (from time series data) of the information flow between subsystems of a complex network. Whilst fully conditioned Granger causality is not affected by synergy, the pairwise analysis fails to put in evidence synergetic effects. We show that maximization of the total Granger causality to a given target, over all the possible partitions of the set of driving variables, puts in evidence redundant multiplets of variables influencing the target, provided that an unnormalized definition of Granger causality is adopted. Along the same lines we also introduce a pairwise index of synergy (w.r.t. to information flow to a third variable) which is zero when two independent sources additively influence a common target; thus, this definion differs from previous definitions of synergy.},
  author       = {Stramaglia , Sebastiano  and Angelini , Leonardo  and Cortes , Jesus  and Marinazzo, Daniele},
  booktitle    = {IEEE Engineering in Medicine and Biology Society Conference Proceedings},
  isbn         = {978-1-4244-9270-1},
  issn         = {1557-170X},
  language     = {eng},
  location     = {Milan, Italy},
  pages        = {4037--4040},
  title        = {Synergy, redundancy and unnormalized Granger causality},
  year         = {2015},
}

Web of Science
Times cited: