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International Conference and School on Network Science

NetSci-X 2016: Advances in Network Science pp 136–146Cite as

Computing Information Integration in Brain Networks

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9564))

Abstract

How much information do large brain networks integrate as a whole over the sum of their parts? Can the complexity of such networks be quantified in an information-theoretic way and be meaningfully coupled to brain function? Recently, measures of dynamical complexity such as integrated information have been proposed. However, problems related to the normalization and Bell number of partitions associated to these measures make these approaches computationally infeasible for large-scale brain networks. Our goal in this work is to address this problem. Our formulation of network integrated information is based on the Kullback-Leibler divergence between the multivariate distribution on the set of network states versus the corresponding factorized distribution over its parts. We find that implementing the maximum information partition optimizes computations. These methods are well-suited for large networks with linear stochastic dynamics. As an application to brain networks, we compute the integrated information for the human brain’s connectomic data. Compared to a randomly re-wired network, we find that the specific topology of the brain generates greater information complexity.

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Notes

  1. 1.

    For the case of asymmetric weights, the entries of the covariance matrix cannot be explicitly expressed as a matrix equation. However, they may still be solved by Jordan decomposition of both sides of the Lyapunov equation.

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Acknowledgments

This work has been supported by the European Research Council’s CDAC project: “The Role of Consciousness in Adaptive Behavior: A Combined Empirical, Computational and Robot based Approach” (ERC-2013- ADG 341196).

Author information

Authors and Affiliations

  1. Synthetic Perceptive Emotive and Cognitive Systems (SPECS) Lab, Center of Autonomous Systems and Neurorobotics, Universitat Pompeu Fabra, Barcelona, Spain

    Xerxes D. Arsiwalla & Paul Verschure

  2. Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain

    Paul Verschure

Authors
  1. Xerxes D. Arsiwalla
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  2. Paul Verschure
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Corresponding author

Correspondence to Xerxes D. Arsiwalla .

Editor information

Editors and Affiliations

  1. Polish-Japanese Academy, Warzaw, Poland

    Adam Wierzbicki

  2. Universität Konstanz, Konstanz, Baden-Württemberg, Germany

    Ulrik Brandes

  3. ETH Zürich, Zürich, Switzerland

    Frank Schweitzer

  4. University of Pisa, Pisa, Italy

    Dino Pedreschi

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© 2016 Springer International Publishing Switzerland

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Arsiwalla, X.D., Verschure, P. (2016). Computing Information Integration in Brain Networks. In: Wierzbicki, A., Brandes, U., Schweitzer, F., Pedreschi, D. (eds) Advances in Network Science. NetSci-X 2016. Lecture Notes in Computer Science(), vol 9564. Springer, Cham. https://doi.org/10.1007/978-3-319-28361-6_11

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  • DOI: https://doi.org/10.1007/978-3-319-28361-6_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-28360-9

  • Online ISBN: 978-3-319-28361-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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