A Temporal Estimate of Integrated Information for Intracranial Functional Connectivity View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2018-09-26

AUTHORS

Xerxes D. Arsiwalla , Daniel Pacheco , Alessandro Principe , Rodrigo Rocamora , Paul Verschure

ABSTRACT

A major challenge in computational and systems neuroscience concerns the quantification of information processing at various scales of the brain’s anatomy. In particular, using human intracranial recordings, the question we ask in this paper is: How can we estimate the informational complexity of the brain given the complex temporal nature of its dynamics? To address this we work with a recent formulation of network integrated information that 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. In this work, we extend this formulation for temporal networks and then apply it to human brain data obtained from intracranial recordings in epilepsy patients. Our findings show that compared to random re-wirings of the data, functional connectivity networks, constructed from human brain data, score consistently higher in the above measure of integrated information. This work suggests that temporal integrated information may indeed be a good starting point as a future measure of cognitive complexity. More... »

PAGES

403-412

References to SciGraph publications

  • 2016-12. The global dynamical complexity of the human brain network in APPLIED NETWORK SCIENCE
  • 2016. On Three Categories of Conscious Machines in BIOMIMETIC AND BIOHYBRID SYSTEMS
  • 2017. Why the Brain Might Operate Near the Edge of Criticality in ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING – ICANN 2017
  • 2016. Computing Information Integration in Brain Networks in ADVANCES IN NETWORK SCIENCE
  • 2016. High Integrated Information in Complex Networks Near Criticality in ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING – ICANN 2016
  • 2004-12. An information integration theory of consciousness in BMC NEUROSCIENCE
  • 2003-12. Measuring information integration in BMC NEUROSCIENCE
  • 2014. Quantifying Synergistic Mutual Information in GUIDED SELF-ORGANIZATION: INCEPTION
  • Book

    TITLE

    Artificial Neural Networks and Machine Learning – ICANN 2018

    ISBN

    978-3-030-01420-9
    978-3-030-01421-6

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-030-01421-6_39

    DOI

    http://dx.doi.org/10.1007/978-3-030-01421-6_39

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1107244552


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