A Novel Perturbation Based Compression Complexity Measure for Networks

Mohit, Virmani and Nagaraj, Nithin (2019) A Novel Perturbation Based Compression Complexity Measure for Networks. Heliyon 5 - e01181, 5 (2).

[img] Text
2019-Heliyon-Nithin-Nagaraj.pdf - Published Version
Restricted to Registered users only

Download (1MB)
ContributionNameEmail
Abstract: Measuring complexity of brain networks in the form of integrated information is a leading approach towards building a fundamental theory of consciousness. Integrated Information Theory (IIT) has gained attention in this regard due to its theoretically strong framework. Nevertheless, it faces some limitations such as current state dependence, computational intractability and inability to be applied to real brain data. On the other hand, Perturbational Complexity Index (PCI) is a clinical measure for distinguishing different levels of consciousness. Though PCI claims to capture the functional differentiation and integration in brain networks (similar to IIT), its link to integrated information is rather weak. Inspired by these two perspectives, we propose a new complexity measure for brain networks – ΦCΦC using a novel perturbation based compression-complexity approach that serves as a bridge between the two, for the first time. ΦCΦC is founded on the principles of lossless data compression based complexity measures which is computed by a perturbational approach. ΦCΦC exhibits following salient innovations: (i) mathematically well bounded, (ii) negligible current state dependence unlike Φ, (iii) network complexity measured as compression-complexity rather than as an infotheoretic quantity, and (iv) lower computational complexity since number of atomic bipartitions scales linearly with the number of nodes of the network, thus avoiding combinatorial explosion. Our computations have revealed that ΦCΦC has similar hierarchy to <Φ> for several multiple-node networks and it demonstrates a rich interplay between differentiation, integration and entropy of the nodes of a network. ΦCΦC is a promising heuristic measure to characterize network complexity (and hence might be useful in contributing to building a measure of consciousness) with potential applications in estimating brain complexity on neurophysiological data.
Item Type: Article
Additional Information: Copyright belongs to the Publisher
Keywords: Mathematical biosciences, Neuroscience
Subjects: School of Humanities > Consciousness Studies
Divisions: Schools > Humanities
Date Deposited: 13 Mar 2019 11:53
Last Modified: 13 Mar 2019 11:53
Official URL: https://www.heliyon.com/article/e01181/
Related URLs:
    Funders: UNSPECIFIED
    Projects: UNSPECIFIED
    URI: http://eprints.nias.res.in/id/eprint/1756

    Actions (login required)

    View Item View Item