A novel perturbation based compression complexity measure for networks
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 li...
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| Format: | Article |
| Language: | English |
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Elsevier
2019-02-01
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| Series: | Heliyon |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844017338318 |
| _version_ | 1818499862959751168 |
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| author | Mohit Virmani Nithin Nagaraj |
| author_facet | Mohit Virmani Nithin Nagaraj |
| author_sort | Mohit Virmani |
| collection | DOAJ |
| description | 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 – ΦC using a novel perturbation based compression-complexity approach that serves as a bridge between the two, for the first time. ΦC is founded on the principles of lossless data compression based complexity measures which is computed by a perturbational approach. ΦC 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 ΦC 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.ΦC 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. |
| first_indexed | 2024-12-10T20:35:23Z |
| format | Article |
| id | doaj.art-5e88456d63604e90a160a4ee51517096 |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-12-10T20:35:23Z |
| publishDate | 2019-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-5e88456d63604e90a160a4ee515170962022-12-22T01:34:33ZengElsevierHeliyon2405-84402019-02-0152e01181A novel perturbation based compression complexity measure for networksMohit Virmani0Nithin Nagaraj1Corresponding author.; Consciousness Studies Programme, National Institute of Advanced Studies, IISc Campus, Bengaluru, Karnataka, IndiaConsciousness Studies Programme, National Institute of Advanced Studies, IISc Campus, Bengaluru, Karnataka, IndiaMeasuring 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 – ΦC using a novel perturbation based compression-complexity approach that serves as a bridge between the two, for the first time. ΦC is founded on the principles of lossless data compression based complexity measures which is computed by a perturbational approach. ΦC 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 ΦC 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.ΦC 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.http://www.sciencedirect.com/science/article/pii/S2405844017338318Mathematical biosciencesNeuroscience |
| spellingShingle | Mohit Virmani Nithin Nagaraj A novel perturbation based compression complexity measure for networks Heliyon Mathematical biosciences Neuroscience |
| title | A novel perturbation based compression complexity measure for networks |
| title_full | A novel perturbation based compression complexity measure for networks |
| title_fullStr | A novel perturbation based compression complexity measure for networks |
| title_full_unstemmed | A novel perturbation based compression complexity measure for networks |
| title_short | A novel perturbation based compression complexity measure for networks |
| title_sort | novel perturbation based compression complexity measure for networks |
| topic | Mathematical biosciences Neuroscience |
| url | http://www.sciencedirect.com/science/article/pii/S2405844017338318 |
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