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International Conference on Artificial Neural Networks

ICANN 2017: Artificial Neural Networks and Machine Learning – ICANN 2017 pp 326–333Cite as

Why the Brain Might Operate Near the Edge of Criticality

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10613))

Abstract

Would operating near criticality provide any functional benefit to the brain? In this paper we show that near critical dynamics is necessary for efficient information integration. The latter is quantified by a dynamical complexity measure \(\varPhi \), which aims to capture the amount of information generated by a networked dynamical system as a whole over and above that generated by the sum of its parts when the system transitions from one dynamical state to another. This formulation is based on the Kullback-Leibler divergence between the multi-variate distribution on the set of network states versus the corresponding factorized distribution over its parts. Using Gaussian distributions, we compute \(\varPhi \) for several network topologies. Our formulation applies to weighted networks with stochastic dynamics. We first compute \(\varPhi \) for artificial networks and then for the human brain’s connectome network. In all case we find that operating near the edge of criticality leads to high integrated information.

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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.

References

  1. Arsiwalla, X.D., Verschure, P.: Integrated information for large complex networks. In: The 2013 International Joint Conference on Neural Networks (IJCNN), pp. 1–7, August 2013

    Google Scholar 

  2. Arsiwalla, X.D.: Entropy functions with 5D Chern-Simons terms. J. High Energy Phys. 2009(09), 059 (2009)

    Article  MathSciNet  Google Scholar 

  3. Arsiwalla, X.D., Betella, A., Bueno, E.M., Omedas, P., Zucca, R., Verschure, P.F.: The dynamic connectome: a tool for large-scale 3D reconstruction of brain activity in real-time. In: ECMS, pp. 865–869 (2013)

    Google Scholar 

  4. Arsiwalla, X.D., Dalmazzo, D., Zucca, R., Betella, A., Brandi, S., Martinez, E., Omedas, P., Verschure, P.: Connectomics to semantomics: addressing the brain’s big data challenge. Procedia Comput. Sci. 53, 48–55 (2015)

    Article  Google Scholar 

  5. Arsiwalla, X.D., Moulin-Frier, C., Herreros, I., Sanchez-Fibla, M., Verschure, P.F.: The morphospace of consciousness. arXiv preprint arXiv:1705.11190 (2017)

  6. Arsiwalla, X.D., Verschure, P.: Computing information integration in brain networks. In: Wierzbicki, A., Brandes, U., Schweitzer, F., Pedreschi, D. (eds.) NetSci-X 2016. LNCS, vol. 9564, pp. 136–146. Springer, Cham (2016). doi:10.1007/978-3-319-28361-6_11

    Chapter  Google Scholar 

  7. Arsiwalla, X.D., Verschure, P.F.M.J.: High integrated information in complex networks near criticality. In: Villa, A.E.P., Masulli, P., Pons Rivero, A.J. (eds.) ICANN 2016. LNCS, vol. 9886, pp. 184–191. Springer, Cham (2016). doi:10.1007/978-3-319-44778-0_22

    Chapter  Google Scholar 

  8. Arsiwalla, X.D., Verschure, P.F.: The global dynamical complexity of the human brain network. Appl. Netw. Sci. 1(1), 16 (2016)

    Article  Google Scholar 

  9. Arsiwalla, X.D., Zucca, R., Betella, A., Martinez, E., Dalmazzo, D., Omedas, P., Deco, G., Verschure, P.: Network dynamics with brainX3: a large-scale simulation of the human brain network with real-time interaction. Front. Neuroinformatics 9(2) (2015)

    Google Scholar 

  10. Balduzzi, D., Tononi, G.: Integrated information in discrete dynamical systems: motivation and theoretical framework. PLoS Comput. Biol. 4(6), e1000091 (2008)

    Article  Google Scholar 

  11. Barrett, A.B., Barnett, L., Seth, A.K.: Multivariate granger causality and generalized variance. Phys. Rev. E 81(4), 041907 (2010)

    Article  MathSciNet  Google Scholar 

  12. Barrett, A.B., Seth, A.K.: Practical measures of integrated information for time-series data. PLoS Comput. Biol. 7(1), e1001052 (2011)

    Article  MathSciNet  Google Scholar 

  13. Betella, A., Bueno, E.M., Kongsantad, W., Zucca, R., Arsiwalla, X.D., Omedas, P., Verschure, P.: Understanding large network datasets through embodied interaction in virtual reality. In: Proceedings of the 2014 Virtual Reality International Conference, VRIC 2014, pp. 23:1–23:7. ACM, New York (2014)

    Google Scholar 

  14. Betella, A., Cetnarski, R., Zucca, R., Arsiwalla, X.D., Martínez, E., Omedas, P., Mura, A., Verschure, P.: BrainX3: embodied exploration of neural data. In: Proceedings of the 2014 Virtual Reality International Conference, VRIC 2014, pp. 37:1–37:4. ACM, New York (2014)

    Google Scholar 

  15. Deco, G., Ponce-Alvarez, A., Mantini, D., Romani, G.L., Hagmann, P., Corbetta, M.: Resting-state functional connectivity emerges from structurally and dynamically shaped slow linear fluctuations. J. Neurosci. 33(27), 11239–11252 (2013)

    Article  Google Scholar 

  16. Domb, C.: Phase Transitions and Critical Phenomena, vol. 19. Academic Press, London (2000)

    MATH  Google Scholar 

  17. Eguiluz, V.M., Chialvo, D.R., Cecchi, G.A., Baliki, M., Apkarian, A.V.: Scale-free brain functional networks. Phys. Rev. Lett. 94(1), 018102 (2005)

    Article  Google Scholar 

  18. Galán, R.F.: On how network architecture determines the dominant patterns of spontaneous neural activity. PLoS ONE 3(5), e2148 (2008)

    Article  Google Scholar 

  19. Hagmann, P., Cammoun, L., Gigandet, X., Meuli, R., Honey, C.J., Wedeen, V.J., Sporns, O.: Mapping the structural core of human cerebral cortex. PLoS Biol. 6(7), 15 (2008)

    Article  Google Scholar 

  20. Oizumi, M., Albantakis, L., Tononi, G.: From the phenomenology to the mechanisms of consciousness: integrated information theory 3.0. PLoS Comput. Biol. 10(5), e1003588 (2014)

    Article  Google Scholar 

  21. Omedas, P., Betella, A., Zucca, R., Arsiwalla, X.D., et al.: Xim-engine: a software framework to support the development of interactive applications that uses conscious and unconscious reactions in immersive mixed reality. In: Proceedings of the 2014 Virtual Reality International Conference, VRIC 2014, pp. 26:1–26:4. ACM, New York (2014)

    Google Scholar 

  22. Tononi, G., Sporns, O.: Measuring information integration. BMC Neurosci. 4(1), 31 (2003)

    Article  Google Scholar 

  23. Tononi, G., Sporns, O., Edelman, G.M.: A measure for brain complexity: relating functional segregation and integration in the nervous system. Proc. Natl. Acad. Sci. 91(11), 5033–5037 (1994)

    Article  Google Scholar 

  24. Zucca, R., Arsiwalla, X.D., Le, H., Rubinov, M., Verschure, P.F.M.J.: Scaling properties of human brain functional networks. In: Villa, A.E.P., Masulli, P., Pons Rivero, A.J. (eds.) ICANN 2016. LNCS, vol. 9886, pp. 107–114. Springer, Cham (2016). doi:10.1007/978-3-319-44778-0_13

    Chapter  Google Scholar 

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Acknowledgments

The CDAC project (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. University of Lausanne, Lausanne, Switzerland

    Alessandra Lintas

  2. University of Genoa, Genoa, Italy

    Stefano Rovetta

  3. Universitat Pompeu Fabra, Barcelona, Spain

    Paul F.M.J. Verschure

  4. University of Lausanne, Lausanne, Switzerland

    Alessandro E.P. Villa

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Arsiwalla, X.D., Verschure, P. (2017). Why the Brain Might Operate Near the Edge of Criticality. In: Lintas, A., Rovetta, S., Verschure, P., Villa, A. (eds) Artificial Neural Networks and Machine Learning – ICANN 2017. ICANN 2017. Lecture Notes in Computer Science(), vol 10613. Springer, Cham. https://doi.org/10.1007/978-3-319-68600-4_38

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  • DOI: https://doi.org/10.1007/978-3-319-68600-4_38

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68599-1

  • Online ISBN: 978-3-319-68600-4

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