Why the Brain Might Operate Near the Edge of Criticality

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, 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.

Keywords

Network dynamics Complexity measures Information theory 

References

  1. 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 2013Google Scholar
  2. 2.
    Arsiwalla, X.D.: Entropy functions with 5D Chern-Simons terms. J. High Energy Phys. 2009(09), 059 (2009)CrossRefMathSciNetGoogle Scholar
  3. 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. 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)CrossRefGoogle Scholar
  5. 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. 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 CrossRefGoogle Scholar
  7. 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 CrossRefGoogle Scholar
  8. 8.
    Arsiwalla, X.D., Verschure, P.F.: The global dynamical complexity of the human brain network. Appl. Netw. Sci. 1(1), 16 (2016)CrossRefGoogle Scholar
  9. 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. 10.
    Balduzzi, D., Tononi, G.: Integrated information in discrete dynamical systems: motivation and theoretical framework. PLoS Comput. Biol. 4(6), e1000091 (2008)CrossRefGoogle Scholar
  11. 11.
    Barrett, A.B., Barnett, L., Seth, A.K.: Multivariate granger causality and generalized variance. Phys. Rev. E 81(4), 041907 (2010)CrossRefMathSciNetGoogle Scholar
  12. 12.
    Barrett, A.B., Seth, A.K.: Practical measures of integrated information for time-series data. PLoS Comput. Biol. 7(1), e1001052 (2011)CrossRefMathSciNetGoogle Scholar
  13. 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. 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. 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)CrossRefGoogle Scholar
  16. 16.
    Domb, C.: Phase Transitions and Critical Phenomena, vol. 19. Academic Press, London (2000)MATHGoogle Scholar
  17. 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)CrossRefGoogle Scholar
  18. 18.
    Galán, R.F.: On how network architecture determines the dominant patterns of spontaneous neural activity. PLoS ONE 3(5), e2148 (2008)CrossRefGoogle Scholar
  19. 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)CrossRefGoogle Scholar
  20. 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)CrossRefGoogle Scholar
  21. 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. 22.
    Tononi, G., Sporns, O.: Measuring information integration. BMC Neurosci. 4(1), 31 (2003)CrossRefGoogle Scholar
  23. 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)CrossRefGoogle Scholar
  24. 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 CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Synthetic Perceptive Emotive and Cognitive Systems (SPECS) Lab, Center of Autonomous Systems and NeuroroboticsUniversitat Pompeu FabraBarcelonaSpain
  2. 2.Institució Catalana de Recerca i Estudis Avançats (ICREA)BarcelonaSpain

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