Why the Brain Might Operate Near the Edge of Criticality

  • Xerxes D. Arsiwalla
  • Paul Verschure
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10613)


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.


Network dynamics Complexity measures Information theory 



The CDAC project (ERC-2013-ADG 341196).


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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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