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Information Integration from Distributed Threshold-Based Interactions

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  • Valmir C. Barbosa

Abstract

We consider distributed units that interact by message-passing. Each message carries a tag and causes the receiving unit to send out messages as a function of the tags it has received and a threshold. This simple model abstracts some of the essential characteristics of several artificial intelligence systems and of biological systems epitomized by the brain. We study the integration of information inside a temporal window as the dynamics unfolds. We quantify information integration by the total correlation, relative to the window’s duration, of a set of random variables valued as a function of message arrival. Total correlation refers to the rise of information gain above that which the units achieve individually, being therefore related to some models of consciousness. We report on extensive computational experiments exploring the interrelations of the model’s parameters (two probabilities and the threshold). We find that total correlation can occur at significant fractions of the maximum possible value and reinterpret the model’s parameters in terms of the current best estimates of some quantities pertaining to cortical structure and dynamics. We find the resulting possibilities to be well aligned with the time frames within which percepts are thought to be processed and eventually rendered conscious.

Suggested Citation

  • Valmir C. Barbosa, 2017. "Information Integration from Distributed Threshold-Based Interactions," Complexity, Hindawi, vol. 2017, pages 1-14, January.
    • Handle: RePEc:hin:complx:7046359
    DOI: 10.1155/2017/7046359
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    References listed on IDEAS

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    1. Masafumi Oizumi & Larissa Albantakis & Giulio Tononi, 2014. "From the Phenomenology to the Mechanisms of Consciousness: Integrated Information Theory 3.0," PLOS Computational Biology, Public Library of Science, vol. 10(5), pages 1-25, May.
    2. Yousheng Shu & Andrea Hasenstaub & Alvaro Duque & Yuguo Yu & David A. McCormick, 2006. "Modulation of intracortical synaptic potentials by presynaptic somatic membrane potential," Nature, Nature, vol. 441(7094), pages 761-765, June.
    3. Wei-Chung Allen Lee & Vincent Bonin & Michael Reed & Brett J. Graham & Greg Hood & Katie Glattfelder & R. Clay Reid, 2016. "Anatomy and function of an excitatory network in the visual cortex," Nature, Nature, vol. 532(7599), pages 370-374, April.
    4. Volodymyr Mnih & Koray Kavukcuoglu & David Silver & Andrei A. Rusu & Joel Veness & Marc G. Bellemare & Alex Graves & Martin Riedmiller & Andreas K. Fidjeland & Georg Ostrovski & Stig Petersen & Charle, 2015. "Human-level control through deep reinforcement learning," Nature, Nature, vol. 518(7540), pages 529-533, February.
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    Cited by:

    1. Luciano Rossoni & Cezar Eduardo Aranha & Wesley Mendes-Da-Silva, 2018. "The Complexity of Social Capital: The Influence of Board and Ownership Interlocks on Implied Cost of Capital in an Emerging Market," Complexity, Hindawi, vol. 2018, pages 1-12, February.

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