Towards information-theoretic limits of the global neuronal workspace architecture

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The global neuronal workspace architecture has been proposed as a biologically plausible computational model for the operation of human consciousness, essentially arguing that signals ow from several perceptual, memory, and attentional regions to a central broadcast medium where certain signals cause a cascade that enters conscious awareness. Separately, the integrated information theory of consciousness has proposed that multivariate information measures Φ that generalize Shannon's mutual information capture the level of interaction among brain regions and therefore measure consciousness. Here, we ask whether these two theories are in fact two sides of the same coin, by suggesting a mathematical theorem on the operational limits of the global neuronal workspace, similar to Shannon's noisy channel coding theorem, that would naturally be in terms of one particular multivariate information measure Φ.

Original languageEnglish (US)
JournalCEUR Workshop Proceedings
StatePublished - 2018
Event2019 Papers of the Towards Conscious AI Systems Symposium, TOCAIS 2019 - Stanford, United States
Duration: Mar 25 2019Mar 27 2019


  • Coding theorem
  • Global neuronal workspace theory
  • Integrated information theory

ASJC Scopus subject areas

  • Computer Science(all)


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