title: Statistical mechanics of consciousness: Maximization of information content of network is associated with conscious awareness.
abstract: It is said that complexity lies between order and disorder. In the case of brain activity and physiology in general, complexity issues are being considered with increased emphasis. We sought to identify features of brain organization that are optimal for sensory processing, and that may guide the emergence of cognition and consciousness, by analyzing neurophysiological recordings in conscious and unconscious states. We find a surprisingly simple result: Normal wakeful states are characterized by the greatest number of possible configurations of interactions between brain networks, representing highest entropy values. Therefore, the information content is larger in the network associated to conscious states, suggesting that consciousness could be the result of an optimization of information processing. These findings help to guide in a more formal sense inquiry into how consciousness arises from the organization of matter.
authors.affiliation: Laboratoire Psychologie de la Perception, CNRS and Université Paris Descartes, Sorbonne Paris Cité, Paris 75006, France.
authors.forename: R
authors.initials: R
authors.lastname: Guevara Erra
authors.affiliation: Neuroscience and Mental Health Programme, Division of Neurology, Hospital for Sick Children, Institute of Medical Science and Department of Paediatrics, University of Toronto, Toronto M5G1X8, Canada.
authors.forename: D M
authors.initials: DM
authors.lastname: Mateos
authors.affiliation: Krembil Neuroscience Centre, Toronto Western Hospital, University of Toronto, Toronto M5G1X8, Canada.
authors.forename: R
authors.initials: R
authors.lastname: Wennberg
authors.affiliation: Neuroscience and Mental Health Programme, Division of Neurology, Hospital for Sick Children, Institute of Medical Science and Department of Paediatrics, University of Toronto, Toronto M5G1X8, Canada.
authors.forename: J L
authors.initials: JL
authors.lastname: Perez Velazquez
mesh_terms: D001921:Brain / Q000502:physiology; D003243:Consciousness / Q000502:physiology*; D003627:Data Interpretation, Statistical; D019277:Entropy; D006801:Humans; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*