One in a series of talks from the 2019 Models of Consciousness conference.
Ramón Guevara Erra
Integrative Neuroscience and Cognition Center (UMR 8002), CNRS and
Université Paris Descartes, Paris, France
It has been argued that consciousness could be an emergent property of large neuronal networks, associated to the integration of information in the brain. However, it is not yet clear how is consciousness related to the complexity of functional brain networks. Based on a statistical mechanics approach, we sought to identify features of brain organization that are optimal for sensory processing, and that may guide the emergence of 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.
Filmed at the Models of Consciousness conference, University of Oxford, September 2019.