title: Accuracy and performance of the state-based Φ and liveliness measures of information integration.
abstract: A number of people have suggested that there is a link between information integration and consciousness, and a number of algorithms for calculating information integration have been put forward. The most recent of these is Balduzzi and Tononi's state-based Φ algorithm, which has factorial dependencies that severely limit the number of neurons that can be analyzed. To address this issue an alternative state-based measure known as liveliness has been developed, which uses the causal relationships between neurons to identify the areas of maximum information integration. This paper outlines the state-based Φ and liveliness algorithms and sets out a number of test networks that were used to compare their accuracy and performance. The results show that liveliness is a reasonable approximation to state-based Φ for some network topologies, and it has a much more scalable performance than state-based Φ.
authors.affiliation: Department of Computing, Imperial College, London SW7 2BT, UK. dgamez@imperial.ac.uk
authors.forename: David
authors.initials: D
authors.lastname: Gamez
authors.forename: Igor
authors.initials: I
authors.lastname: Aleksander
mesh_terms: D000465:Algorithms; D003243:Consciousness; D006801:Humans; D007257:Information Theory; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology; D055139:Sensory Gating / Q000502:physiology