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.
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| http://dx.doi.org/10.1016/j.concog.2011.05.016 | DOI Listing |
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