Efficient Algorithms for Searching the Minimum Information Partition in Integrated Information Theory
Araya, Inc., Toranomon 15 Mori Building, 2-8-10 Toranomon, Minato-ku, Tokyo 105-0001, Japan
Graduate School of Engineering, Kobe University, 1-1 Rokkodai-cho, Nada-ku, Kobe-shi, Hyogo 657-8501, Japan
RIKEN Brain Science Institute, 2-1 Hirosawa Wako City, Saitama 351-0198, Japan
Authors to whom correspondence should be addressed.
Received: 18 December 2017 / Revised: 26 February 2018 / Accepted: 27 February 2018 / Published: 6 March 2018
The ability to integrate information in the brain is considered to be an essential property for cognition and consciousness. Integrated Information Theory (IIT) hypothesizes that the amount of integrated information (
) in the brain is related to the level of consciousness. IIT proposes that, to quantify information integration in a system as a whole, integrated information should be measured across the partition of the system at which information loss caused by partitioning is minimized, called the Minimum Information Partition (MIP). The computational cost for exhaustively searching for the MIP grows exponentially with system size, making it difficult to apply IIT to real neural data. It has been previously shown that, if a measure of
satisfies a mathematical property, submodularity, the MIP can be found in a polynomial order by an optimization algorithm. However, although the first version of
is submodular, the later versions are not. In this study, we empirically explore to what extent the algorithm can be applied to the non-submodular measures of
by evaluating the accuracy of the algorithm in simulated data and real neural data. We find that the algorithm identifies the MIP in a nearly perfect manner even for the non-submodular measures. Our results show that the algorithm allows us to measure
in large systems within a practical amount of time.
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MDPI and ACS Style
Kitazono, J.; Kanai, R.; Oizumi, M. Efficient Algorithms for Searching the Minimum Information Partition in Integrated Information Theory. Entropy 2018, 20, 173.
Kitazono J, Kanai R, Oizumi M. Efficient Algorithms for Searching the Minimum Information Partition in Integrated Information Theory. Entropy. 2018; 20(3):173.
Kitazono, Jun; Kanai, Ryota; Oizumi, Masafumi. 2018. "Efficient Algorithms for Searching the Minimum Information Partition in Integrated Information Theory." Entropy 20, no. 3: 173.
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