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Efficient Algorithms for Searching the Minimum Information Partition in Integrated Information Theory. (arXiv:1712.06745v1 [q-bio.NC])
来源于:arXiv
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 ($\Phi$) 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 $\Phi$ satisfies a
mathematical property, submodularity, the MIP can be found in a polynomial
order by an optimization algorithm. However, although the first version of
$\Phi$ is submodular, the later versions are not. In this study, we empirically
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