Efficient Algorithms for Searching the Minimum Information Partition in Integrated Information Theory.

Jun Kitazono,Ryota Kanai,Masafumi Oizumi

doi:10.3390/e20030173

Jun Kitazono, Ryota Kanai + Show 1 more

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https://doi.org/10.3390/e20030173

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Abstract

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.

Highlights

The brain receives various information from the external world
We proposed an efficient algorithm for searching for the Minimum Information
The computational time of an exhaustive search for the Minimum Information Partition (MIP) grows exponentially with the arithmetic growth of system size, which has been an obstacle to applying Information Theory (IIT) to experimental data

Summary

Introduction

The brain receives various information from the external world. Integrating this information is an essential property for cognition and consciousness [1]. When we see an object, we cannot experience only its shape independently of its color. We cannot experience only the left half of the visual field independently of the right half. Integrated Information Theory (IIT) of consciousness considers that the unification of consciousness should be realized by the ability of the brain to integrate information [2,3,4]. The brain has internal mechanisms to integrate information about the shape and color of an object or information of the right and left visual field, and our visual experiences are unified. The hypothesis is indirectly supported by experiments which showed the breakdown of effective connectivity in the brain during loss

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