Measuring integrated information from the decoding perspective

Masafumi Oizumi, Shun-ichi Amari, Toru Yanagawa, Naotaka Fujii, Naotsugu Tsuchiya

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57 Citations (Scopus)


Accumulating evidence indicates that the capacity to integrate information in the brain is a prerequisite for consciousness. Integrated Information Theory (IIT) of consciousness provides a mathematical approach to quantifying the information integrated in a system, called integrated information, F. Integrated information is defined theoretically as the amount of information a system generates as a whole, above and beyond the amount of information its parts independently generate. IIT predicts that the amount of integrated information in the brain should reflect levels of consciousness. Empirical evaluation of this theory requires computing integrated information from neural data acquired from experiments, although difficulties with using the original measure F precludes such computations. Although some practical measures have been previously proposed, we found that these measures fail to satisfy the theoretical requirements as a measure of integrated information. Measures of integrated information should satisfy the lower and upper bounds as follows: The lower bound of integrated information should be 0 and is equal to 0 when the system does not generate information (no information) or when the system comprises independent parts (no integration). The upper bound of integrated information is the amount of information generated by the whole system. Here we derive the novel practical measure F* by introducing a concept of mismatched decoding developed from information theory. We show that F* is properly bounded from below and above, as required, as a measure of integrated information. We derive the analytical expression of F* under the Gaussian assumption, which makes it readily applicable to experimental data. Our novel measure F* can generally be used as a measure of integrated information in research on consciousness, and also as a tool for network analysis on diverse areas of biology
Original languageEnglish
Article numbere1004654
Number of pages18
JournalPLoS Computational Biology
Issue number1
Publication statusPublished - 2016

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