CODES

Practical Phi Toolbox for Integrated Information Analysis

This toolbox provides MATLAB codes for end-to-end computation in "practical versions" of integrated information theory (IIT):

  • Computing practical measures of integrated information (Phi).

  • Searching for minimum information partitions (MIPs).

  • Searching for complexes.

These are key concepts in IIT and can be generally utilized for analyzing stochastic systems, i.e., evaluating how much information is integrated in a system, finding the optimal partition and the cores of a system.

In general, these computations take a large amount of time, which has hindered the application of IIT to real data.

This toolbox provides fast algorithms, enabling us to analyze large systems in a practical amount of time.

GitHub:

https://github.com/oizumi-lab/PhiToolbox/blob/master/Readme.md

References

[1] Oizumi, M., Amari, S, Yanagawa, T., Fujii, N., & Tsuchiya, N. (2016). Measuring integrated information from the decoding perspective. PLoS Comput Biol, 12(1), e1004654.

http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004654


[2] Oizumi, M., Tsuchiya, N., & Amari, S. (2016). Unified framework for information integration based on information geometry. Proceedings of the National Academy of Sciences, 113(51), 14817-14822.

http://www.pnas.org/content/113/51/14817.short


[3] Hidaka, S., Oizumi, M. (2018). Fast and exact search for the partition with minimal information loss. PLoS ONE, 13(9), e0201126.


[4] Kitazono, J., Kanai, R., Oizumi, M. (2018). Efficient algorithms for searching the minimum information partition in integrated information theory. Entropy, 20, 173.

http://www.mdpi.com/1099-4300/20/3/173


[5] Kitazono, J., Kanai, R., Oizumi, M. (2020). Efficient search for informational cores in complex systems: Application to brain networks. bioRxiv.

https://www.biorxiv.org/content/10.1101/2020.04.06.027441v2.external-links.html