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