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Integrated Information Theory With Pyphi: Testing and Improvement Strategies.
The study of consciousness has increased in relevance in recent years in the scientific community. Along the same line, integrated information theory (IIT) is making inroads into understanding consciousness. However, the enormous computational costs make applying IIT to experimental data challenging. In this work, we intend to explore and design efficient computational algorithms applying techniques such as Divide and Conquer and parallel computation that can provide some solution to the challenges proposed by IIT based on optimizations and ap-proximations used by PyPhi toolbox to reduce the complexity of the calculations. This software package allows users to efficiently study cause-effect structures of discrete dynamical systems of binary elements, for causal analysis serves as an up-to-date reference implementation of the formalisms of integrated information theory. It has been used in our research on efficient algorithms.