A Compression-Complexity Measure of Integrated Information
arXiv Preprint Archive August 23, 2016 Mohit Virmani, Nithin Nagaraj
A new measure called Φ^C bridges Integrated Information Theory (IIT) and the Perturbational Complexity Index (PCI) by using lossless data compression to quantify integrated information in brain networks. Unlike IIT's Φ, which is computationally expensive and dependent on current state, Φ^C is mathematically well bounded, has negligible state dependence, and scales linearly with network nodes, avoiding combinatorial explosion. Computer simulations show Φ^C produces similar hierarchies to Φ across multiple-node networks and reveals interactions between differentiation, integration, and entropy. It offers a faster heuristic for measuring integrated information—and thus a potential proxy for consciousness—in larger networks like the human brain, enabling tests of brain complexity predictions on real neural data.