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An outstanding challenge with the Integrated Information Theory ofConsciousness (IIT) is to find a way of rapidly and accurately calculatingintegrated information from neural data. A number of measures of integratedinformation based on time series data have been proposed, but most measuresrequire finding the Minimum Information Partition of a network, which iscomputationally expensive and not practical for real brain data. Here, weintroduce a novel partition, the Maximum Modularity Partition, across which toquickly calculate integrated information. We also introduce a novel detectiontask on simulated data to evaluate the performance of integrated informationmeasures across different partitions. We show that integrated information canbe reliably and quickly calculated across the Maximum Modularity Partition, aswell as the previously proposed atomic partition, even in relatively largenetworks, which constitutes an advance in researchers' ability to empiricallytest the predictions of IIT in real brain data.

Moving Past the Minimum Information Partition: How To Quickly and Accurately Calculate Integrated Information