  An outstanding challenge with the Integrated Information Theory of
Consciousness (IIT) is to find a way of rapidly and accurately calculating
integrated information from neural data. A number of measures of integrated
information based on time series data have been proposed, but most measures
require finding the Minimum Information Partition of a network, which is
computationally expensive and not practical for real brain data. Here, we
introduce a novel partition, the Maximum Modularity Partition, across which to
quickly calculate integrated information. We also introduce a novel detection
task on simulated data to evaluate the performance of integrated information
measures across different partitions. We show that integrated information can
be reliably and quickly calculated across the Maximum Modularity Partition, as
well as the previously proposed atomic partition, even in relatively large
networks, which constitutes an advance in researchers' ability to empirically
test the predictions of IIT in real brain data.
