There is growing recognition of the importance of considering the information content of experimentally recorded neural signals, rather than studying only differences in activation levels between conditions. I will present Gaussian-Copula Mutual Information (GCMI) [1], a mutual information estimator that has a number of advantages for practical data analysis. I will demonstrate how this estimator can be used to quantify representational interactions in neuroimaging data through co-information and the Partial Information Decomposition [2], both approaches which can quantify redundancy and synergy between neural representations or between stimulus features. I will also introduce the Partial Entropy Decomposition [3], which can reveal a detailed quantitative picture of entropy sharing structure in (small scale) complex systems.
[1] Ince et al. (2017) Human Brain Mapping doi:10.1002/hbm.23471
[2] Ince (2017) Entropy doi:10.3390/e19070318
[3] Ince (2017) arXiv 1702.01591