CCN meeting | Robin Ince (University of Glasgow, UK)

07-03-2019 from 15:00 to 16:00
Henri Dunantlaan 2, room 4.1
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Quantifying representational interactions in neuroimaging data using information theory

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 show how GCMI and PID can be used for detailed comparison of predictive models.

[1] Ince et al. (2017) Human Brain Mapping doi:10.1002/hbm.23471
[2] Ince (2017) Entropy doi:10.3390/e19070318