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Abstract

Neuroscience has recently made much progress, expanding the complexity of both neural-activity measurements and brain-computational models. However, we lack robust methods for connecting theory and experiment by evaluating our new big models with our new big data. Here we introduce a new inferential methodology to evaluate models based on their predictions of representational geometries. The inference can handle flexible parametrized models and can treat both subjects and conditions as random effects, such that conclusions generalize to the respective populations of subjects and conditions. We validate the inference methods using extensive simulations with deep neural networks and resampling of calcium imaging and functional MRI data. Results demonstrate that the methods are valid and conclusions generalize correctly. These data analysis methods are available in an open-source Python toolbox.