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- W4386083906 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 new inference methods enabling researchers to evaluate and compare models based on the accuracy of their predictions of representational geometries: A good model should accurately predict the distances among the neural population representations (e.g. of a set of stimuli). Our inference methods combine novel 2-factor extensions of crossvalidation (to prevent overfitting to either subjects or conditions from inflating our estimates of model accuracy) and bootstrapping (to enable inferential model comparison with simultaneous generalization to both new subjects and new conditions). We validate the inference methods on data where the ground-truth model is known, by simulating data with deep neural networks and by 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 ( rsatoolbox.readthedocs.io )." @default.
- W4386083906 created "2023-08-24" @default.
- W4386083906 creator A5017065424 @default.
- W4386083906 creator A5025465517 @default.
- W4386083906 creator A5049394448 @default.
- W4386083906 creator A5084467223 @default.
- W4386083906 date "2023-08-23" @default.
- W4386083906 modified "2023-10-01" @default.
- W4386083906 title "Statistical inference on representational geometries" @default.
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