Characterization of deep neural network features by decodability from human brain activity

We present a dataset derived through the DNN feature decoding analyses (Horikawa and Kamitani, 2017), including true and decoded feature values of DNNs (AlexNet and VGG19) and decoding accuracies of individual DNN features with their rankings. The decoding accuracies of individual DNN features were highly correlated across subjects, suggesting the systematic differences between the brain and DNNs. The unpreprocessed fMRI data is available from the OpenNeuro (https://openneuro.org/datasets/ds001246). We hope the present dataset will contribute to reveal the gap between the brain and DNNs and provide an opportunity to make use of the decoded features for further applications.