Reconstructing feedback representations in the ventral visual pathway with a generative adversarial autoencoder
Fig 8
The impact of architecture and training procedure on the representational similarity of the model and brain spatial data.
(A) Encoder, decoder and LV layer RDMs of the autoencoder model are correlated (Spearman’s R) with subject-specific EVC RDMs. The averaged correlations over subjects with standard error of the mean are depicted. (B) Encoder, decoder and LV layer RDMs of the autoencoder model are correlated (Spearman’s R) with subject-specific IT RDMs. The averaged correlations over subjects with standard error of the mean are depicted. (C) Encoder, decoder and LV layer RDMs of the untrained model are correlated (Spearman’s R) with subject-specific EVC RDMs. The averaged correlations over subjects with standard error of the mean are depicted. (D) Encoder, decoder and LV layer RDMs of the untrained model are correlated (Spearman’s R) with subject-specific IT RDMs. The averaged correlations over subjects with standard error of the mean are depicted. The color coded (*) above each panel in C-D indicates that the correlation of the corresponding layer is significantly above zero. The black (*) indicates the correlations of the corresponding encoder and decoder layers are significantly different (N = 15; two-sided ttests; false discovery rate corrected at P < 0.05).