  We study the intriguing connection between visual data, deep networks, and
the brain. Our method creates a universal channel alignment by using brain
voxel fMRI response prediction as the training objective. We discover that deep
networks, trained with different objectives, share common feature channels
across various models. These channels can be clustered into recurring sets,
corresponding to distinct brain regions, indicating the formation of visual
concepts. Tracing the clusters of channel responses onto the images, we see
semantically meaningful object segments emerge, even without any supervised
decoder. Furthermore, the universal feature alignment and the clustering of
channels produce a picture and quantification of how visual information is
processed through the different network layers, which produces precise
comparisons between the networks.
