Unraveling the complexity of rat object vision requires a full convolutional network and beyond.

Despite their prominence as model systems of visual functions, it remains unclear whether rodents are capable of truly advanced processing of visual information. Here, we used a convolutional neural network (CNN) to measure the computational complexity required to account for rat object vision. We found that rat ability to discriminate objects despite scaling, translation, and rotation was well accounted for by the CNN mid-level layers. However, the tolerance displayed by rats to more severe image manipulations (occlusion and reduction of objects to outlines) was achieved by the network only in the final layers. Moreover, rats deployed perceptual strategies that were more invariant than those of the CNN, as they more consistently relied on the same set of diagnostic features across transformations. These results reveal an unexpected level of sophistication of rat object vision, while reinforcing the intuition that CNNs learn solutions that only marginally match those of biological visual systems.