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Deep convolutional neural networks (CNNs) trained on objects and scenes haveshown intriguing ability to predict some response properties of visual corticalneurons. However, the factors and computations that give rise to such ability,and the role of intermediate processing stages in explaining changes thatdevelop across areas of the cortical hierarchy, are poorly understood. Wefocused on the sensitivity to textures as a paradigmatic example, since recentneurophysiology experiments provide rich data pointing to texture sensitivityin secondary but not primary visual cortex. We developed a quantitativeapproach for selecting a subset of the neural unit population from the CNN thatbest describes the brain neural recordings. We found that the first two layersof the CNN showed qualitative and quantitative correspondence to the corticaldata across a number of metrics. This compatibility was reduced for thearchitecture alone rather than the learned weights, for some other relatedhierarchical models, and only mildly in the absence of a nonlinear computationakin to local divisive normalization. Our results show that the CNN class ofmodel is effective for capturing changes that develop across early areas ofcortex, and has the potential to facilitate understanding of the computationsthat give rise to hierarchical processing in the brain.

Correspondence of Deep Neural Networks and the Brain for Visual Textures