Divisive normalization (DN) is a prominent computational building block in the brain that has
been proposed as a canonical cortical operation. Numerous experimental studies have …

Deep neural networks (DNNs) trained to perform visual tasks learn representations that
align with the hierarchy of visual areas in the primate brain. This finding has been taken to …

Neural processing is hypothesized to apply the same mathematical operations in a variety of
contexts, implementing so-called canonical neural computations. Divisive normalization …

The first two areas of the primate visual cortex (V1, V2) provide a paradigmatic example of
hierarchical computation in the brain. However, neither the functional properties of V2 nor …

Deep neural networks set the state-of-the-art across many tasks in computer vision, but their
generalization ability to simple image distortions is surprisingly fragile. In contrast, the …

Classical models describe primary visual cortex (V1) as a filter bank of orientation-selective
linear-nonlinear (LN) or energy models, but these models fail to predict neural responses to …

Highlights•Subunit pooling and normalization are building blocks of hierarchical cortical
models.•Image statistics models predict when normalization is recruited in primary …

Visual object recognition has been extensively studied in both neuroscience and computer
vision. Recently, the most popular class of artificial systems for this task, deep convolutional …

Geometric descriptions of deep neural networks (DNNs) have the potential to uncover core
representational principles of computational models in neuroscience. Here we examined the …

Cortical responses to repeated presentations of a sensory stimulus are variable. This
variability is sensitive to several stimulus dimensions, suggesting that it may carry useful …