Convolutional neural networks (CNNs) were inspired by early findings in the study of
biological vision. They have since become successful tools in computer vision and state-of …

Artificial deep neural networks (DNNs) initially inspired by the brain enable computers to
solve cognitive tasks at which humans excel. In the absence of explanations for such …

Seeing the interactions between other people is a critical part of our everyday visual
experience, but recognizing the social interactions of others is often considered outside the …

The human visual system is an intricate network of brain regions that enables us to
recognize the world around us. Despite its abundant lateral and feedback connections …

Neuronal networks with strong recurrent connectivity provide the brain with a powerful
means to perform complex computational tasks. However, high-gain excitatory networks are …

Feed-forward convolutional neural networks (CNNs) are currently state-of-the-art for object
classification tasks such as ImageNet. Further, they are quantitatively accurate models of …

Deep convolutional artificial neural networks (ANNs) are the leading class of candidate
models of the mechanisms of visual processing in the primate ventral stream. While initially …

The human brain achieves visual object recognition through multiple stages of linear and
nonlinear transformations operating at a millisecond scale. To predict and explain these …

Deep convolutional neural networks (DCNNs) and the ventral visual pathway share vast
architectural and functional similarities in visual challenges such as object recognition …

Deep artificial neural networks with spatially repeated processing (aka, deep convolutional
ANNs) have been established as the best class of candidate models of visual processing in …