Highlights•Artificial and biological neural networks can be analyzed using similar
methods.•Neural analysis has revealed similarities between the representations in artificial …

Deep convolutional networks (DCNNs) are achieving previously unseen performance in
object classification, raising questions about whether DCNNs operate similarly to human …

Our knowledge of sensory processing has advanced dramatically in the last few decades,
but this understanding remains far from complete, especially for stimuli with the large …

Deep networks provide a potentially rich interconnection between neuroscientific and
artificial approaches to understanding visual intelligence, but the relationship between …

Deep convolutional neural networks (CNNs) trained on visual objects have shown intriguing
ability to predict some response properties of visual cortical neurons. However, the factors …

Drawing on elementary invariance principles, we propose that a statistical geometric object,
the probability distribution of the normalized contour curvatures (NCC) in the intensity field of …

Deep convolutional neural networks (CNNs) trained on objects and scenes have shown
intriguing ability to predict some response properties of visual cortical neurons. However, the …

Humans and other primates rely on vision. Our visual system endows us with the ability to
perceive, recognize, and manipulate objects, to avoid obstacles and dangers, to choose …

Deep convolutional neural networks (CNNs) are becoming increasingly popular models to
predict neural responses in visual cortex. However, contextual effects, which are prevalent in …

Artificial neural networks are inspired by information processing performed by neural circuits
in biology. While existing models are sufficient to solve many real-world tasks, they are far …