Artificial vision has often been described as one of the key remaining challenges to be
solved before machines can act intelligently. Recent developments in a branch of machine …

Using movies and narratives as naturalistic stimuli in human neuroimaging studies has
yielded significant advances in understanding of cognitive and emotional functions. The …

Biological and artificial information processing systems form representations that they can
use to categorize, reason, plan, navigate, and make decisions. How can we measure the …

Convolutional neural networks (CNNs) are increasingly used to model human vision due to
their high object categorization capabilities and general correspondence with human brain …

Cortical regions apparently selective to faces, places, and bodies have provided important
evidence for domain-specific theories of human cognition, development, and evolution. But …

Studying the neural basis of human dynamic visual perception requires extensive
experimental data to evaluate the large swathes of functionally diverse brain neural …

Convolutional neural network (CNN) driven by image recognition has been shown to be
able to explain cortical responses to static pictures at ventral-stream areas. Here, we further …

The visual system of mammals is comprised of parallel, hierarchical specialized pathways.
Different pathways are specialized in so far as they use representations that are more …

Information that is shared across brains is encoded in idiosyncratic fine-scale functional
topographies. Hyperalignment captures shared information by projecting pattern vectors for …

Deep neural networks (DNNs) have recently been applied successfully to brain decoding
and image reconstruction from functional magnetic resonance imaging (fMRI) activity …