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 …

Visual systems of primates are the gold standard of robust perception. There is thus a
general belief that mimicking the neural representations that underlie those systems will …

Visual systems of primates are the gold standard of robust perception. There is thus a
general belief that mimicking the neural representations that underlie those systems will …

Humans accurately identify observed actions despite large dynamic changes in their retinal
images and a variety of visual presentation formats. A large network of brain regions in …

The ability to perceive visual objects with various types of transformations, such as rotation,
translation, and scaling, is crucial for consistent object recognition. In machine learning …

A comprehensive understanding of the stimulus-response properties of individual neurons is
necessary to crack the neural code of sensory cortices. However, a barrier to achieving this …

Forming transformation-tolerant object representations is critical to high-level primate vision.
Despite its significance, many details of tolerance in the human brain remain unknown …

Does the human brain represent perspectival shapes, ie, viewpoint-dependent object
shapes, especially in relatively higher-level visual areas such as the lateral occipital cortex …

Object recognition is challenging because the same object can produce vastly different
images, mixing signals related to its identity with signals due to its image attributes, such as …

Object recognition has been a central question in human vision research. The general
consensus is that the ventral and dorsal visual streams are the major processing pathways …