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 …

Advances in experimental neuroscience have transformed our ability to explore the structure
and function of neural circuits. At the same time, advances in machine learning have …

A major shift is happening within neurophysiology: a population doctrine is drawing level
with the single-neuron doctrine that has long dominated the field. Population-level ideas …

What makes certain images more memorable than others? While much of memory research
has focused on participant effects, recent studies using a stimulus-centric perspective have …

Modular and distributed coding theories of category selectivity along the human ventral
visual stream have long existed in tension. Here, we present a reconciling framework …

Deep neural networks (DNNs) excel at visual recognition tasks and are increasingly used as
a modeling framework for neural computations in the primate brain. Just like individual …

Although we are continuously bombarded with visual input, only a fraction of incoming visual
events is perceived, remembered or acted on. The neural underpinnings of various forms of …

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

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

Various works have suggested that the memorability of an image is consistent across
people, and thus can be treated as an intrinsic property of an image. Using computer vision …