Convolutional neural networks trained on object recognition derive inspiration from the
neural architecture of the visual system in mammals, and have been used as models of the …

Deep convolutional neural networks (CNNs) have certain structural, mechanistic,
representational, and functional parallels with primate visual cortex and also many …

Recent advances in neural network modeling have enabled major strides in computer vision
and other artificial intelligence applications. Human-level visual recognition abilities are …

Task-optimized deep convolutional neural networks are the most quantitatively accurate
models of the primate ventral visual stream. However, such networks are implausible as a …

How well do deep neural networks fare as models of mouse visual cortex? A majority of
research to date suggests results far more mixed than those produced in the modeling of …

Convolutional Neural Networks (CNN) are a class of machine learning models
predominately used in computer vision tasks and can achieve human-like performance …

Convolutional neural networks (CNNs) have achieved very high object categorization
performance recently. It has increasingly become a common practice in human fMRI …

Partially inspired by features of computation in visual cortex, deep neural networks compute
hierarchical representations of their inputs. While these networks have been highly …

Recent work on modeling neural responses in the primate visual system has benefited from
deep neural networks trained on large-scale object recognition, and found a hierarchical …

Deep neural networks provide the current best models of visual information processing in
the primate brain. Drawing on work from computer vision, the most commonly used networks …