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 …

Deep neural networks (DNN) have set new standards at predicting responses of neural
populations to visual input. Most such DNNs consist of a convolutional network (core) …

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 …

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

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 …

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 …

Deep neural networks (DNNs) trained to perform visual tasks learn representations that
align with the hierarchy of visual areas in the primate brain. This finding has been taken to …

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 …

Unit activity in particular deep neural networks (DNNs) are remarkably similar to the
neuronal population responses to static images along the primate ventral visual cortex …