The success of deep neural networks suggests that cognition may emerge from
indecipherable patterns of distributed neural activity. Yet these networks are pattern …

Neural network models can now recognise images, understand text, translate languages,
and play many human games at human or superhuman levels. These systems are highly …

Deep neural networks achieve outstanding results in a large variety of tasks, often
outperforming human experts. However, a known limitation of current neural architectures is …

Modern neural network architectures can leverage large amounts of data to generalize well
within the training distribution. However, they are less capable of systematic generalization …

Artificial deep neural networks (DNNs) initially inspired by the brain enable computers to
solve cognitive tasks at which humans excel. In the absence of explanations for such …

The goal of cognitive neuroscience is to understand how mental operations are performed
by the brain. Given the complexity of the brain, this is a challenging endeavor that requires …

The goal of computational neuroscience is to find mechanistic explanations of how the
nervous system processes information to support cognitive function and behaviour. At the …

The remarkable successes of convolutional neural networks (CNNs) in modern computer
vision are by now well known, and they are increasingly being explored as computational …

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 …

Recent work has shown that convolutional neural networks (CNNs) trained on image
recognition tasks can serve as valuable models for predicting neural responses in primate …