Neuroscience has focused on the detailed implementation of computation, studying neural
codes, dynamics and circuits. In machine learning, however, artificial neural networks tend …

Abstract Systems neuroscience seeks explanations for how the brain implements a wide
variety of perceptual, cognitive and motor tasks. Conversely, artificial intelligence attempts to …

Neuroscience research is undergoing a minor revolution. Recent advances in machine
learning and artificial intelligence research have opened up new ways of thinking about …

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 …

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 …

The human cerebral cortex is central to a wide array of cognitive functions, from vision to
language, reasoning, decision-making, and motor control. Yet, nearly a century after the …

For both humans and machines, the essence of learning is to pinpoint which components in
its information processing pipeline are responsible for an error in its output, a challenge that …

Deep learning and predictive coding architectures commonly assume that inference in
neural networks is hierarchical. However, largely neglected in deep learning and predictive …

The human brain is exceedingly complex and studying it encompasses gathering
information across a range of levels, from molecular processes to behavior. The sheer …

Perspectives for understanding the brain vary across disciplines and this has challenged our
ability to describe the brain's functions. In this comment, we discuss how emerging …