In this Review, we describe the singular success of attractor neural network models in
describing how the brain maintains persistent activity states for working memory, corrects�…

Learning and interpreting the structure of the environment is an innate feature of biological
systems, and is integral to guiding flexible behaviors for evolutionary viability. The concept of�…

Integrating neurons into digital systems may enable performance infeasible with silicon
alone. Here, we develop DishBrain, a system that harnesses the inherent adaptive�…

Learning to predict rewards based on environmental cues is essential for survival. It is
believed that animals learn to predict rewards by updating predictions whenever the�…

Artificial neural networks (ANNs) inspired by biology are beginning to be widely used to
model behavioural and neural data, an approach we call 'neuroconnectionism'. ANNs have�…

Humans and animals use mental representations of the spatial structure of the world to
navigate. The classical view is that these representations take the form of Euclidean�…

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 medial frontal cortex and adjacent orbitofrontal cortex have been the focus of
investigations of decision-making, behavioral flexibility, and social behavior. We review�…

The emergence of powerful artificial intelligence (AI) is defining new research directions in
neuroscience. To date, this research has focused largely on deep neural networks trained�…

Research in Neuroscience, as in many scientific disciplines, is undergoing a renaissance
based on deep learning. Unique to Neuroscience, deep learning models can be used not�…