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 …

Neuroscientists have long characterized the properties and functions of the nervous system,
and are increasingly succeeding in answering how brains perform the tasks they do. But the …

Semantic navigation is necessary to deploy mobile robots in uncontrolled environments
such as homes or hospitals. Many learning-based approaches have been proposed in …

Navigation is a fundamental problem of mobile robots, for which Deep Reinforcement
Learning (DRL) has received significant attention because of its strong representation and …

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 …

In coming decades, artificial general intelligence (AGI) may surpass human capabilities at
many critical tasks. We argue that, without substantial effort to prevent it, AGIs could learn to …

Flexible behaviors over long timescales are thought to engage recurrent neural networks in
deep brain regions, which are experimentally challenging to study. In insects, recurrent …

What has happened in machine learning lately, and what does it mean for the future of
medical image analysis? Machine learning has witnessed a tremendous amount of attention …

The hippocampal-entorhinal system is important for spatial and relational memory tasks. We
formally link these domains, provide a mechanistic understanding of the hippocampal role in …

The use of artificial intelligence, and the deep-learning subtype in particular, has been
enabled by the use of labeled big data, along with markedly enhanced computing power …