The past decade has witnessed the great success of deep neural networks in various
domains. However, deep neural networks are very resource-intensive in terms of energy …

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 …

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

The rapid development of information technology has led to urgent requirements for high
efficiency and ultralow power consumption. In the past few decades, neuromorphic …

It is typically assumed that large networks of neurons exhibit a large repertoire of nonlinear
behaviours. Here we challenge this assumption by leveraging mathematical models derived …

Current hardware approaches to biomimetic or neuromorphic artificial intelligence rely on
elaborate transistor circuits to simulate biological functions. However, these can instead be …

Modeling complex physical dynamics is a fundamental task in science and engineering.
Traditional physics-based models are first-principled, explainable, and sample-efficient …

While deep learning has shown tremendous success in a wide range of domains, it remains
a grand challenge to incorporate physical principles in a systematic manner to the design …

BACKGROUND Much of ecological theory and the understanding of ecological systems has
been based on the idea that the observed states and dynamics of ecological systems can be …

Undulatory swimming represents an ideal behavior to investigate locomotion control and the
role of the underlying central and peripheral components in the spinal cord. Many vertebrate …