Classical autoassociative memory models have been central to understanding emergent computations in recurrent neural circuits across diverse biological contexts. However, they typically neglect neurom...

Active persistent dynamics are shown to mimic some aspects of Hebbian unlearning --- suggesting that nonequilibrium dynamics can provide an alternative way to improve associative recall in neural netw...

Understanding how biological systems achieve robust control despite relying on imperfect local information remains a challenging problem. Here, we consider non-equilibrium models which are generically...

How cells use biophysical processes to interpret complex input signals is not well understood. This study reveals limits to the computational power of generic non-equilibrium systems and shows how the...

In this work we show how a network inspired by a coarse-grained description of actomyosin cytoskeleton can learn - in a contrastive learning framework - from environmental perturbations if it is endow...

Chemical waves represent one of the fundamental behaviors that emerge in nonlinear, out-of-equilibrium chemical systems. They also play a central role in regulating behaviors and development of biolog...

Active nematics, formed from a liquid crystalline suspension of active force dipoles, are a paradigmatic active matter system whose study provides insights into how chemical driving produces the cellu...

Diffusion models exhibit robust generative properties by approximating the underlying distribution of a dataset and synthesizing data by sampling from the approximated distribution. In this work, we e...