Attractor Network

Attractor networks are computational models inspired by neuroscience, aiming to represent and process information through stable states (attractors) in a dynamical system. Current research focuses on applying attractor network principles to improve sequential memory, enhance neural network architectures like transformers, and optimize gradient descent algorithms for faster convergence. These advancements are impacting diverse fields, including robotics (e.g., prosthetic control), speech processing (e.g., diarization), and time series forecasting, by enabling more efficient, robust, and biologically plausible models.