Extent to which transient-dynamics-based computation can support learning

Determine the extent to which computing with transients—i.e., transient trajectory-based computation away from steady-state attractors—can incorporate learning, particularly on-the-fly (online) learning and life-long learning, in biological and artificial systems.

Background

The paper argues that attractor-based computational frameworks are insufficient to explain real-time, flexible, and robust information processing in living systems. Instead, it advocates computation based on transient dynamics away from attractors, highlighting mechanisms such as ghost states at criticality that can provide quasi-stable working memory and responsiveness to time-varying inputs.

Building on this perspective, the authors note that a comprehensive framework for natural computation must also encompass learning—especially online and life-long learning. While they cite evidence suggesting quasi-stable transient structures (e.g., slow points/ghosts) may support learning in artificial and natural neural systems, they emphasize that the general extent and mechanisms by which transient-based computation enables learning remain unresolved.