Topic: persistent learning signals

Attractor dynamics are essential components of neural computation— they have given rise to elegant, interpretable models of population coding and working memory. Frequently, attractors are classified based on whether their invariant manifolds can represent discrete/continuous variables, with point attractors being an example of the former and ‘continuous attractors’ of the latter. Here we highlight that ‘learnability’ is another quality that may serve to differentiate neural dynamics while offering novel insight. Counterintuitively, good working memory does not necessarily imply that a well-de