Probing learning through the lens of changes in circuit dynamics

Despite the success of dynamical systems as accounts of circuit computation and observed behavior, our understanding of how dynamical systems evolve over learning is very limited. Here we develop a computational framework for extracting core dynamical systems features of recurrent circuits across learning and analyze the properties of these meta-dynamics in model analogues of several brain-relevant tasks. Across learning algorithms and tasks we find a stereotyped path to task mastery, which involves the creation of dynamical systems features and their refinement to a stable solution. This learning universality reveals common principles in the organization of recurrent neural networks in service to function and highlights some of the challenges in reverse engineering learning principles from chronic population recordings of neural activity.