Schema formation in a neural population subspace underlies learning-to-learn in flexible sensorimotor problem-solving.

ABSTRACT: Learning-to-learn, a progressive speedup of learning while solving a series of similar problems, represents a core process of knowledge acquisition that draws attention in both neuroscience and artificial intelligence. To investigate its underlying brain mechanism, we trained a recurrent neural network model on arbitrary sensorimotor mappings known to depend on the prefrontal cortex. The network displayed an exponential time course of accelerated learning. The neural substrate of a schema emerges within a low-dimensional subspace of population activity; its reuse in new problems facilitates learning by limiting connection weight changes. Our work highlights the weight-driven modifications of the vector field, which determines the population trajectory of a recurrent network and behavior. S

SUBMITTER: Goudar V 

PROVIDER: S-EPMC11559441 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature