Local prediction-learning in high-dimensional spaces enables neural networks to plan.

ABSTRACT: Planning and problem solving are cornerstones of higher brain function. But we do not know how the brain does that. We show that learning of a suitable cognitive map of the problem space suffices. Furthermore, this can be reduced to learning to predict the next observation through local synaptic plasticity. Importantly, the resulting cognitive map encodes relations between actions and observations, and its emergent high-dimensional geometry provides a sense of direction for reaching distant goals. This quasi-Euclidean sense of direction provides a simple heuristic for online planning that works almost as well as the best offline planning algorithms from AI. If the problem space is a physical space, this method automatically extracts structural regularities from the sequence of observations

SUBMITTER: Stockl C 

PROVIDER: S-EPMC10943103 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature