Dynamic predictive coding: A model of hierarchical sequence learning and prediction in the neocortex.

ABSTRACT: We introduce dynamic predictive coding, a hierarchical model of spatiotemporal prediction and sequence learning in the neocortex. The model assumes that higher cortical levels modulate the temporal dynamics of lower levels, correcting their predictions of dynamics using prediction errors. As a result, lower levels form representations that encode sequences at shorter timescales (e.g., a single step) while higher levels form representations that encode sequences at longer timescales (e.g., an entire sequence). We tested this model using a two-level neural network, where the top-down modulation creates low-dimensional combinations of a set of learned temporal dynamics to explain input sequences. When trained on natural videos, the lower-level model neurons developed space-time receptive fiel

SUBMITTER: Jiang LP 

PROVIDER: S-EPMC10880975 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature