Predictive coding is a neural learning framework in which hierarchical layers continually generate predictions and update themselves through local prediction-error dynamics. Within this picture, learning is not treated as a single monolithic end-to-end adjustment, but as an iterative inferential process in which latent states and parameters are refined through structured exchanges of top-down prediction and bottom-up error.
Predictive and causal coding work with information-geometric principles and commutator relationships to shape how learning propagates through the system: local influence estimates, mixed-curvature structure, and small-commutator dynamics help determine where updates should go, where they should not go, and how modular competence can be preserved under continual adaptation.
Recent formulations develop this further through a two-level architecture in which Bayesian routing governs which columns or modules should be active, reused, or forked, while predictive-coding microstructures within those modules are kept internally coherent through pruning, inhibition, and shell-based consolidation. In this form, predictive and causal coding are not merely neural techniques but a broader strategy for scalable neural-symbolic integration, offering a principled account of how sub-symbolic learning dynamics, modular reuse, transfer, and emergent higher-level structure may be coordinated across the Hyperon stack.