Understanding the hippocampus as an apex of the cortical hierarchy and self-supervised predictive learning engine

The mammalian cortex is organized along hierarchical gradients that extend from primary sensory regions to transmodal association networks. Converging neuroanatomical theory and data-driven analyses place the hippocampus at the apex of this hierarchy, where its subregional organization mirrors large-scale cortical networks and their evolutionary expansion. Building on these observations, we propose that the hippocampus functions as a predictive learning engine, generating latent training signals that support cortical learning. This view aligns with self-supervised machine learning frameworks, in which predictive processes occupy the top of hierarchical models. We suggest that hippocampal predictive learning constitutes a foundational mechanism of mammalian intelligence, linking cortical organization with principles underlying modern artificial systems.