Representations in the hippocampal-entorhinal system emerge from learning sensory predictions

The hippocampal formation and adjacent parahippocampal areas are central to intelligent behaviour such as memory and navigation. Understanding how systems in the brain generate structured representations from experience remains a fundamental goal in neuroscience. A central open question is whether a single computational principle can account for the diverse neural responses observed across the hippocampal-entorhinal circuit. Existing models often rely on hand-crafted features or specialized learning mechanisms unrelated to sensory observations, and typically express representations of only a small subset of known cell types. Further, representations learned in such models are often not empirically evaluated against neural representations observed in the navigating brain. Here, we introduce a neurobiologically-inspired and robust computational model in which diverse cell types emerge from a single learning objective with minimal hand-engineered assumptions. Our model applies contrastive graph representation learning to transitions between high-dimensional visual observations, constructing a metric space in which temporally adjacent sensory observations are mapped to nearby states. Inspired by the anatomical information flow of the hippocampal-entorhinal system, and anchored in output representations based on neural coding in the entorhinal cortex, the model gives rise to activity resembling place cells, grid cells, boundary vector cells, band cells, corner cells, and conjunctive cells among others. Across varied environments and sensory streams, the framework captures not only diverse neural response patterns but also the functional dependencies between them, mirroring the proposed sequential representational structure observed in the hippocampal-entorhinal system. Crucially, place-cell-like features of the model quantitatively reproduce remapping dynamics observed in CA1 of freely moving animals, and afford theoretical explanatory power of existing neurobiologically-informed models. This work thus offers a unified computational model of spatial coding in the hippocampal-entorhinal system and a testable framework for generating mechanistic hypotheses in silico, to be evaluated in vivo.