Representations in the hippocampal-entorhinal system emerge from learning sensory predictions
Bibliographic record
Abstract
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 .
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".