Behaviorally emergent hippocampal place maps remain stable during memory recall
Bibliographic record
Abstract
Summary The hippocampus is critical for the formation and recall of episodic memories 1, 2 which store past experience of events (‘what’) occurring at particular locations (‘where’) in time (‘when’). Hippocampal place cells, pyramidal neurons which show location-specific modulation of firing rates during navigation 3, 4 , together form a spatial representation of the environment. It has long been hypothesized that place cells serve as the neural substrate for long-term episodic memory of space 5, 6 . However, recent studies call to question this tenet of the field by demonstrating unexpected levels of representational drift in hippocampal place cells with respect to the duration of episodic memories in mice 7, 8 . In the present study, we examined behaviorally driven long-term organization of the place map, to resolve the relationship between memory and place cells. Leveraging the stability of two-photon calcium imaging, we tracked activity of the same set of CA1 pyramidal neurons during learning and memory recall in an operant, head-fixed, odorcued spatial navigation task. We found that place cells are rapidly recruited into task-dependent spatial maps, resulting in emergence of orthogonal as well as overlapping representations of space. Further, task-selective place cells used a diverse set of remapping strategies to represent changing task demands that accompany learning. We found behavioral performance dependent divergence of spatial maps between trial types occurs during learning. Finally, imaging during remote recall spanning up to 30 days revealed increased stabilization of learnt place cell maps following memory consolidation. Our findings suggest that a subset of place cells is recruited by rule based spatial learning, actively reconfigured to represent task-relevant spatial relationships, and stabilized following successful learning and consolidation.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".