Neurogenesis-dependent transformation of hippocampal memory traces during systems consolidation
Bibliographic record
Abstract
Summary Memories for events (i.e., episodic memories) change qualitatively with time. Systems consolidation theories posit that organizational changes accompany qualitative shifts in memory resolution, but differ as to the locus of this reorganization. Whereas some theories favor inter-regional changes in organization (e.g., hippocampus→cortex; multiple trace theory), others favor intra-regional reorganization (e.g., within-hippocampus; trace transformation theory). Using an engram tagging and manipulation approach in mice, here we provide evidence that intra-regional changes in organization underlie shifts in memory resolution. We establish that contextual fear memories lose resolution as a function of time, with mice exhibiting conditioned freezing in both the training apparatus (context A) and a second apparatus (context B) at more remote delays (freezing A ≡ freezing B at remote delay). By tagging either hippocampal (dentate gyrus) or cortical (prelimbic cortex) neuronal ensembles in context A, and then pairing their optogenetic activation with shock (in context C), we monitored the resolution of these artificially-generated memories at recent versus remote post-conditioning delays by testing mice in contexts A and B. Hippocampal engrams for a fear conditioning event were initially high-resolution (recent delay: freezing A >> freezing B ) but lost resolution with time (remote delay: freezing A ≡ freezing B ). In contrast, cortical engrams were initially low-resolution and remained low-resolution over time (recent and remote delay: freezing A ≡ freezing B ). Transformation of hippocampal engrams was dependent on adult hippocampal neurogenesis. Eliminating hippocampal neurogenesis arrested hippocampal engrams in a recent-like, high-resolution state where mice continued to exhibit discriminative freezing at remote delays. Graphical abstract
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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".