Posttraining Ablation of Adult-Generated Neurons Degrades Previously Acquired Memories
Bibliographic record
Abstract
New neurons are continuously generated in the subgranular zone of the adult hippocampus and, once sufficiently mature, are thought to integrate into hippocampal memory circuits. However, whether they play an essential role in subsequent memory expression is not known. Previous studies have shown that suppression of adult neurogenesis often (but not always) impairs subsequent hippocampus-dependent learning (i.e., produces anterograde effects). A major challenge for these studies is that these new neurons represent only a small subpopulation of all dentate granule cells, and so there is large potential for either partial or complete compensation by granule cells generated earlier on during development. A potentially more powerful approach to investigate this question would be to ablate adult-generated neurons after they have already become part of a memory trace (i.e., retrograde effects). Here we developed a diphtheria toxin-based strategy in mice that allowed us to selectively ablate a population of predominantly mature, adult-generated neurons either before or after learning, without affecting ongoing neurogenesis. Removal of these neurons before learning did not prevent the formation of new contextual fear or water maze memories. In contrast, removal of an equivalent population after learning degraded existing contextual fear and water maze memories, without affecting nonhippocampal memory. Ablation of these adult-generated neurons even 1 month after learning produced equivalent memory degradation in the water maze. These retrograde effects suggest that adult-generated neurons form a critical and enduring component of hippocampal memory traces.
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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 | 0.000 | 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".