The role of adult hippocampal neurogenesis in reducing interference.
Bibliographic record
Abstract
Adult neurogenesis in the hippocampal dentate gyrus plays an important role in learning and memory. However, the precise contribution of the new neurons to hippocampal function remains controversial. Emerging evidence suggests that neurogenesis is important for pattern separation and for mitigating interference when similar items must be learned at different times. In the present study, we directly test this prediction using a recently developed olfactory memory task that has those specific features. In this task, rats learn two highly interfering lists of odor pairs, one after the other, in either the same or in different contexts. Consistent with our hypothesis, focal cranial irradiation, resulting in selective reduction of neurogenesis within the dentate gyrus, significantly impaired the ability to overcome interference during learning of the second list. The ability to learn a single odor list was unimpaired. We also show that irradiation had no effect on learning in a hippocampal-dependent spatial alternation task. Although both tasks involved learning interfering responses, the time course for learning the interfering items differed. Learning the interfering odor lists took place sequentially, over the course of several sessions, whereas learning the interfering spatial locations took place concurrently, within each session. Thus, the gradual addition of new neurons may have provided a pattern separation mechanism for the olfactory task but not for the maze task. These findings demonstrate a role for neurogenesis in resolving interference and they are consistent with models suggesting a critical role for neurogenesis in pattern separation.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".