Inhibition of cell proliferation in black‐capped chickadees suggests a role for neurogenesis in spatial learning
Bibliographic record
Abstract
Following development, the avian brain continues to produce neurons throughout adulthood, which functionally integrate throughout the telencephalon, including the hippocampus. In food-storing birds like the black-capped chickadee (Poecile atricapillus), new neurons incorporated into the hippocampus are hypothesized to play a role in spatial learning. Previous results on the relation between hippocampal neurogenesis and spatial learning, however, are correlational. In this study, we experimentally suppressed hippocampal neuronal recruitment and tested for subsequent effects on spatial learning in adult chickadees. After chickadees exhibited significant learning, we treated birds with daily injections of either saline or methylazoxymethanol (MAM), a toxin that suppresses cell proliferation in the brain and monitored subsequent spatial learning. MAM treatment significantly reduced cell proliferation around the lateral ventricles and neuronal recruitment in the hippocampus, measured using the cell birth marker bromodeoxyuridine. MAM-treated birds performed significantly worse than controls on the spatial learning task 12 days following the initiation of MAM treatment, a time when new neurons would begin functionally integrating into the hippocampus. This difference in learning, however, was limited to a single trial. MAM treatment did not affect any measure of body condition, suggesting learning impairments were not a product of non-specific adverse effects of MAM. This is the first evidence of a potential causal link between hippocampal neurogenesis and spatial learning in birds.
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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".