Homing and feral pigeons differ in hippocampal formation neuron size: a Golgi study
Bibliographic record
Abstract
Abstract Artificial selection for behavioural traits can significantly affect the anatomy of brain regions related to the behaviour under selection. The homing pigeon ( Columba livia ) is an example of how anatomical changes can arise from artificial selection. Compared with feral and other pigeon breeds, homing pigeons have a much higher density and number neurons in the hippocampal formation, a region important for spatial memory and homing. Neuron numbers and density are, however, only one component of a brain region’s processing capacity and whether hippocampal formation neuron size and morphology also differ remains unknown. Using Golgi staining and virtual microscopy, we reconstructed and quantified the size and morphology of neurons within the dorsomedial and dorsolateral regions of the hippocampal formation in homing and feral pigeons. While no significant differences were found in the size or morphology of dorsolateral neurons between the two breeds, homing pigeons had significantly smaller neurons (approximately 30% reduction in total volume and soma volume) in the dorsomedial region compared to feral pigeons. These findings suggest that smaller dorsomedial neurons in homing pigeons may facilitate increased neuronal packing density, potentially enhancing spatial resolution. How these differences in neuron size reflect behaviour in homing and feral pigeons has yet to be determined, but our results suggest that there may be behavioural and physiological differences in spatial cognition between the two breeds.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".