Morphological identification and systematic classification of mammalian retinal ganglion cells. I. Rabbit retinal ganglion cells
Bibliographic record
Abstract
Retinal ganglion cells (RGCs) convey visual signals to 50 regions of the brain. For reasons of interest and convenience, they constitute an excellent system for the study of brain structure and function. There is general agreement that, absent a complete "parts list," understanding how the nervous system processes information will remain an elusive goal. Recent studies indicate that there are 30-50 types of ganglion cell in mouse retina, whereas only a few years ago it was still written that mice and the more visually oriented lagomorphs had less than 20 types of RGC. More than 30 years ago, I estimated that rabbits have about 40 types of RGC. The present study indicates that this number is much too low. I have employed the old but powerful method of Golgi-impregnation to rabbit retina, studying the range of component neurons in this already well-studied retinal system. Close quantitative and qualitative analyses of 1,142 RGCs in 26 retinas take into account cell body and dendritic field size, level(s) of dendritic stratification in the retina's inner plexiform layer, and details of dendritic branching. Ninety-one morphologies are recognized. Of these, at least 32 can be correlated with physiologically studied RGCs, dye-injected for morphological analysis. It is unlikely that rabbits have 91 types of RGC, but is argued here that this number lies between 60 and 70. The present study provides a "yardstick" for measuring the output of future molecular studies that may be more definitive in fixing the number of RGC types in rabbit retina.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".