Convergence and Segregation of the Multiple Rod Pathways in Mammalian Retina
Bibliographic record
Abstract
Using a multidisciplinary approach, we demonstrate that three different pathways are responsible for the transmission of rod signals across the mouse retina. Each pathway serves a primarily nonoverlapping range of stimulus intensities, with ganglion cells receiving either segregated or convergent inputs. For both on-center (ON) and off-center (OFF) ganglion cells, the primary rod pathway carries signals with the lowest threshold, whereas the secondary rod pathway is less sensitive by approximately 1 log unit. In addition, OFF signaling uses a tertiary rod pathway that is approximately 1 log unit less sensitive than the secondary. Although some ganglion cells received rod inputs exclusively from one of the pathways, others showed convergent inputs. Using pharmacological and genetic approaches, we defined classes of ON and OFF ganglion cells for which the scotopic inputs derive only from the primary pathway or from both primary and secondary pathways. In addition, we observed a class of OFF ganglion cell receiving mixed input from primary and tertiary pathways. Interestingly, OFF ganglion cells receiving convergent inputs from all three rod pathways or from the secondary and tertiary pathways together were never observed. Overall, our data show a complex arrangement of convergence and segregation of rod inputs to ganglion cells in the mammalian retina.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".