Faculty Opinions recommendation of Light increases the gap junctional coupling of retinal ganglion cells.
Bibliographic record
Abstract
We examined the effect of light adaptation on the gap junctional coupling of α-ganglion cells (α-GCs) in rabbit and mouse retinas. We assayed changes in coupling by measuring parameters of tracer coupling following injection of α-GCs with Neurobiotin and the concerted spike activity of α-GC neighbours under dark- and light-adapted conditions. We found that light adaptation using mesopic or photopic background lights resulted in a dramatic increase in the labelling intensity, number, and spatial extent of ganglion and amacrine cells coupled to OFF α-GCs when compared to levels seen under dark adaptation. While this augmentation of coupling by light did not produce an increase in the concerted spontaneous activity of OFF α-GC neighbours, it did significantly increase correlated light-evoked spiking. This was seen as an increase in the number of correlated spikes for α-GC neighbours and an extension of correlations to second-tier neighbours that was not seen under dark-adapted conditions. Pharmacological studies in the rabbit retina indicated that dopamine mediates the observed changes in coupling by differentially activating D1 and D2 receptors under different adaptation states. In this scheme, activation of dopamine D1 receptors following light exposure triggers cAMP-mediated intracellular pathways resulting in an increase in gap junctional conductance. Overall, our results indicate that as we move from night to day there is an enhanced electrical coupling between α-GCs, thereby increasing the concerted activity believed to strengthen the capacity and efficiency of information flow across the optic nerve. PMID: 20819943 Funding information This work was supported by: NEI NIH HHS, United States Grant ID: EY017832 NEI NIH HHS, United States Grant ID: EY007360 NEI NIH HHS, United States Grant ID: R01 EY017832 NEI NIH HHS, United States Grant ID: R01 EY007360
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.493 | 0.191 |
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".