Genetic Control of Circuit Function:<i>Vsx1</i>and<i>Irx5</i>Transcription Factors Regulate Contrast Adaptation in the Mouse Retina
Bibliographic record
Abstract
Transcriptional programs guide the specification of neural cell types in the developing nervous system. However, it is unclear whether such programs also control specific aspects of neural circuit function at maturity. In the mammalian retina, Vsx1 and Irx5 transcription factors are present in a subset of bipolar interneurons that convey signals from photoreceptors to ganglion cells. The biased expression of Vsx1 and Irx5 in hyperpolarizing OFF compared with depolarizing ON bipolar cells suggests that these transcription factors may selectively regulate signal processing in OFF circuits. To test this hypothesis, we generated mice lacking both Vsx1 and Irx5. Bipolar cells in these mice were morphologically normal, but the expression of cell-specific markers in some OFF but not ON bipolar cells was reduced or absent. To assess visual function in Vsx1(-/-)Irx5(-/-) retinas, we recorded light responses from ensembles of retinal ganglion cells (RGCs). We first identified functional RGC types in control mice and describe their response properties and adaptation to temporal contrast using a simple linear-nonlinear model. We found that space-time receptive fields of RGCs are unchanged in Vsx1(-/-)Irx5(-/-) mice compared with control retinas. In contrast, response threshold, gain, and range were lowered in a cell-type-specific manner in OFF but not ON RGCs in Vsx1(-/-)Irx5(-/-) retinas. Finally, we discovered that the ability to adapt to temporal contrast is greatly reduced in OFF RGCs in the double mutant, suggesting that Vsx1 and Irx5 control specific aspects of visual function in circuits of the mammalian 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| 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".