Neural Circuits Underlying Multifeature Extraction in the Retina
Bibliographic record
Abstract
Classic ON-OFF direction-selective ganglion cells (DSGCs) that encode the four cardinal directions were recently shown to also be orientation-selective. To clarify the mechanisms underlying orientation selectivity, we employed a variety of electrophysiological, optogenetic, and gene knock-out strategies to test the relative contributions of glutamate, GABA, and acetylcholine (ACh) input that are known to drive DSGCs, in male and female mouse retinas. Extracellular spike recordings revealed that DSGCs respond preferentially to either vertical or horizontal bars, those that are perpendicular to their preferred-null motion axes. By contrast, the glutamate input to all four DSGC types measured using whole-cell patch-clamp techniques was found to be tuned along the vertical axis. Tuned glutamatergic excitation was heavily reliant on type 5A bipolar cells, which appear to be electrically coupled via connexin 36 containing gap junctions to the vertically oriented processes of wide-field amacrine cells. Vertically tuned inputs are transformed by the GABAergic/cholinergic "starburst" amacrine cells (SACs), which are critical components of the direction-selective circuit, into distinct patterns of inhibition and excitation. Feed-forward SAC inhibition appears to "veto" preferred orientation glutamate excitation in dorsal/ventral (but not nasal/temporal) coding DSGCs "flipping" their orientation tuning by 90° and accounts for the apparent mismatch between glutamate input tuning and the DSGC's spiking response. Together, these results reveal how two distinct synaptic motifs interact to generate complex feature selectivity, shedding light on the intricate circuitry that underlies visual processing in the 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) | 0.000 | 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".