Bibliographic record
Abstract
Early experimental work suggested that the retina’s main role was to detect\nchanges in brightness and contrast, namely working as a light detector, and that most of\nthe complex computations in the visual system happened upstream in the brain. In reality, there is a growing wealth of literature indicating that the retina itself processes multiple channels of visual information (contrast, motion, orientation, etc.), making it much more complex than it originally appeared. For instance, there now appear to be over 20 types of retinal ganglion cells. To this end, the work in this thesis will focus on the identification and characterization of a single type of retinal ganglion cell in the mouse retina. In the first section of my results, I will show that this cell type, identified as the only GFP+ ganglion cell in the transgenic Hb9::eGFP retina, is a directionally selective ganglion cell (DSGC), that preferentially responds to objects moving upward through the visual field. This cell has a pronounced morphological asymmetry that helps it to synergistically (along with asymmetric inhibition) generate directionally selective responses. In the second results section, I will describe a novel phenomenon exhibited by Hb9+ DSGCs: Thanks to gap junction mediated signals, Hb9+ cells are able to anticipate moving stimuli and correct for lags that are inherent in visual signals generated by photoreceptors. In the third results section I will elucidate the mechanisms for the gap junction mediated anticipatory signals outlined in the second results section. Together, these results provide a significant advancement in our understanding of how the retina processes moving stimuli and provide a compelling example of how chemical and electrical synapses interact to allow for exquisite signal multiplexing.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".