AB015. Interocular normalization in monkey primary visual cortex (V1)
Bibliographic record
Abstract
Background: The two images, slightly different, seen by the two eyes allow the brain to build a 3D representation of the world. Monocular signals enter the primary visual cortex through layer 4, where they are segregated and organized in ocular dominance stripes. They are later combined in upper layers. In order to study the integration of the information coming from the two eyes at this mesoscopical scale in V1, we use optical imaging in anaesthetized macaque monkey. Methods: Ocular dominance maps have been obtained with intrinsic optical imaging. Dichoptic interactions have then been studied with voltage-sensitive dye imaging (VSDI) with a frequency-tagging paradigm. Visual stimuli with different contrasts were respectively presented at 6 and 10 Hz to the two eyes, independently or simultaneously with a passive 3D screen. Frequency analysis thus allowed to identify each eye’s contribution to the signal. Results: We observed that V1 population activity generated by one eye stimulation is suppressed when the other eye is stimulated too. This integration of monocular signals at the population level can be accurately modeled with an interocular normalization model. Conclusions: This approach and this model confirm V1 implication in combining the signals coming from the two eyes. The mechanisms underlying this interocular normalization, through local, feedforward, feedback or long-range connections, are still to be determined.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".