Parallel channels for motion feature extraction in the pretectum and tectum of larval zebrafish
Bibliographic record
Abstract
Abstract Non-cortical visual areas in vertebrate brains extract different stimulus features, such as motion, object size and location, to support behavioural tasks. The optic tectum and pretectum, two primary visual areas, are thought to fulfil complementary biological functions in zebrafish to support prey capture and optomotor stabilisation behaviour. However, the adaptations of these brain areas to behaviourally relevant stimulus statistics are unknown. Here, we used calcium imaging to characterize the receptive fields of 1,926 motion-sensitive neurons in diencephalon and midbrain. We show that many caudal pretectal neurons have large receptive fields (RFs), whereas RFs of tectal neurons are smaller and mostly size-selective. RF centres of large-size RF neurons in the pretectum are predominantly located in the lower visual field, while tectal neurons sample the upper-nasal visual field more densely. This tectal visual field sampling matches the expected prey item locations, suggesting that the tectal magnification of the upper-nasal visual field might be an adaptation to hunting behaviour. Finally, we probed optomotor responsiveness and found that even relatively small stimuli drive optomotor swimming, if presented in the lower-temporal visual field, suggesting that the pretectum preferably samples information from this region on the ground to inform optomotor behaviour. Our characterization of the parallel processing channels for non-cortical motion feature extraction provides a basis for further investigation into the sensorimotor transformations of the zebrafish brain and its adaptations to habitat and lifestyle.
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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".