Large-scale two-photon imaging revealed super-sparse population codes in V1 superficial layer of awake monkeys
Bibliographic record
Abstract
Abstract Efficient coding has been proposed as a general principle for the sensory systems. The efficient coding hypothesis predicts that neuronal population responses should be sparse, but limited by the measurement techniques, the precise estimates of the population sparseness of visual cortical neurons are still uncertain. Here, we employed large-scale two-photon calcium imaging to examine the neuronal population activities in V1 superficial layers of awake macaques in response to a large set of natural images. We found that only 0.5% of these neurons on average responded strongly to any given natural image with response strength above half of their individual peak responses, which is more than tenfold sparse over those reported by early studies. We further showed that these sparse population activities contain sufficient information for discriminating images with high accuracy. This study provided the first accurate measure of sparseness in V1 neuronal population responses, which support super-sparse neural codes in primates.
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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 | 0.001 |
| 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 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".