Models of primate ventral stream that categorize and visualize images
Bibliographic record
Abstract
Abstract An open question in systems neuroscience is which objective function (or computational “goal”) best describes the computations performed by the ventral stream (VS) of primate visual cortex. Substantial past research has suggested that object categorization could be such a goal. Recent experiments, however, showed that information about object positions, sizes, etc. is encoded with increasing explicitness along this pathway. Because that information is not necessarily needed for object categorization, this motivated us to ask whether primate VS may do more than “just” object recognition. To address that question, we trained deep neural networks, all with the same architecture, with three different objectives: a supervised object categorization objective; an unsupervised autoencoder objective; and a semi-supervised objective that combined autoencoding with categorization. We then compared the image representations learned by these models to those observed in areas V4 and IT of macaque monkeys using canonical correlation analysis (CCA). We found that the semi-supervised model provided the best match the monkey data, followed closely by the unsupervised model, and more distantly by the supervised one. These results suggest that multiple objectives – including, critically, unsupervised ones – might be essential for explaining the computations performed by primate VS.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".