Similarities and differences between stimulus tuning in the\n inferotemporal visual cortex and convolutional networks
Bibliographic record
Abstract
Deep convolutional neural networks (CNNs) trained for object classification\nhave a number of striking similarities with the primate ventral visual stream.\nIn particular, activity in early, intermediate, and late layers is closely\nrelated to activity in V1, V4, and the inferotemporal cortex (IT). This study\nfurther compares activity in late layers of object-classification CNNs to\nactivity patterns reported in the IT electrophysiology literature. There are a\nnumber of close similarities, including the distributions of population\nresponse sparseness across stimuli, and the distribution of size tuning\nbandwidth. Statisics of scale invariance, responses to clutter and occlusion,\nand orientation tuning are less similar. Statistics of object selectivity are\nquite different. These results agree with recent studies that highlight strong\nparallels between object-categorization CNNs and the ventral stream, and also\nhighlight differences that could perhaps be reduced in future CNNs.\n
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".