Do Topographic Deep ANN Models of the Primate Ventral Stream Predict the Perceptual Effects of Direct IT Cortical Interventions?
Bibliographic record
Abstract
Ever-advancing artificial neural network (ANN) models of the ventral visual stream capture core object recognition behavior and the neural mechanisms underlying it with increasing precision. These models take images as input, propagate through simulated neural representations that resemble biological neural representations at all stages of the primate ventral stream, and produce simulated behavioral choices that resemble primate behavioral choices. We here extend this modeling approach to make and test predictions of neural intervention experiments. Specifically, we enable a new prediction regime for topographic deep ANN (TDANN) models of primate visual processing through the development of perturbation modules that translate micro-stimulation, optogenetic suppression, and muscimol suppression into changes in model neural activity . This unlocks the ability to predict the behavioral effects from particular neural perturbations. We compare these predictions with the key results from the primate IT perturbation experimental literature via a suite of nine corresponding benchmarks. Without any fitting to the benchmarks, we find that TDANN models generated via co-training with both a spatial correlation loss and a standard categorization task qualitatively predict all nine behavioral results. In contrast, TDANN models generated via random topography or via topographic unit arrangement after classification training predict less than half of those results. However, the models’ quantitative predictions are consistently misaligned with experimental data, over-predicting the magnitude of some behavioral effects and under-predicting others. None of the TDANN models were built with separate model hemispheres and thus, unsurprisingly, all fail to predict hemispheric-dependent effects. Taken together, these findings indicate that current topographic deep ANN models paired with perturbation modules are reasonable guides to predict the qualitative results of direct causal experiments in IT, but that improved TDANN models will be needed for precise quantitative predictions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".