Probing the role of bypass connections in core object recognition by chemogenetic suppression of macaque V4 neurons
Bibliographic record
Abstract
The macaque ventral visual pathway is typically modeled as a series of hierarchically organized cortical areas that successively transform the retinal input into visual object-based linearly separable neural representations. The most behaviorally explicit form of this representation has been discovered in the macaque inferior temporal (IT) cortex. However, each stage of the ventral stream (e.g., areas V1, V2) projects to multiple other areas in the brain. In addition, many projections from early visual areas (e.g., V1, V2) bypass area V4 and connect directly to the IT cortex. Therefore, the underlying neural circuitry is far more complex than a feedforward architecture. Here I provide evidence for the functional relevance of such bypass connections during object recognition. I hypothesized that images that require fewer transformations to generate linearly separable object representations most benefit from bypass connections. This would allow fast object detection under time-sensitive decision-making like assessing predatory threats. Using deep convolutional neural networks (DCNNs), I categorized 10,000 images (10 objects, 1000 images/object) into two categories (with equal categorization performance at the final layer). The “slow-evolved” images required more transformations to reach their final object classification accuracy than the “fast-evolved” images. To test this hypothesis in the primate ventral stream, I expressed inhibitory DREADDs within a 5x5 mm subregion of the V4 cortex via multiple viral injections (AAV8-hSyn-hM4Di-mCherry; two macaques). I recorded from multi-electrode arrays implanted over the transfected V4 and downstream IT cortex while monkeys’ performed object discrimination tasks. Successful V4 neural suppression (~20%) ensured I could produce a partial lesion within the ventral stream hierarchy. Interestingly, I observed that monkeys’ accuracies were significantly higher for “fast-evolved” compared to “slow-evolved” images (only when the objects overlapped with the transfected V4 receptive field). These results suggest that bypass connections allow "fast-evolved" images to retain high object recognition accuracy despite a V4 lesion.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".