Putative excitatory and inhibitory neurons in the macaque inferior temporal cortex play distinct roles in core object recognition
Bibliographic record
Abstract
Distributed neural population activity in the macaque inferior temporal (IT) cortex, which lies at the apex of the visual ventral stream hierarchy, is critical in supporting an array of object recognition behavior. Previous research, however, has been agnostic to the relevance of specific cell types, inhibitory vs. excitatory, in the formation of "behaviorally sufficient" IT population codes that can accurately predict primate object confusion patterns. Therefore, here, we first compared the strength of behavioral predictions of neural decoding ("readout") models constructed from specific (putative) cell types in the IT cortex. We performed large-scale neural recordings while monkeys (n=3) fixated images (640) presented (100ms) in their central (8 degrees) field of view. Monkeys (n=3) also performed binary object discrimination tasks (8 objects; 640 images; 28 binary tasks). We performed PCA (and spike shape) based spike sorting analysis to categorize the recorded neural signals into two groups: broad-spiking (104; putative excitatory) and narrow-spiking (33; putative inhibitory) neurons. We observed that decoding strategies (205 linking hypotheses tested) derived from excitatory neurons significantly outperform those produced by inhibitory neurons in overall accuracy and image-by-image match to monkey behavioral patterns. Given that current artificial neural network (ANN) models of the ventral stream (as documented in Brain-Score) explain ~50% of macaque IT neural variance and produce human-like accuracies in object recognition tasks, we compared their predictions of putative excitatory (Exc) vs. inhibitory (Inh) IT neurons. Interestingly, we observed that ANNs predict Exc neurons significantly better than Inh neurons (Exc-Inh = 10%; p<0.0001). Taken together, the correlative evidence for cell-type specificity in the linkage between IT population activity and object recognition behavior, along with the novel cell-type specific benchmarks (that disrupt the current Brain-Score ranking of the encoding models for macaque IT), provides valuable guidance for the next generation of more refined brain models.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".