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.