Distinct roles of putative excitatory and inhibitory neurons in the macaque inferior temporal cortex in core object recognition behavior

Abstract

AbstractA spatially distributed population of neurons in the macaque inferior temporal (IT) cortex supports object recognition behavior, but the cell-type specificity of the population in forming “behaviorally sufficient” object decodes remain unclear. To address this, we recorded neural signals from the macaque IT cortex and compared the object identity information and the alignment of decoding strategies derived from putative inhibitory (Inh) and excitatory (Exc) neurons to the monkeys’ behavior. We observed that while Inh neurons represented significant category information, decoding strategies based on Exc neural population activity outperformed those from Inh neurons in overall accuracy and their image-level match to the monkeys’ behavioral reports. Interestingly, both Exc and Inh responses explained a fraction of unique variance of the monkeys’ behavior, demonstrating a distinct role of the two cell types in generating object identity solutions for a downstream readout. We observed that current artificial neural network (ANN) models of primate ventral stream, designed with AI goals of performance optimization on image categorization, better predict Exc neurons (and its contribution to object recognition behavior) than Inh neurons. Beyond, the refinement of linking propositions between IT and object recognition behavior, our results guide the development of more biologically constrained brain models by offering novel cell-type specific neural benchmarks.