Cell-type specific population codes link inferior temporal cortex to object recognition behavior

Abstract

A 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 remains poorly understood. 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 their contribution to object recognition behavior) than Inh neurons. Beyond, refining the linking propositions between IT neurons and object recognition behavior, our results guide the development of next-generation biologically constrained brain models by offering novel cell-type specific neural benchmarks.