Abstract
Neurons in the inferotemporal (IT) cortex respond selectively to complex visual features, implying their role in object perception. However, perception is subjective and cannot be read out from neural responses; thus, bridging the causal gap between neural activity and perception demands independent characterization of perception. Historically though, the complexity of the perceptual alterations induced by artificial stimulation of IT cortex has rendered them impossible to quantify. Here we addressed this old problem by combining machine learning with high-throughput behavioral optogenetics in macaque monkeys. In closed-loop experiments, we generated complex and highly specific images that the animal could not discriminate from the state of being cortically stimulated. These images, named “perceptograms” for the first time, reveal and depict the contents of the complex hallucinatory percepts induced by local neural perturbation in IT cortex. Furthermore, we found that the nature and magnitude of these hallucinations highly depend on concurrent visual input, stimulation location, and intensity. Objective characterization of stimulation-induced perceptual events opens the door to developing a mechanistic theory of visual perception. Further, it enables us to make better visual prosthetic devices and gain a greater understanding of visual hallucinations in mental disorders.
One-Sentence Summary Combining state-of-the-art AI with high-throughput closed-loop brain stimulation experiments, for the first time, we took “pictures” of the complex and subjective visual hallucinations induced by local stimulation in the inferior temporal cortex, a cortical area associated with object recognition.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
The abstract and the sub-categories are changed in this version