Divisive Normalization Predicts Adaptation-Induced Response Changes in Macaque Inferior Temporal Cortex

Stimulus repetition alters neural responses to the repeated stimulus. This so-called adaptation phenomenon has been commonly observed at multiple spatial and temporal scales and in different brain areas, and has been hypothesized to affect the neural representation of the sensory input. Yet, the neural mechanisms underlying adaptation still remain unclear, especially in higher-order cortical areas. Here we employ a divisive normalization model of neural responses to predict adaptation-induced changes in responses of single neurons in the macaque inferior temporal (IT) cortex. According to this model, the response of a neuron is determined by an interplay between its direct excitatory and divisive normalizing inputs, with each input being subject to adaptation. To test the model, we recorded the responses of single IT cortex neurons to complex visual stimuli while separately adapting the two putative types of input to those neurons. We compared the changes in responses of these neurons following such adaptation with predictions derived from the divisive normalization model. As predicted by the model, we show that adaptation in the IT cortex can, depending on the relative strength of each putative type of input to a neuron, suppress or enhance the neural response to a complex stimulus. More generally, our data suggest that adaptation serves to selectively enhance processing of the stimuli that differ from recently experienced ones, even when these occur within a configuration of multiple stimuli.