Neural responses to natural and model-matched stimuli reveal distinct computations in primary and non-primary auditory cortex

Abstract

A central goal of sensory neuroscience is to construct models that can explain neural responses to complex, natural stimuli. As a consequence, sensory models are often tested by comparing neural responses to natural stimuli with model responses to those stimuli. One challenge is that distinct model features are often correlated across natural stimuli, and thus model features can predict neural responses even if they do not in fact drive them. Here we propose a simple alternative for testing a sensory model: we synthesize stimuli that yield the same model response as a natural stimulus, and test whether the natural and \"model-matched\" stimulus elicit the same neural response. We used this approach to test whether a common model of auditory cortex - in which spectrogram-like peripheral input is processed by linear spectrotemporal filters - can explain fMRI responses in humans to natural sounds. Prior studies have that shown that this model has good predictive power throughout auditory cortex, but this finding could reflect stimulus-driven correlations. We observed that fMRI voxel responses to natural and model-matched stimuli were nearly equivalent in primary auditory cortex, but that non-primary regions showed highly divergent responses to the two sound sets, suggesting that neurons in non-primary regions extract higher-order properties not made explicit by traditional models. This dissociation between primary and non-primary regions was not clear from model predictions due to the influence of stimulus-driven response correlations. Our methodology enables stronger tests of sensory models and could be broadly applied in other domains.\n\nAuthor SummaryModeling neural responses to natural stimuli is a core goal of sensory neuroscience. Here we propose a new approach for testing sensory models: we synthesize a \"model-matched\" stimulus that yields the same model response as a natural stimulus, and test whether it produces the same neural response. We used model-matching to test whether a standard model of auditory cortex can explain human cortical responses measured with fMRI. Model-matched stimuli produced nearly equivalent voxel responses in primary auditory cortex, but highly divergent responses in non-primary regions. This dissociation was not evident using more standard approaches for model testing, and suggests that non-primary regions compute higher-order stimulus properties not captured by traditional models. The methodology could be broadly applied in other domains.