How Stimulus Statistics Affect the Receptive Fields of Cells in Primary Visual Cortex

Abstract

We studied the changes that neuronal receptive field (RF) models undergo when the statistics of the stimulus are changed from those of white Gaussian noise (WGN) to those of natural scenes (NSs), by fitting the models to multielectrode data recorded from primary visual cortex (V1) of female cats. This allowed the estimation of both a cascade of linear filters on the stimulus, as well as the static nonlinearities that map the output of the filters to the neuronal spike rates. We found that cells respond differently to these two classes of stimuli, with mostly higher spike rates and shorter response latencies to NSs than to WGN. The most striking finding was that NSs resulted in RFs that had additional uncovered filters compared with WGN. This finding was not an artifact of the higher spike rates observed for NSs relative to WGN, but rather was related to a change in coding. Our results reveal a greater extent of nonlinear processing in V1 neurons when stimulated using NSs compared with WGN. Our findings indicate the existence of nonlinear mechanisms that endow V1 neurons with context-dependent transmission of visual information.

SIGNIFICANCE STATEMENT This study addresses a fundamental question about the concept of the receptive field (RF): does the encoding of information depend on the context or statistical regularities of the stimulus type? We applied state-of-the-art RF modeling techniques to data collected from multielectrode recordings from cat visual cortex in response to two statistically distinct stimulus types: white Gaussian noise and natural scenes. We find significant differences between the RFs that emerge from our data-driven modeling. Natural scenes result in far more complex RFs that combine multiple features in the visual input. Our findings reveal that different regimes or modes of operation are at work in visual cortical processing depending on the information present in the visual input. The complexity of V1 neural coding appears to be dependent on the complexity of the stimulus. We believe this new finding will have interesting implications for our understanding of the efficient transmission of information in sensory systems, which is an integral assumption of many computational theories (e.g., efficient and predictive coding of sensory processing in the brain).