How Stimulus Statistics Affect The Receptive Fields of V1 Cells

Abstract

Abstract We studied the changes that neuronal RF models undergo when the statistics of the stimulus are changed from those of white Gaussian noise (WGN) to those of natural scenes (NS). Fitting the model to data estimates both a cascade of linear filters on the stimulus, as wells 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 NS than to WGN. The most striking finding was that NS resulted in RFs that had additional uncovered filters than did WGN. This finding was not an artefact of the higher spike rates but rather related to a change in coding. Our results reveal a greater extent of nonlinear processing in V1 neurons when stimulated using NS compared to WGN. Our findings indicate the existence of nonlinear mechanisms that endow V1 neurons with context-dependent transmission of visual information.