  The visual system evolved to process natural scenes, yet most of our
understanding of the topology and function of visual cortex derives from
studies using artificial stimuli. To gain deeper insights into visual
processing of natural scenes, we utilized widefield calcium-imaging of primate
V4 in response to many natural images, generating a large dataset of
columnar-scale responses. We used this dataset to build a digital twin of V4
via deep learning, generating a detailed topographical map of natural image
preferences at each cortical position. The map revealed clustered functional
domains for specific classes of natural image features. These ranged from
surface-related attributes like color and texture to shape-related features
such as edges, curvature, and facial features. We validated the model-predicted
domains with additional widefield calcium-imaging and single-cell resolution
two-photon imaging. Our study illuminates the detailed topological organization
and neural codes in V4 that represent natural scenes.
