A large calcium-imaging dataset reveals a systematic V4 organization for natural scenes
This provides insights into neural coding for natural vision, addressing a gap in neuroscience, but is incremental as it builds on existing methods to map V4 organization.
The study tackled the lack of understanding of visual cortex organization for natural scenes by creating a large calcium-imaging dataset of primate V4 responses to natural images, revealing clustered functional domains for features like color, texture, and facial features through a deep learning model.
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.