A large calcium-imaging dataset reveals a systematic V4 organization for natural scenes

TL;DR

This study uses widefield calcium imaging and deep learning to map the topographical organization of V4 in primates, revealing clustered domains for natural image features and advancing understanding of visual processing.

Contribution

It provides the first large-scale dataset and detailed topographical map of V4 responses to natural scenes, integrating experimental data with a digital twin model.

Findings

Identified clustered functional domains for natural image features.

Validated the topographical map with additional imaging techniques.

Revealed detailed neural codes and organization in V4.

Abstract

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.