The topological organization and feature preferences of primate visual area
V4 have been primarily studied using artificial stimuli. Here, we combined
large-scale calcium imaging with deep learning methods to characterize and
understand how V4 processes natural images. By fitting a deep learning model to
an unprecedentedly large dataset of columnar scale cortical responses to tens
of thousands of natural stimuli and using the model to identify the images
preferred by each cortical pixel, we obtained a detailed V4 topographical map
of natural stimulus preference. The map contains distinct functional domains
preferring a variety of natural image features, ranging from surface-related
features such as color and texture to shape-related features such as edge,
curvature, and facial features. These predicted domains were verified by
additional widefield calcium imaging and single-cell resolution two-photon
imaging. Our study reveals the systematic topological organization of V4 for
encoding image features in natural scenes.

本研究使用大规模的钙成像与深度学习方法，研究了灵长类视觉 V4 区域能否处理自然图像，并得到了 V4 区域自然刺激偏好的详细拓扑地图。该地图包含了一系列自然图像特征，从表面特征例如颜色和纹理到形状特征例如边缘、曲率和面部特征。