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.

翻译：视觉系统在进化中形成了处理自然场景的能力，然而目前我们对视觉皮层拓扑结构和功能的大部分认知仍源于人工刺激实验。为深入理解自然场景的视觉处理机制，我们利用宽场钙成像技术记录灵长类V4区对大量自然图像的响应，构建了柱级尺度响应的大型数据集。基于该数据集，我们通过深度学习构建了V4的数字孪生模型，生成了每个皮层位置对自然图像偏好的详细拓扑图。该拓扑图揭示了针对特定类别自然图像特征的功能域聚类，涵盖从表面相关属性（如颜色和纹理）到形状相关特征（如边缘、曲率和面部特征）的层级。通过额外的宽场钙成像和单细胞分辨率双光子成像实验，我们验证了模型预测的功能域。本研究阐明了V4区表征自然场景的精细拓扑组织及其神经编码机制。