Zanvyl Krieger Mind/Brain Institute, Johns Hopkins University, Baltimore, MD 21218, USA; Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.
Zanvyl Krieger Mind/Brain Institute, Johns Hopkins University, Baltimore, MD 21218, USA; Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.
Curr Biol. 2021 Jan 11;31(1):51-65.e5. doi: 10.1016/j.cub.2020.09.076. Epub 2020 Oct 22.
Area V4 is the first object-specific processing stage in the ventral visual pathway, just as area MT is the first motion-specific processing stage in the dorsal pathway. For almost 50 years, coding of object shape in V4 has been studied and conceived in terms of flat pattern processing, given its early position in the transformation of 2D visual images. Here, however, in awake monkey recording experiments, we found that roughly half of V4 neurons are more tuned and responsive to solid, 3D shape-in-depth, as conveyed by shading, specularity, reflection, refraction, or disparity cues in images. Using 2-photon functional microscopy, we found that flat- and solid-preferring neurons were segregated into separate modules across the surface of area V4. These findings should impact early shape-processing theories and models, which have focused on 2D pattern processing. In fact, our analyses of early object processing in AlexNet, a standard visual deep network, revealed a similar distribution of sensitivities to flat and solid shape in layer 3. Early processing of solid shape, in parallel with flat shape, could represent a computational advantage discovered by both primate brain evolution and deep-network training.
V4 区是腹侧视觉通路中的第一个针对特定对象的处理阶段，就像 MT 区是背侧通路中的第一个针对特定运动的处理阶段一样。近 50 年来，由于 V4 处于 2D 视觉图像转换的早期阶段，其在物体形状编码方面的研究和构想一直基于平面模式处理。然而，在清醒猴子的记录实验中，我们发现大约一半的 V4 神经元对阴影、镜面反射、反射、折射或视差线索所传达的立体、3D 形状深度更为敏感和响应。使用双光子功能显微镜，我们发现平面和立体偏好神经元在 V4 区域的表面上分成了不同的模块。这些发现应该会对早期专注于 2D 模式处理的形状处理理论和模型产生影响。事实上，我们对标准视觉深度网络 AlexNet 中早期物体处理的分析表明，在第 3 层中对平面和立体形状的敏感性分布相似。立体形状的早期处理与平面形状并行，可以代表灵长类动物大脑进化和深度网络训练发现的一种计算优势。
