The visual system can extract information about shape from the pattern of light and dark surface shading on an object. Very little is known about how this is accomplished. We have used a learning algorithm to construct a neural network model that computes the principal curvatures and orientation of elliptic paraboloids independently of the illumination direction. Our chief finding is that receptive fields developed by units of such model network are surprisingly similar to some found in the visual cortex. It appears that neurons that can make use of the continuous gradations of shading have receptive fields similar to those previously interpreted as dealing with contours (i.e. 'bar' detectors or 'edge' detectors). This study illustrates the difficulty of deducing neuronal function within a network solely from receptive fields. It is also important to consider the pattern of connections a neuron makes with subsequent stages, which we call the 'projective field'.

中文翻译：

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视觉皮层的神经网络模型，用于从阴影表面的图像确定表面曲率。

视觉系统可以从物体上的明暗表面阴影的图案中提取有关形状的信息。对于如何实现这一目标知之甚少。我们已经使用一种学习算法来构建一个神经网络模型，该模型独立于照明方向来计算椭圆抛物面的主曲率和方向。我们的主要发现是，由这种模型网络的单元开发的感受野与视觉皮层中的某些令人惊讶地相似。似乎可以利用阴影的连续渐变的神经元具有与以前解释为处理轮廓（即“条形”检测器或“边缘”检测器）相似的感受野。这项研究说明了仅从感受野推断网络内神经元功能的困难。