PDF(Pubmed)
Abstract：
Computational models of the primary visual cortex (V1) have suggested that V1 neurons behave like Gabor filters followed by simple nonlinearities. However, recent work employing convolutional neural network (CNN) models has suggested that V1 relies on far more nonlinear computations than previously thought. Specifically, unit responses in an intermediate layer of VGG-19 were found to best predict macaque V1 responses to thousands of natural and synthetic images. Here, we evaluated the hypothesis that the poor performance of lower layer units in VGG-19 might be attributable to their small receptive field size rather than to their lack of complexity per se. We compared VGG-19 with AlexNet, which has much larger receptive fields in its lower layers. Whereas the best-performing layer of VGG-19 occurred after seven nonlinear steps, the first convolutional layer of AlexNet best predicted V1 responses. Although the predictive accuracy of VGG-19 was somewhat better than that of standard AlexNet, we found that a modified version of AlexNet could match the performance of VGG-19 after only a few nonlinear computations. Control analyses revealed that decreasing the size of the input images caused the best-performing layer of VGG-19 to shift to a lower layer, consistent with the hypothesis that the relationship between image size and receptive field size can strongly affect model performance. We conducted additional analyses using a Gabor pyramid model to test for nonlinear contributions of normalization and contrast saturation. Overall, our findings suggest that the feedforward responses of V1 neurons can be well explained by assuming only a few nonlinear processing stages.
摘要：
初级视觉皮层（V1）的计算模型表明，V1神经元的行为类似于Gabor滤波器，然后是简单的非线性。然而,采用卷积神经网络（CNN）模型的最新工作表明，V1依赖于比以前认为的更多的非线性计算。具体来说,发现VGG-19中间层的单位响应可以最好地预测猕猴V1对数千个自然和合成图像的响应。这里,我们评估了以下假设：VGG-19中下层单元的性能不佳可能归因于它们的感受野大小较小，而不是它们本身缺乏复杂性。我们将VGG-19与AlexNet进行了比较，在其下层有更大的感受场。而性能最佳的VGG-19层发生在七个非线性步骤之后，AlexNet最佳预测V1响应的第一个卷积层。尽管VGG-19的预测准确性比标准AlexNet好一些，我们发现，经过几次非线性计算，AlexNet的修改版本就可以与VGG-19的性能相匹配。控制分析显示，减小输入图像的大小会导致VGG-19性能最佳的层转移到较低的层，与图像大小和感受野大小之间的关系可以强烈影响模型性能的假设一致。我们使用Gabor金字塔模型进行了其他分析，以测试归一化和对比度饱和度的非线性贡献。总的来说,我们的发现表明，V1神经元的前馈反应可以通过假设仅几个非线性处理阶段来很好地解释。
