In this study, we evaluated the convolutional neural network (CNN) method for modeling V1 neurons of awake macaque monkeys in response to a large set of complex pattern stimuli. CNN models outperformed all the other baseline models, such as Gabor-based standard models for V1 cells and various variants of generalized linear models. We then systematically dissected different components of the CNN and found two key factors that made CNNs outperform other models: thresholding nonlinearity and convolution. In addition, we fitted our data using a pre-trained deep CNN via transfer learning. The deep CNN’s higher layers, which encode more complex patterns, outperformed lower ones, and this result was consistent with our earlier work on the complexity of V1 neural code. Our study systematically evaluates the relative merits of different CNN components in the context of V1 neuron modeling.

中文翻译：

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V1对复杂模式响应的卷积神经网络模型。

在这项研究中，我们评估了卷积神经网络（CNN）方法，以响应大量复杂模式刺激，为清醒猕猴的V1神经元建模。CNN模型的表现优于所有其他基线模型，例如基于Gabor的V1细胞标准模型和广义线性模型的各种变体。然后，我们系统地剖析了CNN的不同组成部分，并发现了使CNN优于其他模型的两个关键因素：阈值非线性和卷积。此外，我们通过转移学习使用预先训练的深度CNN拟合了数据。编码更复杂模式的深层CNN高层优于下层，并且此结果与我们之前对V1神经代码的复杂性所做的工作一致。