Deep neural networks capture texture sensitivity in V2
Deep neural networks capture texture sensitivity in V2
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DOI：
10.1167/jov.20.7.21
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发表时间：
2020-07-01
影响因子：
1.8
通讯作者：
Schwartz, Odelia
中科院分区：
文献类型：
--
作者：
Laskar, Md Nasir Uddin;Giraldo, Luis Gonzalo Sanchez;Schwartz, Odelia
经过视觉对象训练的深度卷积神经网络（cnn）在预测视觉皮层神经元的某些反应特性方面表现出了令人感兴趣的能力。然而，产生这种能力的因素（例如，模型是否经过训练，接受野大小）和计算（例如，卷积、纠偏、池化、归一化），在什么水平上，以及中间处理阶段在解释跨皮层层次区域发展的变化中的作用，人们知之甚少。由于最近的神经生理学实验提供了丰富的数据，指向次级（而不是初级）视觉皮层（V2）的纹理敏感性，因此我们将重点放在纹理敏感性上作为一个范例。我们最初对CNN进行了探索，没有对神经数据进行任何拟合，发现CNN的前两层在纹理敏感性方面与前两个皮质区域具有质的对应关系。因此，我们开发了一种定量方法来选择最适合脑神经记录的CNN模型神经元群体。我们发现CNN在整改后可以发展出与第二层次级皮层的兼容性，并且在池化后这种兼容性得到了改善，但只受到局部归一化操作的轻微影响。CNN的更高层可以进一步提高与V2数据的兼容性。当合并随机权重而不是学习权重时，兼容性降低了。我们的研究结果表明，CNN类模型对于捕捉皮层早期区域的变化是有效的，并且有可能帮助识别导致大脑分层处理的计算（代码可在GiftHub中获得）。
Deep convolutional neural networks (CNNs) trained on visual objects have shown intriguing ability to predict some response properties of visual cortical neurons. However, the factors (e.g., if the model is trained or not, receptive field size) and computations (e.g., convolution, rectification, pooling, normalization) that give rise to such ability, at what level, and the role of intermediate processing stages in explaining changes that develop across areas of the cortical hierarchy are poorly understood. We focused on the sensitivity to textures as a paradigmatic example, since recent neurophysiology experiments provide rich data pointing to texture sensitivity in secondary (but not primary) visual cortex (V2). We initially explored the CNN without any fitting to the neural data and found that the first two layers of the CNN showed qualitative correspondence to the first two cortical areas in terms of texture sensitivity. We therefore developed a quantitative approach to select a population of CNN model neurons that best fits the brain neural recordings. We found that the CNN could develop compatibility to secondary cortex in the second layer following rectification and that this was improved following pooling but only mildly influenced by the local normalization operation. Higher layers of the CNN could further, though modestly, improve the compatibility with the V2 data. The compatibility was reduced when incorporating random rather than learned weights. Our results show that the CNN class of model is effective for capturing changes that develop across early areas of cortex, and has the potential to help identify the computations that give rise to hierarchical processing in the brain (code is available in GiftHub).