Advances in Deep Convolutional Neural Networks (DCNN) provide new opportunities for computational neuroscience to pose novel questions regarding the function of biological visual systems. Some attempts have been made to utilize advances in machine learning to answer neuroscientific questions, but how to appropriately make comparisons between the biological systems and artificial neural network structure is an open question. This analysis quantifies network properties of the mouse visual system and a common DCNN model (VGG16), to determine if this comparison is appropriate. Utilizing weighted graph-theoretic measures of node density (weighted node-degree), path length, local clustering coefficient, and betweenness, differences in functional connectivity patterns in the modern artificial computer vision system and the biological vision system are quantified. Results show that the mouse exhibits network measure distributions more similar to Poisson than normal, whereas the VGG16 exhibits network measure distributions with a more Gaussian shape than the sampled biological network. The artificial network shows higher density measures and shorter path lengths in comparison to the biological network. These results show that training a VGG16 for an object recognition task is unlikely to produce a network whose functional connectivity is similar to the mammalian visual system.

深度卷积神经网络（DCNN）的进步为计算神经科学提出了有关生物视觉系统功能的新问题的新机会。已经进行了一些尝试以利用机器学习的进展来回答神经科学问题，但是如何适当地在生物系统和人工神经网络结构之间进行比较是一个悬而未决的问题。该分析量化了鼠标视觉系统和通用DCNN模型（VGG16）的网络属性，以确定这种比较是否合适。利用加权图理论测量节点密度（加权节点度），路径长度，局部聚类系数和中间性，量化了现代人工计算机视觉系统和生物视觉系统中功能连接模式的差异。结果显示，与正常情况相比，鼠标的网络测度分布更类似于Poisson，而与采样的生物网络相比，VGG16的网络测度分布具有更高的高斯形状。与生物网络相比，人工网络显示出更高的密度度量和较短的路径长度。这些结果表明，为目标识别任务训练VGG16不太可能产生功能连通性类似于哺乳动物视觉系统的网络。而VGG16展示的网络度量分布比采样的生物网络具有更高的高斯形状。与生物网络相比，人工网络显示出更高的密度度量和较短的路径长度。这些结果表明，为目标识别任务训练VGG16不太可能产生功能连通性类似于哺乳动物视觉系统的网络。而VGG16展示的网络度量分布比采样的生物网络具有更高的高斯形状。与生物网络相比，人工网络显示出更高的密度度量和较短的路径长度。这些结果表明，为目标识别任务训练VGG16不太可能产生功能连通性类似于哺乳动物视觉系统的网络。