论文标题
卷积神经网络作为视觉系统的模型：过去，现在和未来
Convolutional Neural Networks as a Model of the Visual System: Past, Present, and Future
论文作者
论文摘要
卷积神经网络（CNN）的灵感来自生物视觉研究中的早期发现。此后，它们成为计算机视觉和视觉任务上神经活动和行为的最新模型的成功工具。这篇评论强调了在CNN的背景下，这意味着成为计算神经科学的良好模型，以及模型可以提供洞察力的各种方式。具体而言，它涵盖了CNN的起源以及我们将其验证为生物视觉模型的方法。然后，它通过理解和实验CNN来详细阐述我们可以从生物愿景中学到的知识，并讨论了在视觉研究中使用CNN超出基本对象识别的新兴机会。
Convolutional neural networks (CNNs) were inspired by early findings in the study of biological vision. They have since become successful tools in computer vision and state-of-the-art models of both neural activity and behavior on visual tasks. This review highlights what, in the context of CNNs, it means to be a good model in computational neuroscience and the various ways models can provide insight. Specifically, it covers the origins of CNNs and the methods by which we validate them as models of biological vision. It then goes on to elaborate on what we can learn about biological vision by understanding and experimenting on CNNs and discusses emerging opportunities for the use of CNNS in vision research beyond basic object recognition.
