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.

卷积神经网络 (CNN) 的灵感来自于生物视觉研究的早期发现。从那时起，它们已成为计算机视觉领域的成功工具以及视觉任务中神经活动和行为的最先进模型。这篇评论强调了在 CNN 的背景下，成为计算神经科学中的一个好的模型意味着什么，以及模型可以提供洞察力的各种方式。具体来说，它涵盖了 CNN 的起源以及我们将它们验证为生物视觉模型的方法。然后继续阐述我们可以通过理解和试验 CNN 来了解生物视觉的内容，并讨论在基本对象识别之外的视觉研究中使用 CNN 的新兴机会。