Deep neural networks (DNNs) have revolutionized computer science and are now widely used for neuroscientific research. A hot debate has ensued about the usefulness of DNNs as neuroscientific models of the human visual system; the debate centers on to what extent certain shortcomings of DNNs are real failures and to what extent they are redeemable. Here, we argue that the main problem is that we often do not understand which human functions need to be modeled and, thus, what counts as a falsification. Hence, not only is there a problem on the DNN side, but there is also one on the brain side (i.e., with the explanandum-the thing to be explained). For example, should DNNs reproduce illusions? We posit that we can make better use of DNNs by adopting an approach of comparative biology by focusing on the differences, rather than the similarities, between DNNs and humans to improve our understanding of visual information processing in general.

中文翻译：

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视觉 DNN 建模的比较生物学方法：关注差异，而不是相似之处。

深度神经网络 (DNN) 彻底改变了计算机科学，现在广泛用于神经科学研究。关于 DNN 作为人类视觉系统的神经科学模型的有用性引发了一场激烈的争论。争论的焦点是 DNN 的某些缺点在多大程度上是真正的失败，以及它们在多大程度上是可以弥补的。在这里，我们认为主要问题是我们经常不了解哪些人类功能需要建模，因此，什么算作证伪。因此，不仅DNN侧有问题，大脑侧也有问题（即explanandum——要解释的东西）。例如，DNN 是否应该重现幻觉？我们假设我们可以通过关注差异而不是相似之处采用比较生物学的方法来更好地利用 DNN，