Deep learning is a kind of machine learning which happens in a certain type of artificial neural networks called deep networks. Artificial deep networks, which exhibit many similarities with biological ones, have consistently shown human-like performance in many intelligent tasks. This poses the question whether this performance is caused by such similarities. After reviewing the structure and learning processes of artificial and biological neural networks, we outline two important reasons for the success of deep learning, namely the extraction of successively higher level features and the multiple layer structure, which are closely related to each other. Then some indications about the framing of this heated debate are given. After that, an assessment of the value of artificial deep networks as models of the human brain is given from the similarity perspective of model representation. Finally, a new version of computational functionalism is proposed which addresses the specificity of deep neural computation better than classic, program based computational functionalism.

中文翻译：

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深度学习时代的计算功能主义

深度学习是一种机器学习，它发生在某种称为深度网络的人工神经网络中。人工深度网络与生物网络表现出许多相似之处，在许多智能任务中始终表现出类似人类的表现。这就提出了这种表现是否是由这种相似性引起的问题。在回顾了人工神经网络和生物神经网络的结构和学习过程后，我们概述了深度学习成功的两个重要原因，即逐级提取更高层次的特征和多层结构，它们彼此密切相关。然后给出了关于这场激烈辩论的框架的一些迹象。之后，从模型表示的相似性角度评估人工深度网络作为人脑模型的价值。最后，提出了一种新版本的计算功能主义，它比经典的基于程序的计算功能主义更好地解决了深度神经计算的特殊性。