With the success of artificial neural network models in machine learning has come a renewed interest in the possibility that neural networks can be used as scientific models for understanding the function of real neural systems. When similar questions initially arose in the 1980’s and ‘90’s, the many discrepancies between artificial and natural neural systems contributed to the widespread view that neural network models were not biologically plausible, and hence of limited utility for understanding real neural systems. The current paper suggests, to the contrary, that such models capture a level of description isomorphic to that adopted by two essential tools in cognitive neuroscience: functional brain imaging and connectivity analysis. Recognizing this concordance allows neural network models to serve as conceptual bridges between hypotheses about neuro-cognitive mechanisms and the structural and neurophysiological measurements that are the raw stuff of cognitive neuroscience. To illustrate these points, the paper reviews four different ways in which neural network models are reshaping our understanding of the neural systems that support human semantic memory.

随着人工神经网络模型在机器学习中的成功，人们对将神经网络用作理解真实神经系统功能的科学模型的可能性有了新的兴趣。最初在1980年代和90年代出现类似问题时，人工神经系统与自然神经系统之间的许多差异促使人们普遍认为神经网络模型在生物学上不可行，因此在理解真实神经系统方面的实用性有限。相反，当前的论文表明，这种模型捕获的描述水平与认知神经科学中的两个基本工具采用的描述同构：功能性大脑成像和连接性分析。认识到这种一致性，使得神经网络模型可以充当关于神经认知机制的假设与作为认知神经科学的原始内容的结构和神经生理学测量之间的概念桥梁。为了说明这些观点，本文回顾了神经网络模型重塑我们对支持人类语义记忆的神经系统的理解的四种不同方式。