神经科学中的解释模型：第一部分--认真对待机械抽象
Explanatory models in neuroscience: Part 1 -- taking mechanistic abstraction seriously
摘要：
尽管最近神经网络模型在模拟动物在视觉感知任务上的表现方面取得了成功，但批评人士担心这些模型未能阐明大脑功能。我们认为，系统神经科学中解释的一个核心方法是机械建模，在这种方法中，理解系统需要充实系统的组成部分、组织和活动，以及它们如何引起感兴趣的行为。然而，对于模型描述机制的意义，以及神经网络模型是否具有解释性，仍然存在一些争议。我们认为，某些类型的神经网络模型实际上是很好的例子，机械模型，当正确的概念，机械映射部署。基于现有的工作模型到机制映射（3M），我们描述了这样一个概念，我们称之为3M ++的标准。这些标准要求我们首先确定一个既抽象又详细到足以"运行"的描述级别，然后使用与个体之间脑-脑映射相同的原则来构建模型-脑映射。也许令人惊讶的是，所需要的抽象是那些已经在实验神经科学中使用的，并且是在构建更熟悉的计算模型时使用的那种，就像脑间映射的原理非常符合那些已经用于收集和分析动物数据的原理一样。在配套文件中，我们解决优化和可理解性之间的关系，在功能进化的解释。总而言之，对计算模型的机械解释以及优化过程所阐明的形式和功能之间的依赖关系可以帮助我们理解为什么大脑系统是以它们的方式构建的。
英文摘要：
Despite the recent success of neural network models in mimicking animal performance on visual perceptual tasks, critics worry that these models fail to illuminate brain function. We take it that a central approach to explanation in systems neuroscience is that of mechanistic modeling, where understanding the system is taken to require fleshing out the parts, organization, and activities of a system, and how those give rise to behaviors of interest. However, it remains somewhat controversial what it means for a model to describe a mechanism, and whether neural network models qualify as explanatory. We argue that certain kinds of neural network models are actually good examples of mechanistic models, when the right notion of mechanistic mapping is deployed. Building on existing work on model-to-mechanism mapping (3M), we describe criteria delineating such a notion, which we call 3M++. These criteria require us, first, to identify a level of description that is both abstract but detailed enough to be "runnable", and then, to construct model-to-brain mappings using the same principles as those employed for brain-to-brain mapping across individuals. Perhaps surprisingly, the abstractions required are those already in use in experimental neuroscience, and are of the kind deployed in the construction of more familiar computational models, just as the principles of inter-brain mappings are very much in the spirit of those already employed in the collection and analysis of data across animals. In a companion paper, we address the relationship between optimization and intelligibility, in the context of functional evolutionary explanations. Taken together, mechanistic interpretations of computational models and the dependencies between form and function illuminated by optimization processes can help us to understand why brain systems are built they way they are.
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