神经科学中的解释模型：第2部分--基于约束的可理解性
Explanatory models in neuroscience: Part 2 -- constraint-based intelligibility
摘要：
计算建模在神经科学中扮演着越来越重要的角色，突出了计算模型如何解释的哲学问题。在神经科学的神经网络模型的背景下，人们对模型的可理解性以及它们与大脑中发现的内容的关系（如果有的话）表示担忧。我们认为，使一个系统变得可理解的，是对它的行为和对该行为负有因果责任的因素之间的依赖关系的理解。在生物系统中，许多这些依赖性自然是“自上而下”的：行为学的必要性与自然选择下的进化和发展约束相互作用。我们描述了用于构建NN模型的优化技术如何捕获这些依赖关系的一些关键方面，从而帮助解释为什么大脑系统是这样的-因为当NN和大脑共享一个具有挑战性的生态相关目标时，它会对两种系统中表现出的可能机制施加严格的约束。通过结合两种常见的解释模式--一种基于自下而上的机制（我们在一篇配套论文中讨论了它与神经网络模型的关系），另一种基于自上而下的约束，这些模型阐明了大脑功能。
英文摘要：
Computational modeling plays an increasingly important role in neuroscience, highlighting the philosophical question of how computational models explain. In the context of neural network models for neuroscience, concerns have been raised about model intelligibility, and how they relate (if at all) to what is found in the brain. We claim that what makes a system intelligible is an understanding of the dependencies between its behavior and the factors that are causally responsible for that behavior. In biological systems, many of these dependencies are naturally "top-down": ethological imperatives interact with evolutionary and developmental constraints under natural selection. We describe how the optimization techniques used to construct NN models capture some key aspects of these dependencies, and thus help explain why brain systems are as they are -- because when a challenging ecologically-relevant goal is shared by a NN and the brain, it places tight constraints on the possible mechanisms exhibited in both kinds of systems. By combining two familiar modes of explanation -- one based on bottom-up mechanism (whose relation to neural network models we address in a companion paper) and the other on top-down constraints, these models illuminate brain function.
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