寻找简单性：神经元和行为分析方法
Searching for simplicity: Approaches to the analysis of neurons and behavior
摘要：
动物行为的丰富性和复杂性使我们着迷，但理解行为及其神经基础需要更简单的描述。传统上，通过训练动物从事有限的行为，通过手工将行为分为离散的类别，或者通过限制有机体的感官体验来强加简化。另一种方法是问我们是否可以通过搜索自然行为的动力学来找到明确的证据，证明这些行为比它们可能的要简单。我们回顾了两种简化的数学方法，降维和最大熵方法，我们从生物组织的不同层次，从C。从秀丽线虫到灵长类动物的平稳追踪眼球运动的控制，从视网膜神经元网络对自然场景的编码到英语拼写规则。在每一种情况下，我们认为，明确的搜索简单性揭示了新的和意想不到的功能的生物系统，简化的证据给了我们一种语言，用它来表达新的问题，为下一代的实验。类似的数学结构成功地驯服了非常不同的生物系统的复杂性，这一事实暗示着还有一些更普遍的东西有待发现。
英文摘要：
What fascinates us about animal behavior is its richness and complexity, but understanding behavior and its neural basis requires a simpler description. Traditionally, simplification has been imposed by training animals to engage in a limited set of behaviors, by hand scoring behaviors into discrete classes, or by limiting the sensory experience of the organism. An alternative is to ask whether we can search through the dynamics of natural behaviors to find explicit evidence that these behaviors are simpler than they might have been. We review two mathematical approaches to simplification, dimensionality reduction and the maximum entropy method, and we draw on examples from different levels of biological organization, from the crawling behavior of C. elegans to the control of smooth pursuit eye movements in primates, and from the coding of natural scenes by networks of neurons in the retina to the rules of English spelling. In each case, we argue that the explicit search for simplicity uncovers new and unexpected features of the biological system, and that the evidence for simplification gives us a language with which to phrase new questions for the next generation of experiments. The fact that similar mathematical structures succeed in taming the complexity of very different biological systems hints that there is something more general to be discovered.
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