The brain's function is to enable adaptive behavior in the world. To this end, the brain processes information about the world. The concept of representation links the information processed by the brain back to the world and enables us to understand what the brain does at a functional level. The appeal of making the connection between brain activity and what it represents has been irresistible to neuroscience, despite the fact that representational interpretations pose several challenges: We must define which aspects of brain activity matter, how the code works, and how it supports computations that contribute to adaptive behavior. It has been suggested that we might drop representational language altogether and seek to understand the brain, more simply, as a dynamical system. In this review, we argue that the concept of representation provides a useful link between dynamics and computational function and ask which aspects of brain activity should be analyzed to achieve a representational understanding. We peel the onion of brain representations in search of the layers (the aspects of brain activity) that matter to computation. The article provides an introduction to the motivation and mathematics of representational models, a critical discussion of their assumptions and limitations, and a preview of future directions in this area.

中文翻译：

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剥去大脑表示的洋葱。

大脑的功能是实现世界上的适应性行为。为此，大脑处理有关世界的信息。表示的概念将大脑处理的信息与世界联系起来，使我们能够了解大脑在功能级别上的作用。尽管表征解释带来了许多挑战，但在大脑活动及其代表的事物之间建立联系的吸引力一直是神经科学所无法抗拒的：我们必须定义大脑活动的哪些方面很重要，代码如何工作以及它如何支持计算有助于适应行为。有人建议我们可以完全放弃代表语言，并试图更简单地将大脑理解为动力系统。在这篇评论中，我们认为表征的概念在动力学和计算功能之间提供了有用的联系，并询问应该对大脑活动的哪些方面进行分析以获得表征的理解。我们剥去大脑表示的洋葱，以搜索与计算相关的层次（大脑活动的各个方面）。本文介绍了表示模型​​的动机和数学，对它们的假设和局限性进行了批判性讨论，并展望了该领域的未来发展方向。