The concept of 'neural coding' supposes that neural firing patterns in some sense represent some external correlate, whether sensory, motor, or structural knowledge about the world. While the implied existence of a one-to-one mapping between external referents and neural firing has been useful, the prevalence of adaptation challenges this. Adaptation provides neural responses with dynamics on timescales that range from milliseconds up to many seconds. These timescales are highly relevant for sensory experience in the natural world, in which local statistical properties of inputs change continuously, and are additionally altered by active sensing. Adaptation has a number of consequences for coding: it creates short-term history dependence; it engenders complex feature selectivity that is time-varying; and it can serve to enhance information representation in dynamic environments. Considering how to best incorporate adaptation into neural models exposes a fundamental dichotomy in approaches to the description of neural systems: ones that take an explicitly 'coding' perspective versus ones that describe the system's dynamics. Here we discuss the pros and cons of different approaches to the modeling of adaptive dynamics.

中文翻译：

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适应在神经编码中的作用。

“神经编码”的概念假设神经激发模式在某种意义上代表了某种外部关联，无论是关于世界的感觉，运动还是结构知识。虽然在外部参照物和神经激发之间存在一对一映射的隐含存在很有用，但适应的普遍性对此提出了挑战。适应可以在几毫秒到几秒的时间范围内为神经响应提供动态变化。这些时标与自然世界中的感官体验高度相关，在自然世界中，输入的本地统计属性会不断变化，并且会通过主动感应进行更改。适应对编码有很多影响：适应会造成短期历史依赖；它带来了随时间变化的复杂特征选择性；它可以用来增强动态环境中的信息表示。考虑如何将适应性最好地整合到神经模型中，揭示了神经系统描述方法的基本二分法：一种具有明确的“编码”观点，而另一种则描述了系统动力学。在这里，我们讨论了自适应动力学建模的不同方法的利弊。