Predictive coding offers a potentially unifying account of cortical function -- postulating that the core function of the brain is to minimize prediction errors with respect to a generative model of the world. The theory is closely related to the Bayesian brain framework and, over the last two decades, has gained substantial influence in the fields of theoretical and cognitive neuroscience. A large body of research has arisen based on both empirically testing improved and extended theoretical and mathematical models of predictive coding, as well as in evaluating their potential biological plausibility for implementation in the brain and the concrete neurophysiological and psychological predictions made by the theory. Despite this enduring popularity, however, no comprehensive review of predictive coding theory, and especially of recent developments in this field, exists. Here, we provide a comprehensive review both of the core mathematical structure and logic of predictive coding, thus complementing recent tutorials in the literature. We also review a wide range of classic and recent work within the framework, ranging from the neurobiologically realistic microcircuits that could implement predictive coding, to the close relationship between predictive coding and the widely-used backpropagation of error algorithm, as well as surveying the close relationships between predictive coding and modern machine learning techniques.

中文翻译：

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预测编码：理论和实验回顾

预测编码提供了对皮层功能的潜在统一解释——假设大脑的核心功能是最大限度地减少与世界生成模型相关的预测错误。该理论与贝叶斯大脑框架密切相关，并且在过去的二十年中，在理论和认知神经科学领域取得了重大影响。基于对预测编码的改进和扩展的理论和数学模型的实证测试，以及评估它们在大脑中实施的潜在生物学合理性以及该理论做出的具体神经生理学和心理预测，大量研究已经出现。然而，尽管这种经久不衰的流行，没有对预测编码理论的全面回顾，尤其是该领域的最新发展，存在。在这里，我们全面回顾了预测编码的核心数学结构和逻辑，从而补充了最近的文献教程。我们还回顾了该框架内的一系列经典和近期工作，从可以实现预测编码的神经生物学现实微电路，到预测编码与广泛使用的误差反向传播算法之间的密切关系，以及调查密切预测编码与现代机器学习技术之间的关系。