Baltieri Manuel, Buckley Christopher L
EASY Group-Sussex Neuroscience, Department of Informatics, University of Sussex, Brighton BN1 9RH, UK.
Entropy (Basel). 2019 Mar 7;21(3):257. doi: 10.3390/e21030257.
In the past few decades, probabilistic interpretations of brain functions have become widespread in cognitive science and neuroscience. In particular, the free energy principle and active inference are increasingly popular theories of cognitive functions that claim to offer a unified understanding of life and cognition within a general mathematical framework derived from information and control theory, and statistical mechanics. However, we argue that if the active inference proposal is to be taken as a general process theory for biological systems, it is necessary to understand how it relates to existing control theoretical approaches routinely used to study and explain biological systems. For example, recently, PID (Proportional-Integral-Derivative) control has been shown to be implemented in simple molecular systems and is becoming a popular mechanistic explanation of behaviours such as chemotaxis in bacteria and amoebae, and robust adaptation in biochemical networks. In this work, we will show how PID controllers can fit a more general theory of life and cognition under the principle of (variational) free energy minimisation when using approximate linear generative models of the world. This more general interpretation also provides a new perspective on traditional problems of PID controllers such as parameter tuning as well as the need to balance performances and robustness conditions of a controller. Specifically, we then show how these problems can be understood in terms of the optimisation of the precisions (inverse variances) modulating different prediction errors in the free energy functional.
在过去几十年中，对大脑功能的概率解释在认知科学和神经科学领域已广泛传播。特别是，自由能原理和主动推理是认知功能领域日益流行的理论，它们声称能在源自信息与控制理论以及统计力学的通用数学框架内，对生命和认知提供统一的理解。然而，我们认为，如果主动推理提议要被视为生物系统的一般过程理论，就有必要理解它与常用于研究和解释生物系统的现有控制理论方法之间的关系。例如，最近有研究表明比例 - 积分 - 微分（PID）控制在简单分子系统中得以实现，并且正成为对诸如细菌和变形虫的趋化作用以及生化网络中的稳健适应等行为的一种流行的机制性解释。在这项工作中，当使用世界的近似线性生成模型时，我们将展示PID控制器如何能在（变分）自由能最小化原理下符合更一般的生命和认知理论。这种更一般的解释还为PID控制器的传统问题，如参数调整以及平衡控制器性能和稳健性条件的必要性，提供了新的视角。具体而言，我们随后将展示如何从自由能泛函中调节不同预测误差的精度（逆方差）的优化角度来理解这些问题。
