Da Costa Lancelot, Parr Thomas, Sajid Noor, Veselic Sebastijan, Neacsu Victorita, Friston Karl
Department of Mathematics, Imperial College London, London, SW7 2RH, United Kingdom.
Wellcome Centre for Human Neuroimaging, University College London, London, WC1N 3AR, United Kingdom.
J Math Psychol. 2020 Dec;99:102447. doi: 10.1016/j.jmp.2020.102447.
Active inference is a normative principle underwriting perception, action, planning, decision-making and learning in biological or artificial agents. From its inception, its associated process theory has grown to incorporate complex generative models, enabling simulation of a wide range of complex behaviours. Due to successive developments in active inference, it is often difficult to see how its underlying principle relates to process theories and practical implementation. In this paper, we try to bridge this gap by providing a complete mathematical synthesis of active inference on discrete state-space models. This technical summary provides an overview of the theory, derives neuronal dynamics from first principles and relates this dynamics to biological processes. Furthermore, this paper provides a fundamental building block needed to understand active inference for mixed generative models; allowing continuous sensations to inform discrete representations. This paper may be used as follows: to guide research towards outstanding challenges, a practical guide on how to implement active inference to simulate experimental behaviour, or a pointer towards various in-silico neurophysiological responses that may be used to make empirical predictions.
主动推理是一种规范原则，它支持生物或人工主体中的感知、行动、规划、决策和学习。从其诞生之初起，与之相关的过程理论就不断发展，纳入了复杂的生成模型，从而能够模拟广泛的复杂行为。由于主动推理的不断发展，人们常常难以理解其 underlying principle 与过程理论及实际实现之间的关系。在本文中，我们试图通过对离散状态空间模型上的主动推理进行完整的数学综合来弥合这一差距。本技术总结概述了该理论，从第一原理推导神经元动力学，并将这种动力学与生物过程联系起来。此外，本文提供了理解混合生成模型的主动推理所需的基本构建块；允许连续的感觉为离散表示提供信息。本文可用于以下方面：指导针对突出挑战的研究、作为关于如何实施主动推理以模拟实验行为的实用指南，或作为指向各种可用于进行实证预测的计算机神经生理学反应的指针。
