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.

中文翻译：

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离散状态空间的主动推理：综合

主动推理是生物或人工智能体感知、行动、规划、决策和学习的规范原则。从一开始，其相关的过程理论就已经发展到包含复杂的生成模型，从而能够模拟各种复杂的行为。由于主动推理的不断发展，通常很难看出其基本原理与过程理论和实际实现之间的关系。在本文中，我们试图通过提供离散状态空间模型主动推理的完整数学综合来弥补这一差距。本技术摘要提供了该理论的概述，从第一原理推导出神经元动力学，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；允许连续的感觉来告知离散的表示。本文可用于以下用途：指导研究应对突出的挑战，提供有关如何实施主动推理来模拟实验行为的实用指南，或指向可用于进行经验预测的各种计算机模拟神经生理学反应。