The aim of this paper is to leverage the free-energy principle and its corollary process theory, active inference, to develop a generic, generalizable model of the representational capacities of living creatures; that is, a theory of phenotypic representation. Given their ubiquity, we are concerned with distributed forms of representation (e.g., population codes), whereby patterns of ensemble activity in living tissue come to represent the causes of sensory input or data. The active inference framework rests on the Markov blanket formalism, which allows us to partition systems of interest, such as biological systems, into internal states, external states, and the blanket (active and sensory) states that render internal and external states conditionally independent of each other. In this framework, the representational capacity of living creatures emerges as a consequence of their Markovian structure and nonequilibrium dynamics, which together entail a dual-aspect information geometry. This entails a modest representational capacity: internal states have an intrinsic information geometry that describes their trajectory over time in state space, as well as an extrinsic information geometry that allows internal states to encode (the parameters of) probabilistic beliefs about (fictive) external states. Building on this, we describe here how, in an automatic and emergent manner, information about stimuli can come to be encoded by groups of neurons bound by a Markov blanket; what is known as the neuronal packet hypothesis. As a concrete demonstration of this type of emergent representation, we present numerical simulations showing that self-organizing ensembles of active inference agents sharing the right kind of probabilistic generative model are able to encode recoverable information about a stimulus array.

本文的目的是利用自由能原理及其推论过程理论、主动推理，开发一个通用的、可推广的生物表征能力模型；即表型表征理论。鉴于它们的普遍性，我们关注分布式表示形式（例如，群体代码），其中活体组织中的整体活动模式代表了感觉输入或数据的原因。主动推理框架基于马尔可夫毯子形式主义，它允许我们将感兴趣的系统（例如生物系统）划分为内部状态、外部状态和毯子（主动和感觉）状态，使内部和外部状态有条件地独立于彼此。在这个框架中，生物的表征能力是其马尔可夫结构和非平衡动力学的结果，它们共同带来了双重信息几何。这需要适度的表示能力：内部状态具有描述其在状态空间中随时间变化的轨迹的内在信息几何，以及允许内部状态编码关于（虚构）外部状态的概率信念（的参数）的外在信息几何。在此基础上，我们在这里描述如何以自动和紧急的方式，由马尔可夫毯束缚的神经元组编码有关刺激的信息；什么是所谓的神经元包假说。作为这种类型的涌现表示的具体演示，我们提出了数值模拟，表明共享正确类型的概率生成模型的主动推理代理的自组织集合能够编码有关刺激阵列的可恢复信息。