Neural and phenotypic representation under the free-energy principle. (January 2021)

• Record Type: Journal Article
• Authors: Ramstead, Maxwell J.D.; Hesp, Casper; Tschantz, Alexander; Smith, Ryan; Constant, Axel; Friston, Karl
• Is Part Of: Neuroscience and biobehavioral reviews. Volume 120(2021)
• Page Start: 109
• Page End: 122
• Publication Date: 2021-01
• Subjects: Neural representation -- Neuronal packet hypothesis -- Phenotypic representation -- Markov blankets -- Active inference -- Free-energy principle; Psychophysiology -- Periodicals; Human behavior -- Periodicals; Animal behavior -- Periodicals; Neurology -- Periodicals; Behavior -- Periodicals; Ethology -- Periodicals; Neurology -- Periodicals; Psychophysiologie -- Périodiques; Comportement humain -- Périodiques; Animaux -- Mœurs et comportement -- Périodiques; Neurologie -- Périodiques; Animal behavior; Human behavior; Neurology; Psychophysiology; Periodicals; Electronic journals; 573.8
• DOI: 10.1016/j.neubiorev.2020.11.024
• Languages: English
• ISSNs: 0149-7634

Abstract

Highlights: We develop a generalizable model of the representational capacities of living creatures based on the free-energy principle. We review the free-energy formulation and its account of the emergence of representational capacities in living creatures. We review formulations of self-assembly and pattern formation in living systems, premised on the free-energy principle. We provide numerical demonstrations for the neuronal packet hypothesis (that Markov blankets encode stimulus features). Abstract: 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.