Control flow in active inference systems

• DOI: 10.48550/arxiv.2303.01514
• Publication Date: 2023-03-04
• Authors: Chris Fields; Filippo Fabrocini; Karl Friston; James F. Glazebrook; Hananel Hazan; Michael Levin; Antonino Marcianò
• Keywords: 0301 basic medicine; Quantum Physics; 03 medical and health sciences; Biological Physics (physics.bio-ph); Quantitative Biology - Neurons and Cognition; FOS: Biological sciences; FOS: Physical sciences; Neurons and Cognition (q-bio.NC); Physics - Biological Physics; Quantum Physics (quant-ph)

Living systems face both environmental complexity and limited access to free-energy resources. Survival under these conditions requires a control system that can activate, or deploy, available perception and action resources in a context specific way. We show here that when systems are described as executing active inference driven by the free-energy principle (and hence can be considered Bayesian prediction-error minimizers), their control flow systems can always be represented as tensor networks (TNs). We show how TNs as control systems can be implmented within the general framework of quantum topological neural networks, and discuss the implications of these results for modeling biological systems at multiple scales.