The Free Energy Principle drives neuromorphic development

Abstract

We show how any system with morphological degrees of freedom and locally limited free energy will, under the constraints of the free energy principle, evolve toward a neuromorphic morphology that supports hierarchical computations in which each level of the hierarchy enacts a coarse-graining of its inputs, and dually a fine-graining of its outputs. Such hierarchies occur throughout biology, from the architectures of intracellular signal transduction pathways to the large-scale organization of perception and action cycles in the mammalian brain. Formally, the close formal connections between cone-cocone diagrams (CCCD) as models of quantum reference frames on the one hand, and between CCCDs and topological quantum field theories on the other, allow the representation of such computations in the fully-general quantum-computational framework of topological quantum neural networks.

References in corpus (11)

• Reference frames, superselection rules, and quantum information
• The thermodynamics of prediction
• A free energy principle for a particular physics
• The contextual fraction as a measure of contextuality
• Universal Darwinism as a process of Bayesian inference
• Logical Bell Inequalities
• On Bayesian Mechanics: A Physics of and by Beliefs
• The physical meaning of the holographic principle
• Some Consequences of the Thermodynamic Cost of System Identification
• Predictive Coding Approximates Backprop along Arbitrary Computation Graphs
• Algebraic Topology Foundations of Supersymmetry and Symmetry Breaking in Quantum Field Theory and Quantum Gravity: A Review

Cited by in corpus (1)

• The physical meaning of the holographic principle