The Free Energy Principle drives neuromorphic development

TL;DR

This paper demonstrates how systems constrained by the free energy principle naturally evolve into hierarchical, neuromorphic structures supporting complex computations, with implications across biological and quantum systems.

Contribution

It introduces a formal framework linking the free energy principle to the emergence of hierarchical neuromorphic morphologies and their quantum computational representations.

Findings

Hierarchical structures emerge from free energy constraints in biological systems.

Connections between cone-cocone diagrams and topological quantum field theories are established.

Quantum neural networks can model hierarchical computations in biological systems.

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.