  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.
