The free energy principle induces neuromorphic development

Abstract

We show how any finite physical system with morphological, i.e. three-dimensional embedding or shape, degrees of freedom and locally limited free energy will, under the constraints of the free energy principle, evolve over time towards 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.

Control flow in active inference systems

“…We have shown previously that the FEP will induce "neuromorphic" morphologies -i.e. morphologies that segregate inputs from outputs and enable a fan-in/fan-out computational architecture -in any systems with morphological degrees of freedom [154]. Such systems can be expected to have deep control hierarchies at the cellular level, with hierarchical structure correlating with morphological structure in morphologically-complex cells such as neurons [155], and in multicellular assemblages at all scales.…”

The experience of pain and its ontological modelling from a philosophical point of view: Phenomenological description and ontological revision of the McGill Pain Questionnaire

“…Ontological models can become an even more effective tool in medical settings: They can provide the structure for pain assessment and measurement tools that include qualitative elements such as emotions as is the case with the McGill Pain Questionnaire (MPQ) 28,29 They can be formalised and implemented into machine learning models aimed at building automatic natural language analysis software that allow the medical team to automatically obtain information interpreted from their patients' verbal communication 30 …”

“…They can be formalised and implemented into machine learning models aimed at building automatic natural language analysis software that allow the medical team to automatically obtain information interpreted from their patients' verbal communication 30 …”

“…28,29 − They can be formalised and implemented into machine learning models aimed at building automatic natural language analysis software that allow the medical team to automatically obtain information interpreted from their patients' verbal communication. 30 The ontological theories that underlie the studies on ontological modelling are not properly philosophical ontologies.…”

“…Neuroscientist like Karl Friston 30,32,33 proposed another important ontological model called the Error Prediction Theory. According to this perspective, the brain continuously creates predictive models of the external environment and uses these models to anticipate future events like a prediction machine.…”