Neurons as hierarchies of quantum reference frames

TL;DR

This paper introduces a quantum information-theoretic model of neurons that uses hierarchies of quantum reference frames to represent neural structures and predict correlations in neural activity and remodeling.

Contribution

It develops a scalable, uniform quantum model of neural components and hierarchies, extending previous biological modeling approaches.

Findings

Predicts correlations between synaptic activity and dendritic remodeling.

Provides a framework for modeling neural and tissue development.

Enables generalization to nonneural biological tissues.

Abstract

Conceptual and mathematical models of neurons have lagged behind empirical understanding for decades. Here we extend previous work in modeling biological systems with fully scale-independent quantum information-theoretic tools to develop a uniform, scalable representation of synapses, dendritic and axonal processes, neurons, and local networks of neurons. In this representation, hierarchies of quantum reference frames act as hierarchical active-inference systems. The resulting model enables specific predictions of correlations between synaptic activity, dendritic remodeling, and trophic reward. We summarize how the model may be generalized to nonneural cells and tissues in developmental and regenerative contexts.