Analysis of complex excitation patterns using Feynman-like diagrams

TL;DR

This paper introduces a novel quasiparticle framework called 'cardions' to analyze complex excitation patterns in media like neural and cardiac tissues, using Feynman-like diagrams to understand pattern dynamics and transitions.

Contribution

It presents a new theoretical approach modeling excitation dynamics with three quasiparticles, enabling detailed analysis of pattern formation and transitions in excitable media.

Findings

Identification of three quasiparticles: heads, tails, and pivots.

Representation of excitation interactions using Feynman-like diagrams.

Experimental validation with optical voltage mapping on human atrial myocytes.

Abstract

Many extended chemical and biological systems self-organise into complex patterns that drive the medium behaviour in a non-linear fashion. An important class of such systems are excitable media, including neural and cardiac tissues. In extended excitable media, wave breaks can form rotating patterns and turbulence. However, the onset, sustaining and elimination of such complex patterns is currently incompletely understood. The classical theory of phase singularities in excitable media was recently challenged, as extended lines of conduction block were identified as phase discontinuities. Here, we provide a theoretical framework that captures the rich dynamics in excitable systems in terms of three quasiparticles: heads, tails, and pivots. We propose to call these quasiparticles `cardions'. In simulations and experiments, we show that these basic building blocks combine into at least four different bound states. By representing their interactions similarly to Feynman diagrams in physics, the creation and annihilation of vortex pairs are shown to be sequences of dynamical creation, annihilation, and recombination of the identified quasiparticles. We draw such diagrams for numerical simulations, as well as optical voltage mapping experiments performed on cultured human atrial myocytes (hiAMs). Our results provide a new, unified language for a more detailed theory, analysis, and mechanistic insights of dynamical transitions in excitation patterns.