Characterising the Non-Equilibrium Dynamics of a Neural Cell

TL;DR

This paper models the non-equilibrium dynamics of neural cells using statistical mechanics and dynamical systems theory, explaining phenomena like action potentials, bursting, and phase transitions in a unified multiscale framework.

Contribution

It introduces a novel multiscale model combining non-equilibrium physics and geometry to understand neural dynamics from molecules to spikes.

Findings

Reproduction of the action potential shape

Explanation of bursting dynamics

Identification of non-equilibrium phase transition from resting to spiking

Abstract

We examine the dynamical evolution of the state of a neurone, with particular care to the non-equilibrium nature of the forces influencing its movement in state space. We combine non-equilibrium statistical mechanics and dynamical systems theory to characterise the nature of the neural resting state, and its relationship to firing. The stereotypical shape of the action potential arises from this model, as well as bursting dynamics, and the non-equilibrium phase transition from resting to spiking. Geometric properties of the system are discussed, such as the birth and shape of the neural limit cycle, which provide a complementary understanding of these dynamics. This provides a multiscale model of the neural cell, from molecules to spikes, and explains various phenomena in a unified manner. Some more general notions for damped oscillators, birth-death processes, and stationary non-equilibrium systems are included.