AI Insight
This theoretical paper proposes a new framework for understanding neuronal electrical activity based on "murburn" principles and electron-holding potential (EHP), challenging conventional models that rely primarily on trans-membrane ion gradients. The authors develop a unified mathematical equation that describes resting potential, signal generation, and action potential propagation using redox chemistry and thermodynamic gradients, extending from single-variable to two-variable excitable systems. The framework mathematically derives neuronal conduction velocity and membrane potential as functions of physical parameters like temperature, ionic strength, and axon diameter.
Why it matters
If validated, this alternative theoretical framework could fundamentally reshape our understanding of neuronal function by linking metabolic and redox states directly to electrical activity, potentially impacting how we approach neurological disorders and design neuromodulation therapies.
Understand the Science
Abstract: Trans-membrane ion-gradients and fluxes are central to conventional electrical activity in aerobic cells/organelles. The Murburn concept offers novel physico-chemical models for various metabolic, bioenergetic and electrophysiological phenomena. Here, we develop a foundational framework for neuronal electrical activity and axonal signal propagation using the electron-holding potential (EHP), a dimensionless field related logarithmically to electron chemical potential. By combining local redox relaxation dynamics with spatial transport driven by thermodynamic gradients, we derive a unified reaction-transport-relaxation equation that accounts for resting potential, excitability, waveform generation, and signal propagation within a single formalism. Nonlinear local redox kinetics yield a stable resting state and graded responses from a single scalar field; extending it to the two-variable excitable (FitzHugh Nagumo) form, a bistable reaction with a slow recovery variable, further yields a genuine threshold, all-or-none spikes, a refractory period and a propagating action potential. The framework accommodates known physiological variability of neurons and provides a direct bridge between metabolic/redox state and electrophysiology. This framework offers testable predictions for neuronal dynamics (such as velocity, waveform morphology, and environmental conditions) across biological systems. We derive and solve the equations to obtain the transmembrane potential as a function of time, and the neuronal conduction velocity as a function of parameters like ionic strength, temperature, axon diameter, myelination, and driving potential. In the second part of this work, we present comparative analyses, simulations, and experimental strategies for validation and falsification.