The unified cross-disciplinary model of the operation of neurons

Physics perfectly describes neuronal operation, provided that we take into account that biology uses slow, positively charged ions rather than electrons as charge carriers and remove untested ad hoc hypotheses that contradict science’s first principles. We also incorporate recent experimental discoveries into the outdated classic theoretical description. Lipid mechanisms are really very important for cellular biology, but they are certainly not suitable for describing the phenomena we discuss. We introduce the correct physical model, significantly enhancing the classic \gls{HH} model; furthermore, the fundamentally bio-electrically triggered operation leads to changes in the electrical, mechanical, and thermodynamic properties of living matter. We derive the resting potential from first principles of science, showing that it is unrelated to an ad hoc linear combination of mobilities or reversal potentials, as the \gls{GHK} equation claims. Furthermore, we derive an “equivalent thermodynamic electric field” that enables discussion of, among others, the operation of ion channels, their ion selectivity, and voltage sensing. We demonstrate that a simple electrical-thermodynamic control circuit regulates neuronal operation, setting and maintaining a stable resting potential and handling an unstable transient process known as the \gls{AP}. Its setpoint entirely defines the resting potential, explaining its robustness during growth and evolution. Our cross-disciplinary approach naturally fuses the electrical and mechanical/thermodynamic description of neuronal operation, resolves the decades-old mystery of “heat absorption” and “leakage current” (with their far-reaching consequences), and derives the thermodynamic description of neural computing. We defy that science cannot describe life.

💡 Research Summary

The paper presents a comprehensive, cross‑disciplinary framework for understanding neuronal operation that unifies electrical and thermodynamic descriptions. It begins by critiquing the two dominant historical models: the Hodgkin‑Huxley (HH) purely electrical formulation and the Heimburg‑Jackson thermodynamic (soliton) approach. Both are shown to inherit assumptions from their parent disciplines that are inappropriate for living tissue, chiefly the treatment of ionic currents as if they were electron flows (massless, instantaneous) and the neglect of ion mass, finite velocity, and mutual electrostatic repulsion.

The authors argue that biological currents consist of slow, positively charged ions whose dynamics are governed simultaneously by electric fields, pressure gradients, and chemical potentials. By explicitly incorporating ion mass and finite drift speed, the model bridges the “continuous” view of classical electromagnetism with the “discrete” nature of electrolyte physics. The neuronal membrane is modeled as two coupled capacitors—one representing the lipid bilayer, the other the aqueous electrolyte on each side. Between them, ions move, carrying both charge and momentum, thereby generating an “equivalent thermodynamic electric field” that the authors term electrical temperature.

From this construction the resting membrane potential emerges naturally as a non‑linear function of ion concentrations, membrane capacitance, and the thermodynamic electric field, without invoking the linear combination of permeabilities or reversal potentials that underlies the Goldman‑Hodgkin‑Katz (GHK) equation. The paper demonstrates mathematically that the GHK equation is an approximation that fails when ion drift speed and mass are taken into account.

A central conceptual advance is the representation of the neuron as a PID (proportional‑integral‑derivative) controller. The set‑point of this controller is the resting potential, determined by the physical parameters of the membrane‑electrolyte system. Feedback loops involving voltage‑dependent changes in electrical temperature and ion flux automatically maintain the set‑point, explaining the observed stability of the resting potential across development and species.

When a stimulus perturbs the membrane voltage, the system undergoes a transient response that the authors model as a damped oscillation. By mapping the voltage‑current trajectory onto a Carnot‑type thermodynamic cycle, they derive the entropy production, free‑energy consumption, and theoretical efficiency of an action potential (AP). This analysis resolves the long‑standing “heat absorption” versus “heat release” paradox: the heat exchanged during an AP is shown to be a reversible component of the thermodynamic cycle, not a loss. Consequently, the so‑called leakage current is reinterpreted as a necessary element of the control circuit that balances charge and mass flow.

The paper further applies the unified framework to classic neuronal phenomena: ion selectivity, voltage sensing, gating currents, and the operation of ion pumps. In each case, the authors argue that ion channels act as passive conduits; the active driving forces arise from the coupled electrical‑thermodynamic state of the membrane rather than from mysterious protein conformational changes.

Experimental implications are discussed in detail. The authors provide formulas for extracting thermodynamic parameters (entropy change, efficiency) directly from measured voltage and current waveforms, offering a practical route to test the model. They also outline how variations in membrane thickness, myelination, and extracellular ion composition affect both electrical and thermodynamic aspects of signal propagation, thereby unifying explanations for speed differences between myelinated and unmyelinated fibers.

In the concluding sections, the authors assert that the apparent incompatibility between electrical and thermodynamic theories is a product of disciplinary silos rather than a fundamental physical limitation. By adopting a “non‑disciplinary” perspective—using the same first principles but with appropriate approximations for living matter—they claim to have resolved contradictions that have persisted for decades. The paper ends with a philosophical statement that life, including cognition, does not escape the reach of physics, and that a unified physical model can serve as a foundation for future quantitative neuroscience.

Overall, the manuscript offers a bold, mathematically grounded synthesis that re‑derives the resting potential, explains the action potential as a thermodynamic engine, and positions the neuron as an intrinsic control system. If experimentally validated, this framework could reshape computational modeling, inform the design of bio‑inspired electronics, and provide a new lens through which to interpret neurophysiological data.