Beyond Hodgkin-Huxley—The Ionic-Mechano-Hydraulic (IMH) Model of Nerve Conduction

1. Introduction

The Hodgkin-Huxley (HH) equations, published in 1952, provide a quantitative description of the action potential that remains the foundation of computational neuroscience [3]. Their predictive power is not in question. What is in question is their physical interpretation. Hodgkin and Huxley were explicit on this point:

“We do not wish to suggest that the equations we have used […] necessarily bear any close relationship to the actual physical process involved.”

The equations are a phenomenological fit to data obtained from a specific preparation—the naked giant squid axon with replaced axoplasm [4].

Baker, Hodgkin, and Shaw demonstrated that action potentials persist after axoplasm replacement with saline, and concluded, without further experimental tests, that the eliminated components were functionally neutral.

The IMH model (Ionic-Mechano-Hydraulic: ionic gel phase transition as the primary event, mechanical compression as the trigger, and hydraulic wave propagation as the conduction mechanism) proposes that this conclusion was incorrect and that the cytoplasmic gel, structured water, adsorbed ions, and phase transitions eliminated by the preparation are the primary mechanism of conduction.

We hereafter use the acronym IMH to emphasize the causal hierarchy: ionic desorption precedes and causes the mechanical and hydraulic events that follow.

The genealogy of the dominant model contains two earlier assumptions that deserve scrutiny.

In 1902, Julius Bernstein applied the Nernst equation to quantify the resting membrane potential [2], on the advice of Wilhelm Ostwald.

The Nernst equation is rigorously valid only for a single ion species, an ideal semipermeable membrane, under thermodynamic equilibrium [1] conditions, the axonal cytoplasm does not satisfy.

This application was a convenient approximation that was never validated in the biological context; it was adopted and transmitted. The HH model inherits this unverified foundation.

A further structural issue concerns the preparation itself. Young (1936) established that the giant squid axon is not a single axon but a developmental fusion of hundreds of smaller axons [5], with an uncharacterised internal hydraulic architecture. The canonical preparation of twentieth-century electrophysiology was therefore neither structurally representative nor simple.

The present work does not propose to discard the HH model.

It proposes to explain it: to identify the physical substrate from which its equations emerge as a first-order projection, and to provide the mechanistic account its authors called for.

Section 2 describes the biophysical foundations of the IMH model.

Section 3 presents convergent evidence from independent experimental observations.

Section 4 states falsifiable predictions.

Section 5 places the model in an evolutionary context.

The Discussion and Conclusions assess the relationship between the two frameworks.

2. The IMH Model

2.1. The Cytoplasmic Gel: Ling’s Foundation

Gilbert Ling demonstrated that the cytoplasm is a structured polyelectrolyte gel in which the majority of the intracellular ${\mathrm{K}}^{+}$ is adsorbed at the protein sites rather than dissolved in a free solution [6]. The resting ionic distribution is a thermodynamically stable Donnan equilibrium, expressed in equation (1), where R is the gas constant, T the absolute temperature, F the Faraday constant, and the ratio in brackets is the K concentration gradient ⁺ across the gel boundary:

$$E_{\mathrm{Donnan}}=-{\displaystyle \frac{RT}{F}}ln\left({\displaystyle \frac{{\left[{\mathrm{K}}^{+}\right]}_{\mathrm{in}}}{{\left[{\mathrm{K}}^{+}\right]}_{\mathrm{out}}}}\right)$$

(1)

Equation (1) differs from the Nernst equation in a critical respect: it describes a true thermodynamic equilibrium of the gel system as a whole, not a single-ion diffusion potential across an ideal membrane.

This equilibrium is the energetically favorable state of the charged gel; it requires no metabolic pump to be maintained.

Tamagawa formalized this framework in rigorous thermodynamic terms and quantitatively demonstrated that observed membrane potentials are consistent with Donnan equilibrium without pump-dependent gradient maintenance [7].

In this sense, the ${\mathrm{Na}}^{+}$ / ${\mathrm{K}}^{+}$-ATPase pump performs a regulatory and not a primary electrogenic function.

The adsorption of ${\mathrm{K}}^{+}$ at gel sites follows a Langmuir isotherm, with affinity constants ordered by the Hofmeister ion series [8]. In equation (2), N is the total density of the adsorption sites, $K_a$ is the adsorption affinity constant for ${\mathrm{K}}^{+}$ and ${\left[{\mathrm{K}}^{+}\right]}_{\mathrm{free}}$ is the concentration of unbound ${\mathrm{K}}^{+}$ in the gel interstitium:

$${\left[{\mathrm{K}}^{+}\right]}_{\mathrm{ads}}={\displaystyle \frac{N·K_a·{\left[{\mathrm{K}}^{+}\right]}_{\mathrm{free}}}{1+K_a·{\left[{\mathrm{K}}^{+}\right]}_{\mathrm{free}}}}$$

(2)

Equation (2) has a direct physical consequence: when mechanical compression increases ${\left[{\mathrm{K}}^{+}\right]}_{\mathrm{free}}$ locally, the isotherm shifts and the adsorption sites become transiently saturated, releasing a pulse of free ${\mathrm{K}}^{+}$ into the periaxonal space. This pulse is the ionic trigger of the hydraulic wave.

2.2. The Hydraulic Wave

Mechanical compression of the axon triggers the desorption of ${\mathrm{K}}^{+}$ from the gel sites. Desorbed ions reconstruct their hydration shells, generating a local osmotic pressure gradient and hydraulic flux. A pressure wave propagates in the periaxonal space (12–15 nm width) at a velocity governed by the Korteweg-Moens equation adapted for a fluid-filled elastic tube of small radius:

$$v=\sqrt{{\displaystyle \frac{K_{\mathrm{eff}}}{\rho }}}$$

(3)

$$K_{\mathrm{eff}}={\left({\displaystyle \frac{1}{K_{\mathrm{fluid}}}}+{\displaystyle \frac{2r}{E_{\mathrm{myelin}}·h}}\right)}^{-1}$$

(4)

where $E_{\mathrm{myelin}}$ is the elastic modulus of the myelin sheath, r is the radius of the periaxonal canal, h is the thickness of the sheath, $K_{\mathrm{fluid}}$ is the bulk modulus of the periaxonal fluid and $\rho$ is its density. Equation (4) contains the central experimental prediction of the model: the conduction velocity is determined by the elastic modulus of myelin, not by the density of the sodium channel.

The Heimburg-Jackson thermodynamic soliton model established that the lipid membrane near a phase transition supports mechanically reversible wave propagation [9]. The IMH model integrates this as a component of a three-way thermal balance, expressed in equation (5), where the three terms represent respectively the heat absorbed by the membrane lipid phase transition, the heat released by ${\mathrm{K}}^{+}$ resorption onto gel sites during recovery, and the heat exchanged with the structured water shell surrounding the desorbed ions.

$$\Delta Q_{\mathrm{total}}=\Delta Q_{\mathrm{lipid}}+\Delta Q_{\mathrm{gel}}+\Delta Q_{\mathrm{water}}\approx 0$$

(5)

The near-zero net heat exchange measured during the action potential [11] is a thermodynamic constraint. Equation (5) satisfies it by construction, because the three contributions are thermodynamically coupled and partially cancel: what the membrane lipid phase transition absorbs, the gel recovery releases.

The HH model, which is irreversible by design, does not account for this constraint.

2.3. The Universal Sigmoid as Gel Thermodynamics

Every known biological sensory receptor—mechanoreceptor, photoreceptor, nociceptor, thermoreceptor, and chemoreceptor—exhibits a sigmoidal stimulus-response curve.

In the HH framework, this universality requires a separate molecular justification for each receptor class. In the IMH model, it is a necessary consequence of polyelectrolyte gel thermodynamics, expressed in equation (6), where $R\left(S\right)$ is the receptor response as a function of stimulus intensity S, $R_{\max }$ is the maximum response, $S_{\mathrm{threshold}}$ is the activation threshold, and k is the slope parameter governing transition sharpness:

$$R\left(S\right)={\displaystyle \frac{R_{\max }}{1+e^{-k(S-S_{\mathrm{threshold}})}}}$$

(6)

Equation (6) is not fitted to receptor data post-hoc: its three parameters are determined independently by the physical properties of the gel. $S_{\mathrm{threshold}}$ is the minimum compression for a self-sustaining hydraulic wave (a geometric condition of the periaxonal canal); k is the gel surface-to-volume ratio; and $R_{\max }$ is determined by the total density of the adsorption site.

The sigmoid emerges from gel thermodynamics, not from curve fitting. The diversity of receptors between species reflects the diversity of hydraulic architectures, not the diversity of molecular transduction mechanisms. This reformulates von Uexküll’s Umwelt principle [14] in biophysical terms: the perceptual world of each species is the hydraulic geometry of its peripheral nervous system made accessible to it.

2.4. The Axon-Schwann Cell Couple as Hydraulic Waveguide

The key architectural unit of the IMH model is not the axon in isolation but the axon-Schwann cell couple. Santiago Ramón y Cajal’s neuron doctrine (1906), while correct regarding synaptic connectivity, imposed an analytical separation of the axon from its Schwann cell that made the periaxonal space conceptually invisible. The Schwann cell is not passive support: it provides the elastic wall of the hydraulic tube and contributes to the regulation of periaxonal ionic composition.

Pannese documented the structural continuity of this couple in detail [15].

A century of electrophysiology conducted on the isolated axon –with the elastic wall removed, the periaxonal space disrupted, and the gel replaced – was conducted on half of the functional unit, without ever testing whether the other half was neutral.

3. Convergent Evidence

3.1. Huxley and Stämpfli 1949: The Anomaly That Was Named and Left Unexplained

In their study of saltatory conduction in the sciatic nerve of frogs, Huxley and Stämpfli recorded a result they described in their own words as “impossible in a resistance and capacity system [10]: a positive current entered the axis cylinder at node $N+1$before the membrane potential at node N had reached its maximum. This directly contradicts the temporal logic of any RC-based propagation model, in which the downstream node can only be activated after the upstream node has completed depolarization.

The arithmetic clarifies the constraint.

For a frog sciatic nerve with conduction velocity $v=60$ m/s and internodal length $L=1$ mm:

$$\Delta t_{N\to N+1}={\displaystyle \frac{L}{v}}={\displaystyle \frac{1\times {10}^{-3}}{60}}\approx 17\mu \mathrm{s}$$

(7)

The duration of the action potential at a single node is approximately 1 ms. Node $N+1$ therefore activates when node N has consumed only:

$${\displaystyle \frac{\Delta t}{{\tau }_{\mathrm{AP}}}}={\displaystyle \frac{17\mu \mathrm{s}}{1000\mu \mathrm{s}}}=0.017$$

(8)

That is, 1.7% of its duration of action potential. In the electrical model, this requires that node N generates sufficient axial current within 17 $\mu$s to depolarize node $N+1$ past the threshold, a demand inconsistent with the measured RC time constants of the nodal membrane.

In the IMH model, the periaxonal pressure wave arrives at node $N+1$ in 17 $\mu$s independently of the electrical state of node N, because the wave is the primary event and the electrical response is its consequence. The 1949 anomaly is resolved without modification.

3.2. The RC Invariance Argument

In classical cable theory, $\tau =R_{\mathrm{axial}}·C_{\mathrm{membrane}}$. Both resistance and capacitance scale with internodal length L, as shown in equation (9), where ${\rho }_i$ is axoplasm resistivity, r is axon radius, $c_m$ is specific membrane capacitance and L is internodal length:

$$R_{\mathrm{axial}}={\displaystyle \frac{{\rho }_{i}L}{\pi r^2}},C_{\mathrm{membrane}}=c_m·2\pi r·L$$

(9)

Equation (9) shows directly that $\tau =R_{\mathrm{axial}}·C_{\mathrm{membrane}}$ scales as $L^2$: doubling the internodal length doubles resistance and doubles capacitance, quadrupling the time constant. The apparent velocity gain attributed to increased internodal spacing in myelinated fibers is therefore an arithmetic artifact: the RC reduction from lower capacitance per unit length is exactly canceled by a proportional resistance increase. Myelination cannot accelerate conduction through the electrical mechanism alone. The hydraulic model does not require such cancelation: the velocity is determined directly by $E_{\mathrm{myelin}}$ through equations (3) and (4).

3.3. Thermal Signatures of the Action Potential

Ichiji Tasaki documented over six decades a series of observations incompatible with the purely electrical model: longitudinal mechanical displacement of the myelin sheath coincident with the action potential, near-zero net heat exchange and anomalous bidirectional conduction results [11]. These observations were noted and set aside by the field for lack of a theoretical framework capable of integrating them. The IMH model provides this framework: Tasaki’s longitudinal myelin displacement is the macroscopic surface signature of the periaxonal pressure wave. His thermal measurements satisfy equation (5).

Masson and Gallot (2008) provided a more recent and quantitatively rigorous treatment of thermal exchange during the action potential [12], using a statistical physics model of ionic and water effusion through membrane nanochannels. Their model predicts a temperature variation of approximately 22 $\mu$K for a 10 $\mu$m radius axon, in good agreement with experimental measurements [11]. Crucially, Masson and Gallot identified coupled water flux as a necessary component of the thermal account—a conclusion that converges independently on the IMH framework. However, their model remains within the interpretive structure of the HH: the effusion is transmembranous, the channels are the primary actors, and the near-zero net thermal balance expressed in equation (5) is not addressed.

The IMH model proposes the missing constraint: the three-way coupling of lipid, gel, and water contributions is the physical reason why the net exchange approaches zero—not a coincidence to be quantified after the fact.

3.4. The Cnidarian Nematocyst as Macroscopic Proof of Principle

The nematocyst of cnidarians demonstrates the ionic-hydraulic coupling mechanism on a directly measurable scale [13]. The organelle withstands an osmotic pressure of 150 bar, discharges in 700 ns, and produces an acceleration of $5.4\times {10}^6$ g. The trigger is ${\mathrm{Ca}}^{2+}$ desorption from a polyanionic capsule matrix, the same ionic-gel phase transition proposed for the periaxonal space in vertebrate axons, operating with measurable parameters.

Cnidarians are among the first animals with a nervous system (>500 Myr), which established the ionic-hydraulic mechanism as evolutionarily ancient. The nematocyst is the bottom of the hydraulic fractal: the same principle operating at the nanometer– $\mu$m scale in 700 ns operates at the nanometer– $\mu$ m scale in milliseconds in the axon and at larger scales over longer time constants in the astrocytic syncytium and glymphatic system.

3.5. Axonal Mechanics: The Gel Identity of the Cytoskeleton

Independent biomechanical evidence for the gel nature of the interior of the axon was provided by Dubey et al. (2020), using a strain-controlled optical-fiber force apparatus in chick dorsal root ganglion axons [18]. The authors demonstrate three properties that are diagnostic of a polyelectrolyte gel rather than a simple viscous fluid or a microtubule cable: (i) the axon exhibits a strain softening response in which the steady-state elastic modulus decreases with increasing strain; (ii) the long-time behavior is that of a viscoelastic solid, with a non-zero steady-state tension and memory of the initial state; (iii) the dominant mechanical contributor is not the microtubule bundle but the actin-spectrin periodic lattice, which buffers tension by reversible unfolding of the repeat domains of the spectrin.

These findings were obtained by researchers working entirely within the framework of biophysics and cytoskeletal mechanics, without reference to the HH model or nerve conduction. Their characterization of the axonal cortex as a tension-buffering gel with solid-like long-time behavior and strain-softening is precisely the material description required by the IMH model for the substrate that undergoes an ionic-hydraulic phase transition during the action potential. The convergence is independent and unintentional.

4. Predictions

4.1. Myelin Elastic Modulus and Conduction Velocity

Prediction: Conduction velocity in myelinated axons correlates with the elastic modulus $E_{\mathrm{myelin}}$ of the myelin sheath (measurable by atomic force microscopy in isolated fibers), independently of the channel density ${\mathrm{Na}}^{+}$.

The HH model predicts that the velocity is determined by the kinetics and density of the channels.

The IMH model predicts that it is determined by myelin rigidity via equations (3)–(4). The two predictions are orthogonal and experimentally separable. Demyelinating conditions that reduce $E_{\mathrm{myelin}}$ should slow conduction in proportion to $\sqrt{E_{\mathrm{myelin}}}$, not in proportion to channel loss.

4.2. Mechanoreceptor Adaptation as Hydraulic Geometry

Prediction: Altering the fluid volume or viscosity of sensory corpuscles (Pacinian, Meissner, Krause end bulbs) without modifying receptor channel composition should shift the adaptation rate and frequency selectivity according to hydraulic equilibration time calculations derived from equation (4).

In the HH framework, adaptation is a property of channel kinetics. In the IMH model, it is a property of the hydraulic geometry of the corpuscle.

The two models make quantitatively different predictions for the same viscosity manipulation.

4.3. Terminal Arborisations: Slowness as Geometric Amplifier of the Umwelt

Sensory terminal arborisations are the finest, slowest branches of the peripheral nervous system.

Within the HH framework, this slowness is a physical limitation of small-diameter unmyelinated fibers.

Within the IMH model, it is an evolutionary calibration: the temporal bandwidth of biologically relevant events determines the hydraulic geometry of the terminal arbor.

A punctate mechanical stimulus activates multiple terminal branches at different distances from the first node. The resulting delays in arrival between branches encode the spatial geometry of the stimulus as a temporal signature, as expressed in equation (10), where $L_i$ and $L_j$ are the lengths of two terminal branches reaching the same receptive field point through different paths, and $v_{\mathrm{terminal}}$ is the hydraulic wave velocity in those branches:

$$\Delta t_{ij}={\displaystyle \frac{L_i-L_j}{v_{\mathrm{terminal}}}}$$

(10)

Equation (10) establishes the fundamental encoding principle: $\Delta t_{ij}$ is the temporal delay between the two hydraulic messages reaching the first node of the arborisation from the same stimulus point. This delay is a spatial coordinate transformed into a temporal one. Slowness amplifies geometry.

The motor arborisation inverts the principle: synchrony of muscle fiber contraction requires synchrony of hydraulic wave arrival at motor endplates. Equal branch lengths produce simultaneous arrival and maximal impulsive force; unequal branch lengths produce staggered arrivals and temporally distributed force.

Falsifiable prediction: The spatial resolution of a cutaneous receptor field correlates with the maximum branch length differential of its sensory arborisation, measurable by anatomical reconstruction. For motor units: the temporal profile of the compound EMG reflects terminal branch length distribution, distinguishable from predictions based on fiber conduction velocity alone.

4.4. Motor Tremor as Hydraulic Interference

Smooth motor fusion requires successive hydraulic waves to arrive at terminal arborisations in constructive temporal superposition. The critical firing frequency above which destructive interference produces tremor is given by equation (11), where $v_{\mathrm{hydraulic}}$ is the hydraulic wave velocity in the terminal branches and $L_{\max }\mathrm{branch}$ is the length of the longest branch in the motor arborisation:

$$f_{\mathrm{critical}}={\displaystyle \frac{v_{\mathrm{hydraulic}}}{L_{\max }\mathrm{branch}}}$$

(11)

Equation (11) provides a direct geometric interpretation of pathological tremor: if the firing frequency exceeds $f_{\mathrm{critical}}$, successive hydraulic wavefronts arrive at terminal endplates before the preceding wave has fully dissipated, producing destructive superposition and loss of smooth force summation. The three types of clinical tremor –Parkinsonian (4–6 Hz), essential (8–12 Hz), cerebellar (variable)—correspond to three distinct hydraulic regimes, each with a characteristic branch length distribution.

4.5. Composite Temporal Decoding and the Libet Latency

The peripheral nerve fibers span a conduction velocity range of 0.5 to –120 m/s. On a path length of 1 m, arrival delays range from 10 ms (A$\alpha$) to 1000 ms (C fibers). The Libet threshold of approximately 500 ms for conscious sensory perception is, in the IMH model, the minimum temporal integration window required to receive and decode the complete composite message, including its slow affective component of the C-fiber [16].

Prediction: Selective pharmacological blockade of C-fiber hydraulic transmission should abolish the subjective feel quality of a stimulus while preserving its fast discriminative component. Disruption of astrocytic gap junctions should selectively alter C-fiber signal integration while preserving fast-fiber processing [16].

4.6. Electrical Stimulation as Gel Trigger

The apparent success of external electrical field stimulation has been cited as evidence against purely mechanical models of nerve conduction.

In the IMH model, this objection is resolved by the polyelectrolyte gel. An external electric field acts directly on the charged gel matrix and adsorbed ions, triggering the desorption electrically rather than mechanically.

The same ionic-hydraulic phase transition is initiated; only the triggering stimulus differs.

Prediction: The threshold electric field for nerve stimulation correlates with the gel adsorption affinity constants (Hofmeister series position of the dominant adsorbed cation), not only with the membrane capacitance.

This can be tested with gel-modifying agents that shift the adsorption affinity without altering the membrane electrical properties.

4.7. Non-Contamination Between Adjacent Fibres

Adjacent axons in a nerve bundle transmit distinct signals simultaneously without crosstalk.

In the HH framework, isolation is provided by myelin insulation.

In the IMH model, each fiber is a sealed periaxonal canal; hydraulic waves cannot propagate between structurally independent tubes.

Prediction: Selective mechanical disruption of the periaxonal seal between two adjacent fibers—without affecting their electrical insulation—should produce hydraulic crosstalk detectable as correlated mechanical signals in both fibers simultaneously.

4.8. Electromagnetic Dipole Geometry

A propagating hydraulic wavefront generates a moving ion desorption/resorption dipole along the axon, producing a propagating electromagnetic dipole rather than a stationary monopole.

Prediction: High-resolution EEG/MEG source modeling of single-fiber or small-fascicle signals should detect a dipolar electromagnetic signature progressing at hydraulic velocity, quantitatively distinguishable from the monopolar source geometry predicted by HH.

5. Evolutionary Perspective

The gel generating hydraulic pressure in a confined space operates recursively across at least five biological scales: nematocyst (nm–$\mu$m, ns) [13], periaxonal axonal space ($\mu$m, ms), dendritic arborisations (mm, tens of ms), astrocytic syncytium (cm, s) and glymphatic system (organ, circadian).

The fractal geometry of neural arbors, from terminal arborisations to dendritic trees to cortical columns, may be the spatial imprint of this temporal recursion.

The Heimburg-Jackson soliton model [9] converges to the IMH framework from membrane thermodynamics: both models require reversible mechanical wave propagation and near-zero net heat exchange.

The two frameworks are complementary, addressing different physical levels of the same phenomenon.

Damasio’s identification of C fibers as the primary substrate of felt experience [16] provides an independent clinical convergence: the slow peripheral hydraulic message arriving last is the affective dimension of perception, not noise.

Descartes attributed neural function to hydraulic spirits; Damasio rediscovered the role of slow peripheral signals through clinical observation; the IMH model provides the biophysical substrate.

6. Discussion

The IMH model does not falsify the HH equations. It explains them.

The HH model accurately describes the electrical correlates of the action potential in a preparation that has removed the gel, the periaxonal space, the Schwann cell, and mechanical coupling from the measurement.

In those conditions, the electrical description is correct and complete.

The IMH claim is that these conditions do not describe physiological nerve conduction and that the physical process in the intact fiber has a hydraulic primary layer that the HH electrical measurements capture only as a projection.

Barz et al. (2013) proposed a purely mechanical pressure-wave model in which ion channels are pressure-gated rather than voltage-gated [17].

That model correctly identified the wave as primary but lacked ionic-gel coupling: it had no threshold mechanism, no sigmoid, no ionic selectivity, and no account of electrical stimulation.

The IMH model provides the missing components through the Ling-Tamagawa polyelectrolyte gel framework.

The need for such an extension is recognized within mainstream modeling. Peets, Tamm, and Engelbrecht (2023), reviewing the state of mathematical models of nerve signal propagation, explicitly call for the incorporation of the cytoskeleton as a potentially primary actor, citing evidence that its removal alters the response to axoplasmic pressure by an order of magnitude [19].

They concluded that the cytoskeleton could prove “as important for signal propagation in nerves as the cell membrane.” The IMH model answers this call.

The theoretical positioning of the present model within the broader landscape of non-HH frameworks is clarified by Drukarch and Wilhelmus (2023), who distinguish two approaches to the multiphysics of the nerve signal: a bottom-up mechanistic approach that retains HH as the driving layer and adds mechanical coupling as an epiphenomenon, and a top-down thermodynamic approach that treats the nerve signal as emerging from the collective physico-chemical properties of the axolemma-ectoplasm complex [20].

The IMH model belongs unambiguously to the second category. Drukarch and Wilhelmus, moreover, provide an extensive treatment of Tasaki’s gel phase transition framework — the bistable ${\mathrm{Ca}}^{2+}{\mathrm{/Na}}^{+}$ ectoplasmic gel, volume phase transition, cooperative ion exchange, and refractory period as re-compaction kinetics — which constitutes the direct conceptual precursor of the ionic-gel layer of the present model. Their conclusion, that neuroscience “should welcome and be open to different perspectives on modeling and explanatory understanding of the physics of the nerve signal,” applies directly to the present work, and their publication in a mainstream peer-reviewed venue signals that this openness is no longer confined to the margins of the field.

An additional convergence deserves explicit recognition.

Manoj and Jaeken (2023), proposing the murburn concept as a unifying framework for cellular bioenergetics, situate Ling’s association-induction hypothesis as a foundational precursor shared by several non-classical schools, and cite Tamagawa and Matveev directly as contemporary representatives of this tradition [21].

Tamagawa is himself a co-author of two murburn electrophysiology papers, one providing a critical comparative analysis of the membrane-pump and association-induction frameworks [22], and one repositioning ${\mathrm{Na}}^{+}$ / ${\mathrm{K}}^{+}$-ATPase as a thermodynamic equilibrium facilitator rather than an active electrogenic pump [23]—a conclusion that directly corroborates the regulatory, non-primary role assigned to the pump in Section 2.1 of the present work.

The murburn framework, centered on diffusible reactive species (DRS) as primary agents of ATP synthesis and cellular coherence, addresses the bioenergetic layer that the present model does not treat explicitly.

The two frameworks are complementary rather than competing: the IMH model describes the propagation physics of the nerve signal, while the murburn framework addresses the metabolic resetting that restores the gel to its adsorption-competent state between impulses.

In this reading, the refractory period acquires a biochemical correlate, DRS–mediated gel recompaction, that is absent from both HH and purely mechanical wave models.

Ling’s polyelectrolyte gel thus serves as a common substrate linking bioenergetics (murburn), resting potential (Donnan-Tamagawa) and propagation (the present model): three aspects of a single physical object seen from three disciplinary vantage points.

The principal limitation of the present work is theoretical: the model has not yet been subjected to systematic quantitative fitting against the full HH dataset.

Equations (3) – (4) yield numerical predictions for conduction velocity as a function of measurable mechanical parameters ($E_{\mathrm{myelin}}$, r, h), but these predictions have not yet been quantitatively compared with the extensive velocity-diameter data set available for vertebrate nerve fibers. This comparison is a necessary next step.

A second limitation concerns the preparation of the giant squid axon. Because this axon is a fusion of hundreds of smaller axons [5], its hydraulic architecture is unknown.

The HH data set, obtained on this preparation, cannot straightforwardly constrain a hydraulic model designed for single-axon geometry.

A purpose-designed experimental protocol is required on single myelinated mammalian fibers with intact periaxonal space.

7. Conclusions

The IMH model proposes that the action potential is a coupled ionic-hydraulic phase transition in which mechanical compression triggers ${\mathrm{K}}^{+}$ desorption from cytoplasmic gel sites, generating a pressure wave in the periaxonal space.

Electrical events are the measurable consequence of this wave, not its cause. The model resolves, without post-hoc adjustment, the 75-year anomaly of Huxley and Stämpfli (1949) [10], accounts for the nearly zero net heat exchange of the action potential [11], and provides a unified physical basis for the universal sigmoidal stimulus-response curve of biological sensors.

Eight falsifiable predictions are presented, each testable with existing or near-existing experimental methods.

The model is wrong if conduction velocity is found to be independent of myelin elastic modulus, or if mechanoreceptor adaptation frequency is found to be independent of corpuscle fluid properties, or if terminal arborisation branch length distributions are found to be uncorrelated with receptor field spatial resolution.

The Hodgkin-Huxley model served seven decades with distinction. The IMH model does not request its rejection.

It asks for the experiment.