Hodgkin-Huxley Model Research Articles

Steady-state Approximations for Hodgkin-Huxley Cell Models: Towards Multi-scale Models of Uterine Smooth Muscle

The Tong, et al. 2011 publication presents a comprehensive mathematical model of electro- mechanical behaviour in a uterine smooth muscle cell. It incorporates ion channel currents into a classic Hodgkin-Huxley membrane voltage formulation as well as Ca2+ concentration dynamics with pumps and exchangers. Mechanical force generation by way of the Hai-Murphy smooth muscle cell model is computed based on the Ca2+ concentrations. The ‘full’ Tong, 2011 model (FTM) includes nineteen ordinary differential equations for the ion channel activation/inactivation variables out of a total of 105 equations overall. An effort at reducing the FTM into a smaller number of equations for computational efficiency – the ‘reduced’ Tong model (RTM) – aims at reproducing the overall behaviour of the FTM without excessive detail. We present here the CellML implementation of the RTM for simulating the FTM within the context of the reduced model’s primary publication as derived from the Tong, et al. 2011 model.

Phase synchronization in a sparse network of randomly connected neurons under the effect of Poissonian spike inputs

This article investigates the emergence of phase synchronization in a network of randomly connected neurons by chemical synapses. The study uses the classic Hodgkin–Huxley model to simulate the neuronal dynamics under the action of a train of Poissonian spikes. In such a scenario, we observed the emergence of irregular spikes for a specific range of conductances and also that the phase synchronization of the neurons is reached when the external current is strong enough to induce spiking activity but without overcoming the coupling current. Conversely, if the external current assumes very high values, then an opposite effect is observed, i.e., the prevention of the network synchronization. We explain such behaviors considering different mechanisms involved in the system, such as incoherence, minimization of currents, and stochastic effects from the Poissonian spikes. Furthermore, we present some numerical simulations where the stimulation of only a fraction of neurons, for instance, can induce phase synchronization in the non-stimulated fraction of the network, besides cases in which for larger coupling values, it is possible to propagate the spiking activity in the network when considering stimulation over only one neuron.

Establishment of a Mathematical Model for Parkinson's Disease Based on Complex Neural Networks and Design of Deep Brain Electrical Stimulation Treatment Plans.

This article introduces the modeling idea of complex neural networks based on the analysis of Parkinson's disease(PD) seizures. According to the Hodgkin-Huxley model of neurons and the synaptic connection theory between neurons, a theoretical model of the basal ganglia circuit was established. To reveal the working mechanism of the brain during the attack of nervous system disease. On this basis, the neural system model is combined with the control theory. Finally, an intermittent adaptive feedback controller is proposed to effectively suppress the onset of Parkinson's disease by stimulating the neuronal system accordingly.

Memristor neurons and their coupling networks based on Edge of Chaos Kernel

Chua's theory of local activity shows that local activity is the origin of complexity, and the complexity can only occur on or near the stable locally active domain, referred to as Edge of Chaos (EOC). Very recently, for the voltage-controlled locally active memristors, a new physical concept dubbed “Edge of Chaos Kernel” (EOCK), which consists of a series combination between a negative resistance and a negative inductance and exhibits the EOC phenomenon of being stable yet potentially unstable, was defined and applied to the Hodgkin-Huxley neural circuit model. When an EOCK is coupled to a passive environment, its stability is disrupted, resulting in the emergence of action potentials, chaos and various complex phenomena. This paper proposes the dual version of the EOCK called as R-C EOCK, which consists of the parallel combination between a negative resistance and a negative capacitance. We show that the actual NbO memristor manufactured by NaMLab essentially belongs to a current-controlled locally active memristor which contains a R-C EOCK and gives the signature of its EOCK and EOC. We construct a second-order neuron based on the NbO memristor when connected in parallel with a passive capacitor, and further prove that only memristors endowed with an EOCK can generate action potential. On this basis, we construct a minimum cellular neural network with only 7 components based on two NbO memristor neurons and a passive coupling resistor, in which neuromorphic behaviors of static and dynamic pattern formation may emerge if and only if the single neuron has an EOCK and is poised on the EOC. The analysis in this paper explains the dynamic mechanism of Smale's paradox, in which two mathematically dead neurons coupled by a passive environment may become alive, under the same or different input current excitation, which are more in line with the actual biological neural networks.

Modeling ultrasound modulation of neural function in a single cell

BackgroundLow intensity ultrasound stimulation has been shown to non-invasively modulate neural function in the central nervous system (CNS) and peripheral nervous system (PNS) with high precision. Ultrasound sonication is capable of either excitation or inhibition, depending on the ultrasound parameters used. On the other hand, the mode of interaction of ultrasonic waves with the neural tissue for effective neuromodulation remains ambiguous. New methodHere within we propose a numerical model that incorporates the mechanical effects of ultrasound stimulation on the Hodgkin-Huxley (HH) neuron by incorporating the relation between increased external pressure and the membrane induced tension, with a stress on the flexoelectric effect on the neural membrane. The external pressure causes an increase in the total tension of the membrane thus affecting the probability of the ion channels being open after the conformational changes that those channels undergo. ResultsThe interplay between varying the acoustic intensities and frequencies depicts different action potential suppression rates, whereby a combination of low intensity and low frequency ultrasound sonication proved to be the most effective in modulating neural function.Comparison with Existing Methods: Our method solely depends on the HH model of a single neuron and the linear flexoelectric effect of the dielectric neural membrane, when under an ultrasound-induced mechanical strain, while varying the ion-channels conductances based on different sonication frequencies and intensities. We study the effect of ultrasound parameters on the firing rate, latency, and action potential amplitude of a HH neuron for a better understanding of the neuromodulation modality of ultrasound stimulation (in the continuous and pulsed modes). ConclusionsThis simulation work confirms the published experimental data that low intensity and low frequency ultrasound sonication has a higher success rate of modulating neural firing.

Synchronization of delayed coupled neurons with multiple synaptic connections.

Synchronization is a key feature of the brain dynamics and is necessary for information transmission across brain regions and in higher brain functions like cognition, learning and memory. Experimental findings demonstrated that in cortical microcircuits there are multiple synapses between pairs of connected neurons. Synchronization of neurons in the presence of multiple synaptic connections may be relevant for optimal learning and memory, however, its effect on the dynamics of the neurons is not adequately studied. Here, we address the question that how changes in the strength of the synaptic connections and transmission delays between neurons impact synchronization in a two-neuron system with multiple synapses. To this end, we analytically and computationally investigated synchronization dynamics by considering both phase oscillator model and conductance-based Hodgkin-Huxley (HH) model. Our results show that symmetry/asymmetry of feedforward and feedback connections crucially determines stability of the phase locking of the system based on the strength of connections and delays. In both models, the two-neuron system with multiple synapses achieves in-phase synchrony in the presence of small and large delays, whereas an anti-phase synchronization state is favored for median delays. Our findings can expand the understanding of the functional role of multisynaptic contacts in neuronal synchronization and may shed light on the dynamical consequences of pathological multisynaptic connectivity in a number of brain disorders.

Digital Hardware Implementation of Morris-Lecar, Izhikevich, and Hodgkin-Huxley Neuron Models With High Accuracy and Low Resources

Digital Hardware Implementation of Morris-Lecar, Izhikevich, and Hodgkin-Huxley Neuron Models With High Accuracy and Low Resources

Dynamic Analysis of Neuron Models

Based on the Hodgkin–Huxley theory, this paper establishes several nonlinear system models, analyzes the models’ stability, and studies the conditions for repetitive discharge of neuronal membrane potential. Our dynamic analysis showed that the main channel currents (the fast transient sodium current, the potassium delayed rectifier current, and the fixed leak current) of a neuron determine its dynamic properties and that the GHK formula will greatly widen the stimulation current range of the repetitive discharge condition compared with the Nernst equation. The model including the change in ion concentration will lead to spreading depression (SD)-like depolarization, and the inclusion of a Na-K pump will weaken the current stimulation effect by decreasing the extracellular K accumulation. The results indicate that the Hodgkin–Huxley model is suitable for describing the response to initial stimuli, but due to changes in ion concentration, it is not suitable for describing the response to long-term stimuli.

Modelling of FinFET based ON bipolar cell of silicon retina

—In this paper, we proposed a SOI FinFET based ON bipolar cell of silicon retina which plays an important role in neuromorphic applications like differential motion detection. Low power consumption along with biological plausibility is one of the crucial parameters of any neuromorphic retinal circuit as in the brain all biological systems consisting of billions of neurons consume power in terms of a few μwatts. First, the output of linkage cell between inner and outer plexiform layer i.e. bipolar cell is reconstructed and verified using developed ionic current based mathematical model which is established considering Hodgkin-Huxley model as the base model, and then biological behavior of the ON bipolar cell is mimicked through circuit using 180 nm, 90 nm and 45 nm Metal Oxide Semiconductor Field Effect Transistor (MOSFET). The power consumption in each case is obtained to be approximately 18.5 nW, 10.5 nW and 1 nW which is much larger than the power consumed by biological bipolar cell. Moreover, the same circuit is implemented with 32 nm Silicon on Insulator Fin Field Effect Transistor (SOI FinFET) device. It is observed that SOI FinFET based ON bipolar cell consumes 17.5 pW power which is less in comparison to MOSFET based bipolar cell. The output of bipolar cell is matched with biological nature of the waveform in each case.

Iontronic Nanopore Model for Artificial Neurons: The Requisites of Spiking.

Brain-inspired neuromorphic computing is currently being investigated for effective artificial intelligence (AI) systems. The development of artificial neurons and synapses is imperative to creating efficient computational biomimetic networks. Here we propose the minimal configuration of an effective iontronic spiking neuron based on a conical nanofluidic pore ionic diode. The conductance is composed of a Boltzmann open channel probability and a blocking inactivation function, forming the structure of a memristor. The presence of a negative resistance and the combination of activation-deactivation dynamics cause a Hopf bifurcation. Using the characteristic frequencies of small perturbation impedance spectroscopy, we discuss the conditions of spiking, in which the system enters a limit cycle oscillation. We arrive at the conclusion that an excitable neuron-like system can be made with a single active channel instead of the more complex combination of multiple channels that occurs in the Hodgkin-Huxley neuron model.

Single-compartment model of a pyramidal neuron, fitted to recordings with current and conductance injection.

For single neuron models, reproducing characteristics of neuronal activity such as the firing rate, amplitude of spikes, and threshold potentials as functions of both synaptic current and conductance is a challenging task. In the present work, we measure these characteristics of regular spiking cortical neurons using the dynamic patch-clamp technique, compare the data with predictions from the standard Hodgkin-Huxley and Izhikevich models, and propose a relatively simple five-dimensional dynamical system model, based on threshold criteria. The model contains a single sodium channel with slow inactivation, fast activation and moderate deactivation, as well as, two fast repolarizing and slow shunting potassium channels. The model quantitatively reproduces characteristics of steady-state activity that are typical for a cortical pyramidal neuron, namely firing rate not exceeding 30Hz; critical values of the stimulating current and conductance which induce the depolarization block not exceeding 80mV and 3, respectively (both values are scaled by the resting input conductance); extremum of hyperpolarization close to the midpoint between spikes. The analysis of the model reveals that the spiking regime appears through a saddle-node-on-invariant-circle bifurcation, and the depolarization block is reached through a saddle-node bifurcation of cycles. The model can be used for realistic network simulations, and it can also be implemented within the so-called mean-field, refractory density framework.

High consistency VO2 memristor for artificial auditory neuron

With scalability and diverse device behavior, memristors present potential for executing neuromorphic computation with lower hardware cost and power consumption. However, Low consistency currently limited the application of memristors. Herein, we report a VO2-based memristor fabricated through magnetron sputtering and multiple annealing processes, exhibiting extremely low in both cycle-to-cycle (C2C) and device-to-device (D2D) variations. Further, a Hodgkin-Huxley model neuron circuit based on the extremely high consistency of the devices is established, which achieves auditory neuron perception simulation through spatiotemporal processing of spike signals, allowing for clear differentiation of sound source direction and displaying patterns akin to biological behavior. This easily implemented and highly consistent artificial neuron offers a promising approach for the development of next-generation artificial auditory systems.

Neural energy computations based on Hodgkin-Huxley models bridge abnormal neuronal activities and energy consumption patterns of major depressive disorder

Limited by the current experimental techniques and neurodynamical models, the dysregulation mechanisms of decision-making related neural circuits in major depressive disorder (MDD) are still not clear. In this paper, we proposed a neural coding methodology using energy to further investigate it, which has been proven to strongly complement the neurodynamical methodology. We augmented the previous neural energy calculation method, and applied it to our VTA-NAc-mPFC neurodynamical H–H models. We particularly focused on the peak power and energy consumption of abnormal ion channel (ionic) currents under different concentrations of dopamine input, and investigated the abnormal energy consumption patterns for the MDD group. The results revealed that the energy consumption of medium spiny neurons (MSNs) in the NAc region were lower in the MDD group than that of the normal control group despite having the same firing frequencies, peak action potentials, and average membrane potentials in both groups. Dopamine concentration was also positively correlated with the energy consumption of the pyramidal neurons, but the patterns of different interneuron types were distinct. Additionally, the ratio of mPFC's energy consumption to total energy consumption of the whole network in MDD group was lower than that in normal control group, revealing that the mPFC region in MDD group encoded less neural information, which matched the energy consumption patterns of BOLD-fMRI results. It was also in line with the behavioral characteristics that MDD patients demonstrated in the form of reward insensitivity during decision-making tasks. In conclusion, the model in this paper was the first neural network energy computational model for MDD, which showed success in explaining its dynamical mechanisms with an energy consumption perspective. To build on this, we demonstrated that energy consumption levels can be used as a potential indicator for MDD, which also showed a promising pipeline to use an energy methodology for studying other neuropsychiatric disorders.

Steady-state approximations for Hodgkin-Huxley cell models: Reduction of order for uterine smooth muscle cell model.

Multi-scale mathematical bioelectrical models of organs such as the uterus, stomach or heart present challenges both for accuracy and computational tractability. These multi-scale models are typically founded on models of biological cells derived from the classic Hodkgin-Huxley (HH) formalism. Ion channel behaviour is tracked with dynamical variables representing activation or inactivation of currents that relax to steady-state dependencies on cellular membrane voltage. Timescales for relaxation may be orders of magnitude faster than companion ion channel variables or phenomena of physiological interest for the entire cell (such as bursting sequences of action potentials) or the entire organ (such as electromechanical coordination). Exploiting these time scales with steady-state approximations for relatively fast-acting systems is a well-known but often overlooked approach as evidenced by recent published models. We thus investigate feasibility of an extensive reduction of order for an HH-type cell model with steady-state approximations to the full dynamical activation and inactivation ion channel variables. Our effort utilises a published comprehensive uterine smooth muscle cell model that encompasses 19 ordinary differential equations and 105 formulations overall. The numerous ion channel submodels in the published model exhibit relaxation times ranging from order 10-1 to 105 milliseconds. Substitution of the faster dynamic variables with steady-state formulations demonstrates both an accurate reproduction of the full model and substantial improvements in time-to-solve, for test cases performed. Our demonstration here of an effective and relatively straightforward reduction method underlines the particular importance of considering time scales for model simplification before embarking on large-scale computations or parameter sweeps. As a preliminary complement to more intensive reduction of order methods such as parameter sensitivity and bifurcation analysis, this approach can rapidly and accurately improve computational tractability for challenging multi-scale organ modelling efforts.

Thinking about the action potential: the nerve signal as a window to the physical principles guiding neuronal excitability.

Ever since the work of Edgar Adrian, the neuronal action potential has been considered as an electric signal, modeled and interpreted using concepts and theories lent from electronic engineering. Accordingly, the electric action potential, as the prime manifestation of neuronal excitability, serving processing and reliable "long distance" communication of the information contained in the signal, was defined as a non-linear, self-propagating, regenerative, wave of electrical activity that travels along the surface of nerve cells. Thus, in the ground-breaking theory and mathematical model of Hodgkin and Huxley (HH), linking Nernst's treatment of the electrochemistry of semi-permeable membranes to the physical laws of electricity and Kelvin's cable theory, the electrical characteristics of the action potential are presented as the result of the depolarization-induced, voltage- and time-dependent opening and closure of ion channels in the membrane allowing the passive flow of charge, particularly in the form of Na+ and K+ -ions, into and out of the neuronal cytoplasm along the respective electrochemical ion gradient. In the model, which treats the membrane as a capacitor and ion channels as resistors, these changes in ionic conductance across the membrane cause a sudden and transient alteration of the transmembrane potential, i.e., the action potential, which is then carried forward and spreads over long(er) distances by means of both active and passive conduction dependent on local current flow by diffusion of Na+ ion in the neuronal cytoplasm. However, although highly successful in predicting and explaining many of the electric characteristics of the action potential, the HH model, nevertheless cannot accommodate the various non-electrical physical manifestations (mechanical, thermal and optical changes) that accompany action potential propagation, and for which there is ample experimental evidence. As such, the electrical conception of neuronal excitability appears to be incomplete and alternatives, aiming to improve, extend or even replace it, have been sought for. Commonly misunderstood as to their basic premises and the physical principles they are built on, and mistakenly perceived as a threat to the generally acknowledged explanatory power of the "classical" HH framework, these attempts to present a more complete picture of neuronal physiology, have met with fierce opposition from mainstream neuroscience and, as a consequence, currently remain underdeveloped and insufficiently tested. Here we present our perspective that this may be an unfortunate state of affairs as these different biophysics-informed approaches to incorporate also non-electrical signs of the action potential into the modeling and explanation of the nerve signal, in our view, are well suited to foster a new, more complete and better integrated understanding of the (multi)physical nature of neuronal excitability and signal transport and, hence, of neuronal function. In doing so, we will emphasize attempts to derive the different physical manifestations of the action potential from one common, macroscopic thermodynamics-based, framework treating the multiphysics of the nerve signal as the inevitable result of the collective material, i.e., physico-chemical, properties of the lipid bilayer neuronal membrane (in particular, the axolemma) and/or the so-called ectoplasm or membrane skeleton consisting of cytoskeletal protein polymers, in particular, actin fibrils. Potential consequences for our view of action potential physiology and role in neuronal function are identified and discussed.

Modulation of SK Channels via Calcium Buffering Tunes Intrinsic Excitability of Parvalbumin Interneurons in Neuropathic Pain: A Computational and Experimental Investigation.

Parvalbumin-expressing interneurons (PVINs) play a crucial role within the dorsal horn of the spinal cord by preventing touch inputs from activating pain circuits. In both male and female mice, nerve injury decreases PVINs' output via mechanisms that are not fully understood. In this study, we show that PVINs from nerve-injured male mice change their firing pattern from tonic to adaptive. To examine the ionic mechanisms responsible for this decreased output, we used a reparametrized Hodgkin-Huxley type model of PVINs, which predicted (1) the firing pattern transition is because of an increased contribution of small conductance calcium-activated potassium (SK) channels, enabled by (2) impairment in intracellular calcium buffering systems. Analyzing the dynamics of the Hodgkin-Huxley type model further demonstrated that a generalized Hopf bifurcation differentiates the two types of state transitions observed in the transient firing of PVINs. Importantly, this predicted mechanism holds true when we embed the PVIN model within the neuronal circuit model of the spinal dorsal horn. To experimentally validate this hypothesized mechanism, we used pharmacological modulators of SK channels and demonstrated that (1) tonic firing PVINs from naive male mice become adaptive when exposed to an SK channel activator, and (2) adapting PVINs from nerve-injured male mice return to tonic firing on SK channel blockade. Our work provides important insights into the cellular mechanism underlying the decreased output of PVINs in the spinal dorsal horn after nerve injury and highlights potential pharmacological targets for new and effective treatment approaches to neuropathic pain.SIGNIFICANCE STATEMENT Parvalbumin-expressing interneurons (PVINs) exert crucial inhibitory control over Aβ fiber-mediated nociceptive pathways at the spinal dorsal horn. The loss of their inhibitory tone leads to neuropathic symptoms, such as mechanical allodynia, via mechanisms that are not fully understood. This study identifies the reduced intrinsic excitability of PVINs as a potential cause for their decreased inhibitory output in nerve-injured condition. Combining computational and experimental approaches, we predict a calcium-dependent mechanism that modulates PVINs' electrical activity following nerve injury: a depletion of cytosolic calcium buffer allows for the rapid accumulation of intracellular calcium through the active membranes, which in turn potentiates SK channels and impedes spike generation. Our results therefore pinpoint SK channels as potential therapeutic targets for treating neuropathic symptoms.

Identification of Two FitzHugh-Nagumo Neuron Models Based on the Speed-Gradient and Filtering

The paper is devoted to the problem of parameter identification of two FitzHugh-Nagumo neuron models. The FitzHugh-Nagumo model is a simplification of the Hodgkin-Huxley model and it is very valuable for using on practice thanks to its simplicity. However, within an experiment only one variable of the FitzHugh-Nagumo model, the membrane potential, is measured, while another variable of cumulative effects of all slow ion currents responsible for restoring the resting potential of the membranes and both variables’ derivatives cannot be measured. This circumstance brings additional difficulties to the parameters estimation problem and, therefore, this case needs special attention. Firstly, the model was transformed to more simple form without unmeasured variables. Variables obtained from applying second-order real filter-differentiator were used instead of unmeasured derivatives in model’s equations. As a result, a linear equation was gotten and for this equation the identification goal, which guarantees correct parameters’ adjustment, was formulated and an adaptive system, parameters of which are estimations of original system’s parameters and an output of which estimates the output of the linear equation, was constructed. Then, the integral objective function was defined and the algorithm for the original model parameters identification was designed with the speed-gradient method. The results of computer simulation in the Simulink environment are presented. These results demonstrate that estimates of the model’s state and parameters converge to their true values rather fast. Unlike existing solutions of the FitzHugh-Nagumo identification problem, we propose a much easier deterministic algorithm. Moreover, the parameters are estimated for a system collected from two FitzHugh-Nagumo models, which opens perspectives for using the proposed method in modeling neuron population activity.

Representation of single neuron dynamics using 1-D and 2-D Discrete dynamical systems

Biological neurons are typically modeled using the Hodgkin-Huxley formalism, which requires significant computational power to simulate. However, since realistic neural network models require thousands of synaptically coupled neurons, a faster approach is needed. Discrete dynamical systems are promising alternatives to continuous models, as they can simulate neuron activity in far fewer steps. Many existing discrete models are based on Poincaré-map-like approaches, which trace periodic activity at a cross section of the cycle. However, this approach is limited to periodic solutions. Biological neurons have many key properties beyond periodicity, such as the minimum applied current required for a resting cell to generate an action potential. To address these properties, we propose a discrete dynamical system model of a biological neuron that incorporates the threshold dynamics of the Hodgkin-Huxley model, the logarithmic relationship between applied current and frequency, modifications to relaxation oscillators, and spike-frequency adaptation in response to modulatory hyperpolarizing currents. It is important to note that several critical parameters are transferred from the continuous model to our proposed discrete dynamical system. These parameters include the membrane capacitance, leak conductance, and maximum conductance values for sodium and potassium ion channels, which are essential for accurately simulating the behavior of biological neurons. By incorporating these parameters into our model, we can ensure that it closely approximates the continuous model’s behavior, while also offering a more computationally efficient alternative for simulating neural networks.

Engineering spatiotemporal patterns: information encoding, processing, and controllability in oscillator ensembles

The ability to finely manipulate spatiotemporal patterns displayed in neuronal populations is critical for understanding and influencing brain functions, sleep cycles, and neurological pathologies. However, such control tasks are challenged not only by the immense scale but also by the lack of real-time state measurements of neurons in the population, which deteriorates the control performance. In this paper, we formulate the control of dynamic structures in an ensemble of neuron oscillators as a tracking problem and propose a principled control technique for designing optimal stimuli that produce desired spatiotemporal patterns in a network of interacting neurons without requiring feedback information. We further reveal an interesting presentation of information encoding and processing in a neuron ensemble in terms of its controllability property. The performance of the presented technique in creating complex spatiotemporal spiking patterns is demonstrated on neural populations described by mathematically ideal and biophysical models, including the Kuramoto and Hodgkin-Huxley models, as well as real-time experiments on Wein bridge oscillators.

An Acoustoelectric Approach to Neuron Function

An acoustoelectric approach to neuron function is proposed that combines aspects of the widely accepted electrical-circuit-based Hodgkin–Huxley model for the generation and propagation of action potentials via electric polarization with mechanical models based on propagation via capillary waves. Explaining measured velocities of action potentials quantitatively, it also predicts the electrical tunability of highly anisotropic polarization packages that surf on the dynamic mechanical force field deforming the neuron membrane. It relies substantially on the local motion of dipoles formed by excess charges close to the inside surface of the neuron membrane, which in turn are anisotropically screened by water molecules in their hydration shell, thus modulating the strong electric field at the interface. As demonstrated on acoustic resonators of suspended nanowires fabricated out of amorphous dipolar silicon nitride, high electric fields combined with predominantly axial-strain modulation can cause transverse acoustoelectric polarization waves that propagate soliton-like with extremely low loss. In neurons, the modulation of electric polarization is confined in the nanometer-thin skin of a high electric field inside the neuron membrane and propagates phase-coherent along the axon as a lowest-order one-dimensional breathing mode, similar to transverse polarization pulses studied in nanowire resonators. Some experiments for the further manifestation of the model as well as topological protection of such breathing-mode polarization waves are discussed.