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Inspired by the motion processing pathway, this paper proposes a bio-inspired feedforward spiking network model based on Hodgkin–Huxley neurons for human motion perception. The proposed network mimics the mechanisms of direction selectivity found in simple and complex cells of the primary visual cortex. Simple cells' receptive fields are modeled using Gabor energy filters, while complex cells' receptive fields are constructed by integrating the responses of simple cells in an energy model. To generate the motion map, the spiking output of the network integrates motion information encoded by the responses of complex cells with various preferred directions. Simulation results demonstrate that the spiking neuron-based network effectively replicates the directional selectivity operation of the visual cortex when presented with a sequence of time-varying images. We evaluate the proposed model against state-of-the-art spiking neuron-based motion detection models using publicly available datasets. The results highlight the model's capability to extract motion energy from diverse video sequences, akin to human visual motion perception models. Additionally, we showcase the application of the proposed model in motion segmentation tasks and compare its performance with state-of-the-art motion-based segmentation models using challenging video segmentation benchmarks. The results indicate competitive performance. The motion maps generated by the proposed model can be utilized for action recognition in input videos. [ABSTRACT FROM AUTHOR]

This study aims to consider an ensemble of hippocampal neurons coupled in a ring, which may be responsible for generation of the primary rhythm at limbic epilepsy. Methods. Model equations were solved numerically. To determine the areas of oscillatory and excitable regime existance for a single neuron, the bifurcation analysis for the leakadge conductivity parameter was performed. The coupling delays was not implemented directly, instead, inertia in the synapse was introduced. To determine the stability of generation some couplings were removed and parameter detunig was introduced. Results. In the single neuron model the bistability region was detected, in which a stable focus coexhists with a limit cycle. Two main synchronous regimes were detected. The first regime inherits frequency of individual oscillator, with a relatively small phase shift between oscillators in the ring. The frequency of the second regime depends on the number of neurons in the ring, with the phase shift between neighbor oscillators being equal to ratio of oscillation period and number of neurons. This second regime can occur both for the parameters corresponding to bistabler regime in the individual neuron and for the parameters at which the only existing attractor is stable focus. The second synchronous regime is preserved for parameter detuning of 2% from their absolute values. Conclusion. It was shown that in the mathematical model of the ring of hippocampal neurons, where all the main significant currents are taken into account for individual neurons, and their parameters can vary, there is an oscillatory mode, the frequency of which is determined by the length of the ring and synaptic conductivity, rather than by the parameters individual neuron. In this case, a small change in synaptic conductivity can lead to a sharp (2–7 times) change in the generation frequency.

In the paper we continue the research on the modified Hodgkin–Huxley model in the presence of random noise. It is shown that, adding a certain short-time stimulation current, a random noise of low amplitude does not prevent the system transition from the motion within a neighborhood of the small stable limit cycle (the mode "None" in accordance with the principal neurophysiological "All or none"-law) to the motion along the big stable limit cycle called "duck nose" (the mode "All" respectively). At the same time, the high amplitude random noise can yield a series of transitions from one regime to the other and vice versa, even for a short-time stimulation. In the absence of stimulation and in the presence of high amplitude noise, such series of transitions were observed earlier during computer simulation. [ABSTRACT FROM AUTHOR]

Long-term memory (LTM ) and short-term memory (STM ) and their evolution from one to the other are important mechanisms to understand brain memory. We use the Hodgkin–Huxley (HH ) model, a well-tested and closest model to biological neurons and synapses, to shine some light on LTM and STM memorization mechanisms. The role of Na + and K + ion channels playing in LTM and STM process is carefully examined by using three different types of input signals, namely, a step DC voltage, a positive part of sinusoidal wave and periodic square signal with read voltage. Results are analyzed based on first-order K + memristor and second-order Na + memristor. [ABSTRACT FROM AUTHOR]

Background: Deep brain stimulation (DBS) is a method for stimulating deep regions of the brain for the treatment of various neurological and psychiatric disorders such as depression, obsessive-compulsive disorder, addiction, and Parkinson's disease. Generally, DBS can be performed using both invasive and non-invasive approaches. Invasive DBS is associated with several problems, including intracranial bleeding, infection, and changes in the position of the electrode tip. Temporal interference (TI) stimulation is a non-invasive technique used to stimulate deep regions of the brain by applying two high-frequency sinusoidal currents with slightly different frequencies. New method: This paper presents insights into the response of the spiking in the Hodgkin-Huxley (HH) neuron model of the rat somatosensory cortex by changing the parameters carrier frequency, current ratio, and difference frequency of TI stimulation. Furthermore, in order to experimentally evaluate the effect of TI stimulation on the activation of the left motor cortex, an experiment was conducted to measure the motion induced by the balanced and unbalanced TI stimulation. In the experiment, a three-axis accelerometer was attached to the right hand of the animal to determine the position of the hand. Results: Simulation results of the HH model showed that the frequency of the envelope of the TI stimulation is identical to the fundamental frequency of the neuron spikes. This result was obtained for difference frequencies of 6 Hz and 9 Hz in balanced and unbalanced TI stimulations. Moreover specifically, when the difference frequency is set to zero, the carrier frequency is within the range of 1300–1400 Hz, and the current range is between 140 and 250 μA/cm2, the firing rate reached to its highest value. In the experimental result, the maximum range of movement at a difference frequency of Δf = 6 Hz was approximately 1.6 mm and 5.3 mm in the z and y directions respectively. Comparison with existing method: The results of the spatial spectrum of the rat hand movement were consistent with the spectrum information of the simulation results. Additionally, steering the interfering region to the left motor cortex leads to noticeable contralateral movement of the right hand while no movement was observed in the right hand during the stimulation of the right motor cortex. Conclusion: This technique of stimulation for the deep regions of the brain is a promising tool to noninvasively treat various neurological and psychiatric disorders such as morphine dependence in addicted rats.

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.

The study of neural cell behavior is one of the exciting fields that has attracted scientists' attention since 1890. Accordingly, many efforts have been made to model the neural cell specifying different neuron behaviors and characteristics. These studies mainly focus on two primary targets: finding new computing paradigms and providing better models and tools for medical applications. A critical model for studying biological neurons' behavior is the Hodgkin–Huxley neuron model (HH for short), presented in 1952. In this paper, the model is realized and verified using only one multiplier with some simplification. By comparing the original and simplified HH models, the RMSE and NRMSE are 1.83 and 0.016 for neuron membrane output potential, respectively, at the injected current of 20 uA. The dynamical behaviors of the model are also studied to ensure stability. The proposed model is synthesized and implemented on an FPGA platform, where the outputs are examined, and the results are presented as proof of concept. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Ion currents associated with channel proteins in the presence of membrane potential are ubiquitous in cellular and organelle membranes. When an ion current occurs through a channel protein, Joule heating should occur. However, this Joule heating seems to have been largely overlooked in biology. Here we show theoretical investigation of Joule heating involving channel proteins in biological processes. We used electrochemical potential to derive the Joule's law for an ion current through an ion transport protein in the presence of membrane potential, and we suggest that heat production and absorption can occur. Simulation of temperature distribution around a single channel protein with the Joule heating revealed that the temperature increase was as small as <10-3 K, although an ensemble of channel proteins was suggested to exhibit a noticeable temperature increase. Thereby, we theoretically investigated the Joule heating of systems containing ensembles of channel proteins. Nerve is known to undergo rapid heat production followed by heat absorption during the action potential, and our simulation of Joule heating for a squid giant axon combined with the Hodgkin-Huxley model successfully reproduced the feature of the heat. Furthermore, we extended the theory of Joule heating to uncoupling protein 1 (UCP1), a solute carrier family transporter, which is important to the non-shivering thermogenesis in brown adipose tissue mitochondria (BATM). Our calculations showed that the Joule heat involving UCP1 was comparable to the literature calorimetry data of BATM. Joule heating of ion transport proteins is likely to be one of important mechanisms of cellular thermogenesis. [ABSTRACT FROM AUTHOR]

Alan Hodgkin's and Andrew Huxley's mid-20th century work on the ionic currents generating neuron action potentials stands among that century's great scientific achievements. Unsurprisingly, that case has attracted widespread attention from neuroscientists, historians and philosophers of science. In this paper, I do not propose to add any new insights into the vast historical treatment of Hodgkin's and Huxley's scientific discoveries in that much- discussed episode. Instead, I focus on an aspect of it that hasn't received much attention: Hodgkin's and Huxley's own assessments about what their famous "quantitative description" accomplished. The "Hodgkin-Huxley model" is now widely recognized as a foundation of contemporary computational neuroscience. Yet Hodgkin and Huxley expressed serious caveats about their model and what it added to their scientific discoveries, as far back as their (1952d), in which they first presented their model. They were even more critical of its accomplishments in their Nobel Prize addresses a decade later. Most notably, as I argue here, some worries they raised about their quantitative description seem still to be relevant to current work in ongoing computational neuroscience. [ABSTRACT FROM AUTHOR]

Image transmission is happening more frequently in this era of technologically sophisticated digital information. Additionally, more individuals are becoming aware of its importance. In order to secure images, many academics are participating in research, which is advantageous for guaranteeing data security. In order to strengthen the security of images during transmission, we have investigated new encryption algorithms to guarantee this. First, a current representing the Lorenz chaotic system is introduced into the neuron model. The neuron model generates sequences after receiving the current signal. The next move is made as the current shifts depending on whether the resulting sequences are chaotic or not. If so, the subsequent operation is carried out; otherwise, the current is altered until chaotic sequences are produced. Second, a global scrambling with de-duplication technique is used to scramble the image using the resulting chaotic sequences. To complete the dislocation effect, the Latin square is used to dislocate the image after the initial dislocation. Fourth, the image that has been scrambled is subjected to two rounds of additive mode diffusion. They are diffusion in the forward additive mode and diffusion in the inverse additive mode. Lastly, to improve the diffusion effect, the image is diffused in the finite domain. Eventually, the encrypted image is obtained. After evaluation tests and comparison with related literature, it can be found that the algorithm of this study has certain advantages. Also, the resistance to attack is good. It can protect the security of the image.

The membrane current I m of the Hodgkin-Huxley (HH) cardiac Purkinje fiber (CPF) model is usually calculated as direct current (DC). In this paper, a conventional alternating current (AC) is used, namely I AC = Asin (2 π f t) , as the impressed injection. Dynamic characters of the model with different AC parameters and various initial conditions are observed through phase plane orbits, waveforms, bifurcation diagrams, and Lyapunov exponent spectra, which reveal multiple coexisting membrane action potential patterns, corresponding to the coexisting attractors of the same or different periods in the phase diagram. Meanwhile, the model undergoes period, quasi-period and local forward or inverse period-doubling bifurcations with changes in amplitude A or frequency f, which further proves the complex nonlinear property of the AC-injected model. In addition, by changing the external current I m , the sodium-ion and potassium-ion equilibrium potentials, i.e. E Na and E K , respectively, the regularity of the CPF heartbeat frequency is observed. The state transformations of CPF are found between normal, abnormal and sudden cardiac arrest, and the method adjusting from the dangerous state to the normal heartbeat frequency range is investigated. This study may provide a reference for exploring the evolution of nonlinear dynamics in HH CPF model and protecting the health of life and heart. [ABSTRACT FROM AUTHOR]

Acupuncture is an important part of traditional Chinese medicine (TCM). Although the efficacy of acupuncture has been widely accepted, the scientific basis behind it is still lack of exploration. Some experimental studies have shown that acupuncture can lead to changes in cell membrane potential, thus affecting the transmission of nerve signals to a certain extent. The change of cell membrane potential mainly depends on a large number of voltage dependent ion channels on the cell membrane. Relevant studies have proved that such voltage dependent ion channels have certain mechanical sensitivity, but the principle is not clear. Based on the theoretical model of cell action potential excitability proposed by Hodgkin–Huxley, taking rat skeletal muscle cell fibers as an example, this paper makes a rough stability analysis of the nonlinear system, explores the conditions of repeated discharge of cell membrane, and simulates the law of cell membrane potential frequency variation under different stimulating currents by numerical calculation. Based on the results of numerical simulation, we propose a hypothesis: Mechanical stimulation will produce a certain amount of stimulating current on the cell membrane, making the cell membrane potential in the state of repeated discharge, so as to block or interfere with the transmission of nerve signals and achieve the analgesic effect and so forth. [ABSTRACT FROM AUTHOR]

• A new neuron model with passive dendrite is reconstructed in this article from two levels and in three forms. • Two types of stimulation are performed on this model, which contain the constant electrode current and the synaptic one. • Four coding ways are proposed to encode the spike train where the first-to-spike-time coding one is shown to be the best. Spike is the basic unit in the neuron communication, and different selections of the stimulation locations on the neuron might cause different spike trains, which infers that the spike trains may determine the information of the stimulation locations. The research on this subject deserves intensive attention, whether by numerical experiments or by electrophysiological ones. In this article, to answer the question of how does the spike train encode the stimulus location, by combining the cable model with the leaky integral firing model, a new neuron model called leaky integral firing model with passive dendrite is reconstructed from two levels (i.e., space and time) and in three forms (i.e., the conceptual model, the circuit model, and the mathematical model). Two types of stimulation are performed on this new model, which contain the constant electrode current and the synaptic one, where the latter is also divided into the excitatory current and the inhibitory one. Four coding ways are employed to encode the spike train, among them, by numerical experiments and some theoretical verification, it is shown that the first-to-spike-time coding method is the best one, which could clearly reflect the information of the stimulus position. To be more specific, the closer the stimulation location is to the axon hillock, the shorter the first-to-spike-time is. The neuron model proposed in this paper and the relating encoding methods for the stimulus location could also be applied to the brain-computer interface or constructing new types of neural networks. [ABSTRACT FROM AUTHOR]

This paper presents a two-dimensional simplified Hodgkin–Huxley model under exposure to electric fields. The Hopf bifurcations of the simplified Hodgkin–Huxley model are investigated through qualitative analysis and numerical simulations. A necessary and sufficient condition for the existence of Hopf bifurcations is derived, and the conditions for supercritical and subcritical Hopf bifurcations are obtained. Finally, bifurcation diagrams are given for two parameters, and numerical examples are presented to illustrate the effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]

A neural network is composed of a group of neurons that make a connectome, which is a map of neural connections that allows establishing the paths between the neurons. The neural network can generate the actions of living beings with the neuronal interaction through chemical-electrical signals. The behavior of machines is not dynamic compared to the behavior of animals; then, the machine's behavior must be modeled and made by an exterior design, while in living beings, the behavior is caused by experience. Caenorhabditis Elegans is a worm model to study the connections of its neurons. In order to study the dynamic behavior in software systems based on biologic models, we created an approach to train and classify binary patterns using the structure of the Caenorhabditis Elegans' connectome. We used the connectivity of neurons of Caenorhabditis Elegans to make a custom approach to train a Spiking Neural Network using a branching factor to classify patterns instead of layers of neurons. We made a software system to show the graph of neuronal connections of the Caenorhabditis Elegans. We also used Spike-Timing-Dependent Plasticity in order to establish the strength of the weights between the connections. In addition, we used a Hodgkin-Huxley model to calculate the neuron's potential membrane and handle the spikes of the network. [ABSTRACT FROM AUTHOR]

The textbook picture of nerve activity is that of a propagating voltage pulse driven by electrical currents through ion channel proteins, which are gated by changes in voltage, temperature, pressure or by drugs. All function is directly attributed to single molecules. We show that this leaves out many important thermodynamic couplings between different variables. A more recent alternative picture for the nerve pulse is of thermodynamic nature. It considers the nerve pulse as a soliton, i.e., a macroscopic excited region with properties that are influenced by thermodynamic variables including voltage, temperature, pressure and chemical potentials of membrane components. All thermodynamic variables are strictly coupled. We discuss the consequences for medical treatment in a view where one can compensate a maladjustment of one variable by adjusting another variable. For instance, one can explain why anesthesia can be counteracted by hydrostatic pressure and decrease in pH, suggest reasons why lithium over-dose may lead to tremor, and how tremor is related to alcohol intoxication. Lithium action as well as the effect of ethanol and the anesthetic ketamine in bipolar patients may fall in similar thermodynamic patterns. Such couplings remain obscure in a purely molecular picture. Other fields of application are the response of nerve activity to muscle stretching and the possibility of neural stimulation by ultrasound. [ABSTRACT FROM AUTHOR]

Astrocytes have a regulatory function on the central nervous system (CNS), especially in the temperature-sensitive hippocampal region. In order to explore the thermosensitive dynamic mechanism of astrocytes in the CNS, we establish a neuronâ€"astrocyte minimum system to analyze the synchronization change characteristics based on the Hodgkinâ€"Huxley model, in which a pyramidal cell and an interneuron are connected by an astrocyte. The temperature range is set as 0 °Câ€"40 °C to juggle between theoretical calculation and the reality of a brain environment. It is shown that the synchronization of thermosensitive neurons exhibits nonlinear behavior with changes in astrocyte parameters. At a temperature range of 0 °Câ€"18 °C, the effects of the astrocyte can provide a tremendous influence on neurons in synchronization. We find the existence of a value for inositol triphosphate (IP3) production rate and feedback intensities of astrocytes to neurons, which can ensure the weak synchronization of two neurons. In addition, it is revealed that the regulation of astrocytes to pyramidal cells is more sensitive than that to interneurons. Finally, it is shown that the synchronization and phase transition of neurons depend on the change in Ca2+ concentration at the temperature of weak synchronization. The results in this paper provide some enlightenment on the mechanism of cognitive dysfunction and neurological disorders with astrocytes. [ABSTRACT FROM AUTHOR]

Different models and approaches to studying nerve impulse generation and conduction are discussed. Mechanical, thermodynamic and electrical properties of nerve cells have been addressed in many studies. Although developed 70 years ago, the Hodgkin–Huxley model is still the gold standard in neuroscience. The model theoretically described the electric phenomena known for the action potential at that time and led to the development of novel experimental and theoretical approaches to membrane research in biophysics. A mechanical soliton model was proposed as an alternative explanation of the nerve impulse. According to the mechanical soliton model, the nerve impulse is an undamped mechanical wave associated with a phase transition in the lipid bilayer. Proponents of the mechanical soliton model gave their arguments against some points of the Hodgkin–Huxley model. Most of their statements may find explanation within the Hodgkin–Huxley model, given that changes in membrane potential may lead not only to changes in ion channel permeability, but also to changes in membrane thickness, modifications of protein–lipid interactions, and modulation of cooperativity between ion channels. The appearance of a mechanical soliton might be possible in some cases, but is not the main mechanism of nerve excitability. A universal mathematical model is thus necessary in order to interpret all biophysical changes observed during the nerve impulse. The key to achieving this task is to adapt the Hodgkin–Huxley model. This approach to nerve impulse modelling could lead to new experimental designs and new findings. [ABSTRACT FROM AUTHOR]

• Scalable network design of Hodgkin–Huxley neurons with heterogeneous dynamics. • Feedback structure for separation of neuron-network dynamics. • Model validation on common neuronal motifs and large-scale networks. We present a novel modular, scalable and adaptable modelling framework to accurately model neuronal networks composed of neurons with different dynamic properties and distinct firing patterns based on a control-inspired feedback structure. We consider three important classes of neurons: inhibitory Fast spiking neurons, excitatory regular spiking with adaptations neurons, and excitatory intrinsic bursting neurons. We also take into consideration two basic means of neuronal interconnection: electrical and chemical synapses. By separating the neuronal dynamics from the network dynamics, we have developed a fully flexible feedback structure that can be further augmented to incorporate additional types of neurons and/or synapses. We use an augmented version of the Hodgkin–Huxley model to describe the individual neuron dynamics and graph theory to define the network structure. We provide simulation results for small fundamental neuron motifs as well as bigger neuronal networks and we verify the accuracy, flexibility and scalability of the proposed method. Therefore, we provide the basis for a comprehensive modelling framework that is able to imitate the dynamics of individual neurons and neuronal networks and is able to replicate basic normal brain function. The structure of the proposed framework is ideal for applications of control and optimization methods both for modelling the effect of pharmacological substances as well as for modelling diseased neuron and network conditions. [ABSTRACT FROM AUTHOR]

Neuron and neural network studies are remarkably fostered by novel stimulation and recording systems, which often make use of biochips fabricated with advanced electronic technologies and, notably, micro- and nanoscale complementary metal-oxide semiconductor (CMOS). Models of the transduction mechanisms involved in the sensor and recording of the neuron activity are useful to optimize the sensing device architecture and its coupling to the readout circuits, as well as to interpret the measured data. Starting with an overview of recently published integrated active and passive micro/nanoelectrode sensing devices for in vitro studies fabricated with modern (CMOS-based) micro-nano technology, this paper presents a mixed-mode device-circuit numerical-analytical multiscale and multiphysics simulation methodology to describe the neuron-sensor coupling, suitable to derive useful design guidelines. A few representative structures and coupling conditions are analysed in more detail in terms of the most relevant electrical figures of merit including signal-to-noise ratio. This article is part of the theme issue 'Advanced neurotechnologies: translating innovation for health and well-being'. [ABSTRACT FROM AUTHOR]

Based on the Hodgkin–Huxley (HH) model, a model of calculation is constructed to simulate the neuron. In this paper, how the factors affect the function of HH model is carefully analyzed. It is indicated that temperature is a key role for performance of HH model, under different parameters including temperature, external stimulation, etc. [ABSTRACT FROM AUTHOR]

Biophysics is a way of approaching biological problems through numbers, physical laws, models, and quantitative logic. In a long scientific career, I have seen the formation and fruition of the ion channel concept through biophysical study. Marvelous discoveries were made as our instruments evolved from vacuum tubes to transistors; computers evolved from the size of an entire building to a few chips inside our instruments; and genome sequencing, gene expression, and atom-level structural biology became accessible to all laboratories. Science is rewarding and exhilarating. [ABSTRACT FROM AUTHOR]

This paper presents a two-dimensional simplified Hodgkin–Huxley model under exposure to electric fields. The Hopf bifurcations of the simplified Hodgkin–Huxley model are investigated through qualitative analysis and numerical simulations. A necessary and sufficient condition for the existence of Hopf bifurcations is derived, and the conditions for supercritical and subcritical Hopf bifurcations are obtained. Finally, bifurcation diagrams are given for two parameters, and numerical examples are presented to illustrate the effectiveness of the theoretical results.

Major depressive disorder (MDD) is one of the most serious neuropsychiatric disorders. Exploring the pathogenesis and dynamical coding patterns of MDD can provide new targets for clinical drug treatment and new ideas for the research of other neuropsychiatric and neurodegenerative diseases. We selected the medium spiny neuron (MSN) of nucleus accumbens (NAc) as the research objective. NAc is located in the dopaminergic pathway, regulating rewards, emotions and other behaviors. Abnormalities in these behaviors are considered as the main clinical symptoms of MDD. We simulated the different spike patterns of MSNs in MDD group and control group by dynamical Hodgkin–Huxley model. The simulated results can match the electrophysiological experiments, which occurred due to following reasons: (1) The external stimulus current of MDD group was amplified by the local neural microcircuit; (2) the selective permeability to sodium was abnormally decreased; and (3) the dopamine D2 receptor signaling pathway was abnormal in the MDD group. Furthermore, we proposed a dynamical energy model, and the energy results demonstrated that the energy cost in MDD group was lower, which led to persistent depression in patients with MDD. Simultaneously, the negative-to-total energy ratio of MSN in MDD group was higher than that in control group, and the delay time of the power peak and the potential peak in MDD group was shorter than that in the control group. The results showed that the abnormal firing patterns were the direct cause of abnormal behaviors of MDD and indicated that subthreshold activities of MDD group were more intense. [ABSTRACT FROM AUTHOR]

Hodgkin-Huxley (HH)-type model is the most famous computational model for simulating neural activity. It shows the highest accuracy in capturing neuronal spikes, and its model parameters have definite physiological meanings. However, HH-type models are computationally expensive. To address this problem, a previous study proposed a spike prediction module (SPM) to predict whether a spike will take place 1 ms later based on three voltage values with intervals of 1 ms. Although SPM does well, it fails to evaluate the informative features of the spike. In this study, the feature prediction module (FPM) based on simple artificial neural network (ANN) was proposed to predict spike features including maximum voltage, minimum voltage, and dropping interval. Nine different HH-type models were adopted whose firing patterns cover most of the firing behaviors observed in the brain. Voltage and spike feature samples under constant external input current were collected for training and testing. Experiment results illustrated that the combination of SPM and FPM can accurately predict the spiking part of different HH-type models and can generalize to unseen types of input current. The combination of SPM and FPM may offer a possible way to simulate the action potentials of biological neurons with high accuracy and efficiency. [ABSTRACT FROM AUTHOR]

In this paper, we consider a biologically inspired spiking neural network model for motion detection. The proposed model simulates the neurons' behavior in the cortical area MT to detect different kinds of motion in image sequences. We choose the conductance-based neuron model of the Hodgkin–Huxley to define MT cell responses. Based on the center-surround antagonism of MT receptive fields, we model the area MT by its great proportion of cells with directional selective responses. The network's spiking output corresponds to an MT neuron population's firing rates and enables to extract motion boundaries. We conduct several experiments on real image sequences. The experimental results show the proposed network's ability to segregate multiple moving objects from an image sequence and reproduce the MT cells' responses. We perform a quantitative evaluation on the YouTube Motion Boundaries (YMB) dataset, and we compare the result to state-of-the-art methods for boundary detection in videos: boundary flow estimation (BF) and temporal boundary difference (BD). The proposed network model provides the best results on YMB compared to BF and BD methods. [ABSTRACT FROM AUTHOR]

θ-γ phase-amplitude coupling (PAC) is observed in neural networks of the hippocampus, in which the amplitude of high-frequency oscillations, γ wave, is modulated by the phase of low-frequency oscillations--θ wave. Several studies have shown that the degree of PAC is closely related to memory formation. In contrast, the stochastic resonance (SR) phenomenon, in which the addition of random synaptic noise can enhance the transmission of sub-threshold synaptic stimuli, is also observed in the hippocampus. Although PAC reportedly appears in a specific status of brain activity, it is still unclear if and how random synaptic noise could contribute to generating θ-γ PAC. In this study, we investigated whether or not θ-γ PAC could be generated by an addition of random synaptic noise in a hippocampal neural network model with sub-threshold excitable synaptic stimuli using the modulation index (MI) through computer simulations. The results of computer simulations showed that the typical resonance curve of resonance phenomena, i.e., MI was maximized at a specific amplitude of synaptic noise. Therefore, it is concluded that θ-γ PAC can be generated by the vibrational mode transition, induced by the random synaptic noise, in the hippocampal neural network model with mixed sub-and supra-threshold stimuli. [ABSTRACT FROM AUTHOR]

Hodgkin-Huxley (HH)-type model is the most famous computational model for simulating neural activity. It shows the highest accuracy in capturing neuronal spikes, and its model parameters have definite physiological meanings. However, HH-type models are computationally expensive. To address this problem, a previous study proposed a spike prediction module (SPM) to predict whether a spike will take place 1 ms later based on three voltage values with intervals of 1 ms. Although SPM does well, it fails to evaluate the informative features of the spike. In this study, the feature prediction module (FPM) based on simple artificial neural network (ANN) was proposed to predict spike features including maximum voltage, minimum voltage, and dropping interval. Nine different HH-type models were adopted whose firing patterns cover most of the firing behaviors observed in the brain. Voltage and spike feature samples under constant external input current were collected for training and testing. Experiment results illustrated that the combination of SPM and FPM can accurately predict the spiking part of different HH-type models and can generalize to unseen types of input current. The combination of SPM and FPM may offer a possible way to simulate the action potentials of biological neurons with high accuracy and efficiency.

Objective: Parkinson's disease (PD) is a degenerative disease of the nervous system that frequently occurs in the aged. Transcranial magnetoacoustic stimulation (TMAS) is a neuronal adjustment method that combines sound fields and magnetic fields. It has the characteristics of high spatial resolution and noninvasive deep brain focusing. Methods: This paper constructed a simulation model of TMAS based on volunteer's skull computer tomography, phased controlled transducer and permanent magnet. It simulates a transcranial focused sound pressure field with the Westervelt equation and builds a basal ganglia and thalamus neural network model in the PD state based on the Hodgkin-Huxley model. Results: A biased sinusoidal pulsed ultrasonic TMAS induced current with 0.3 T static magnetic field induction and 0.2 W⋅cm–2 sound intensity can effectively modulate PD states with RI ≥ 0.633. The magnitude of magnetic induction strength was changed to 0.2 and 0.4 T. The induced current was the same when the sound intensity was 0.4 and 0.1 W⋅cm–2. And the sound pressure level is in the range of −1 dB (the induced current difference is less than or equal to 0.019 μA⋅cm–2). TMAS with a duty cycle of approximately 50% can effectively modulates the error firings in the PD neural network with a relay reliability not less than 0.633. Conclusion: TMAS can modulates the state of PD. [ABSTRACT FROM AUTHOR]

Objective: Parkinson’s disease (PD) is a degenerative disease of the nervous system that frequently occurs in the aged. Transcranial magnetoacoustic stimulation (TMAS) is a neuronal adjustment method that combines sound fields and magnetic fields. It has the characteristics of high spatial resolution and noninvasive deep brain focusing.Methods: This paper constructed a simulation model of TMAS based on volunteer’s skull computer tomography, phased controlled transducer and permanent magnet. It simulates a transcranial focused sound pressure field with the Westervelt equation and builds a basal ganglia and thalamus neural network model in the PD state based on the Hodgkin-Huxley model.Results: A biased sinusoidal pulsed ultrasonic TMAS induced current with 0.3 T static magnetic field induction and 0.2 W⋅cm–2 sound intensity can effectively modulate PD states with RI ≥ 0.633. The magnitude of magnetic induction strength was changed to 0.2 and 0.4 T. The induced current was the same when the sound intensity was 0.4 and 0.1 W⋅cm–2. And the sound pressure level is in the range of −1 dB (the induced current difference is less than or equal to 0.019 μA⋅cm–2). TMAS with a duty cycle of approximately 50% can effectively modulates the error firings in the PD neural network with a relay reliability not less than 0.633.Conclusion: TMAS can modulates the state of PD.

Background: Deep brain stimulation (DBS) is a method for stimulating deep regions of the brain for the treatment of various neurological and psychiatric disorders such as depression, obsessive-compulsive disorder, addiction, and Parkinson's disease. Generally, DBS can be performed using both invasive and non-invasive approaches. Invasive DBS is associated with several problems, including intracranial bleeding, infection, and changes in the position of the electrode tip. Temporal interference (TI) stimulation is a non-invasive technique used to stimulate deep regions of the brain by applying two high-frequency sinusoidal currents with slightly different frequencies., New Method: This paper presents insights into the response of the spiking in the Hodgkin-Huxley (HH) neuron model of the rat somatosensory cortex by changing the parameters carrier frequency, current ratio, and difference frequency of TI stimulation. Furthermore, in order to experimentally evaluate the effect of TI stimulation on the activation of the left motor cortex, an experiment was conducted to measure the motion induced by the balanced and unbalanced TI stimulation. In the experiment, a three-axis accelerometer was attached to the right hand of the animal to determine the position of the hand., Results: Simulation results of the HH model showed that the frequency of the envelope of the TI stimulation is identical to the fundamental frequency of the neuron spikes. This result was obtained for difference frequencies of 6 Hz and 9 Hz in balanced and unbalanced TI stimulations. Moreover specifically, when the difference frequency is set to zero, the carrier frequency is within the range of 1300-1400 Hz, and the current range is between 140 and 250 μA/cm 2 , the firing rate reached to its highest value. In the experimental result, the maximum range of movement at a difference frequency of Δ f  = 6 Hz was approximately 1.6 mm and 5.3 mm in the z and y directions respectively., Comparison With Existing Method: The results of the spatial spectrum of the rat hand movement were consistent with the spectrum information of the simulation results. Additionally, steering the interfering region to the left motor cortex leads to noticeable contralateral movement of the right hand while no movement was observed in the right hand during the stimulation of the right motor cortex., Conclusion: This technique of stimulation for the deep regions of the brain is a promising tool to noninvasively treat various neurological and psychiatric disorders such as morphine dependence in addicted rats., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Author(s).)

By analyzing the Hodgkin-Huxley model, it can be estimated how neurons communicate with each other. It is the most widely used model that describes why the action potentials happen and how action potentials are generated and propagated in neurons. In a nerve cell, an action potential is evoked when the membrane potential attains its threshold (bifurcation point of neuron activity) membrane potential. The substantive changes associated with membrane voltage and conductance driving the action potential. Sometimes, nerve cells show abnormal activity for different causes. In this paper, we analyze the bifurcation of the Hodgkin-Huxley model to show the relationship between the abnormal activity of the nerve cells and the ionic conductance and external stimulation into the nerve cells. We also illustrated the possible region where the nerve cells show undesirable hyperactivity by calculating the Hopf bifurcation curve. [ABSTRACT FROM AUTHOR]

Demyelination of neurons can compromise the communication performance between the cells as the absence of myelin attenuates the action potential propagated through the axonal pathway. In this work, we propose a hybrid experimental and simulation model for analyzing the demyelination effects on neuron communication. The experiment involves locally induced demyelination using Lysolecithin and from this, a myelination index is empirically estimated from analysis of cell images. This index is then coupled with a modified Hodgkin-Huxley computational model to simulate the resulting impact that the de/myelination processes has on the signal propagation along the axon. The effects of signal degradation and transfer of neuronal information are simulated and quantified at multiple levels, and this includes (1) compartment per compartment of a single neuron, (2) bipartite synapse and the effects on the excitatory post-synaptic potential, and (3) a small network of neurons to understand how the impact of de/myelination has on the whole network. By using the myelination index in the simulation model, we can determine the level of attenuation of the action potential concerning the myelin quantity, as well as the analysis of internal signalling functions of the neurons and their impact on the overall spike firing rate. We believe that this hybrid experimental and in silico simulation model can result in a new analysis tool that can predict the gravity of the degeneration through the estimation of the spiking activity and vice-versa, which can minimize the need for specialised laboratory equipment needed for single-cell communication analysis. [ABSTRACT FROM AUTHOR]