Effect Of Electromagnetic Induction Research Articles

Influence of memristor and noise on H–R neurons

We study the effect of electromagnetic induction on improved Hindmarsh–Rose neuron model with flux-based memristor terms. The electrical activity of single neuron and of coupled neurons under the influence of quadratic memristor term and the influence of noise on isolated and coupled neurons are analyzed. Our results confirm that, when noise is added, the oscillation death is achieved for relatively smaller magnitudes of external steady current and it also leads to the inhibition of bursting under periodic current. The functional responses of membrane potential of single as well as of coupled neuron exhibit bursting, tonic, quiescent and even suppression of oscillations under quadratic flux induction, as external current is varied. The suppression of oscillation for higher current in the presence of quadratic flux is quite distinct from the cubic flux case where increase in current exhibits continuous spiking. The variation of Hamilton energy against external current confirms the existence of quiescent state. The bifurcation plot of interspike interval versus current in the presence of external quadratic flux is denser than that of cubic flux-based electromagnetic induction which indicates a higher degree of aperiodicity. The plot of Lyapunov exponent versus characteristic parameter exhibited anisotropy and chaotic nature for the dynamics of the neuron. For coupled neurons, the synchronization patterns shows periodic-, chaotic-, and tonic-type transitions. For certain noise intensity and coupling strength, the oscillation death is also exhibited by the coupled neurons. For the exponential flux-controlled memristor, the coupled neurons exhibit the synchronizations with dynamics of antiphase, periodic, chaotic, and of oscillation death. The plot of transverse Lyapunov exponent of coupled neurons establish that the system is chaotic for certain values of coupling constant.

3D sequential inversion of frequency-domain airborne electromagnetic data to determine conductive and magnetic heterogeneities

We have developed two inversion workflows that sequentially invert conductivity and susceptibility models from a frequency-domain controlled-source electromagnetic data set. Both workflows start with conductivity inversion using electromagnetic (EM) kernel and out-of-phase component data, which is mainly sensitive to conductivity, and then we adopt the susceptibility inversion using in-phase component data. The difference between these two workflows is in the susceptibility inversion algorithm: One uses an EM kernel and a conductivity model as the input model; the other uses a magnetostatic kernel and a conductivity model to generate the appropriate input data. Because the appropriate input data for magnetostatic inversion should not contain the EM induction effect, the in-phase induction effect is simulated through the conductivity model obtained by inverting out-of-phase data and subtracting them from observed in-phase data to generate an “induction-subtracted” in-phase data set that becomes input data for magnetostatic inversion. For magnetostatic inversion, we used a linear magnetostatic kernel to enable rapid computation. Then, we applied the two inversion workflows to a field data set of a DIGHEM survey, and we successfully reconstructed the conductivity and susceptibility models from each workflow using two zones within the data sets, in which conductive and susceptible anomalies were present. One important finding is that the susceptibility inversion results obtained from two different workflows are very similar to each other. However, computational time can be significantly saved with linear magnetostatic inversion. We found out how the results of the conductivity and susceptibility models could be well-imaged using a sequential inversion workflow and also how magnetostatic inversion could be used efficiently for airborne EM data inversion.

Dynamic Behaviors in Coupled Neuron System with the Excitatory and Inhibitory Autapse under Electromagnetic Induction

The induced current produced by electromagnetic induction can adjust the membrane potential of neuron through the feedback of a magnetic flux-controlled memristor. We adopt the numerical simulation method with the aim of investigating the synchronous behavior in the neuronal system that is coupled by chemical and electrical synapses under electromagnetic induction. Within the improved model, the effects of electromagnetic induction on neurons are described with additive memristive current on the membrane variable, and the memristive current is dependent on the variation of magnetic flow. The simulation results show that the two coupling modes play an important role in the synchronization of the system. By increasing the chemical synaptic feedback gain, we observe a transition from mixed oscillatory to periodic state at a critical value. In addition, two Hopf bifurcation points are found with the change of the external stimuli, and the state of neuron discharge is influenced by initial values. Furthermore, there is a domain of coupling strength and feedback gain values, in which the two coupled neuron system is synchronized and longer time lag is not conducive to the system synchronization.

Dynamics of neurons in the pre-Bötzinger complex under magnetic flow effect

Breathing is a complex rhythmic movement. The dynamics of neuron firing activity has important implications in understanding the causes of pathological respiratory rhythm. Studies on electrophysiology have shown that the internal and external bioelectricity of nervous system may play important roles on firing activities of neuron. In this paper, the magnetic flow is added as a new variable to the Butera model to investigate the effect of electromagnetic induction on neuronal activities. The effect of magnetic flow on membrane potential is described by imposing additive memristive current on the membrane variable. The memristive current depends on the variation of magnetic flow. Direct and alternating currents of external stimulus are also added to the membrane potential. Dynamics of this modified model is discussed to consider the influence of magnetic flux on the membrane potential under different conditions of direct and alternating currents. The results show that the magnetic flux makes the pre-BotC neuron oscillate under a lower value of the direct current. Regular bursting and the mixed modes bursting types can be observed by changing the external condition and stimulus. Further studies on the combination effects of the parameter $${k_1}$$ and the direct and alternating currents are performed by two-parameter bifurcation analysis.

Synchronization and wave propagation in neuronal network under field coupling

Electric and chemical synapse play important role in connecting neurons and thus signal propagation can be realized between neurons. External electric stimulus can change the excitability of neuron and then the electrical activities can be modulated completely. Continuous fluctuation of ion concentration in cell can induce complex time-varying electromagnetic field during the exchange of charged ions across the membrane of neuron. Polarization and magnetization in the media (and neuron), which exposed to electromagnetic radiation, can modulate the dynamical response and mode transition in electrical activities of neurons. In this paper, magnetic flux is used to describe the effect of electromagnetic field, and the three-variable Hindmarsh-Rose neuron model is updated to propose a four-variable neuron model that the effect of electromagnetic induction and radiation can be explained. Based on the physical law of electromagnetic induction, exchange of charged ions and flow of ion currents will change the distribution of electromagnetic filed in cell, and each neuron will be exposed to the superimposed field triggered by other neurons. Therefore, signal exchange could occur even synapse coupling between neurons is removed in the case of field coupling. A chain network is proposed to investigate the signal exchange between neurons under field coupling when synapse coupling is not available. It is found that field coupling between neurons can change the collective behaviors in electrical activities. A statistical factor of synchronization and spatial patterns are calculated, these results confirmed that field coupling is effective for signal communication between neurons. In the end, open problems are suggested for readers’ extensive guidance in this field.

Chimera states and synchronization behavior in multilayer memristive neural networks

To study the effect of electromagnetic induction on the spatiotemporal dynamic behavior of neural networks, in this paper, we have mainly studied both the synchronization behavior and the evolution of chimera states (CS) in coupled neural networks. To do this, a multilayer memristive neural network was constructed by selecting the Hindmarsh–Rose neurons as the network nodes, and the effect of electromagnetic induction is introduced by using the cubic flux-controlled memristive model as synapse. For simplicity, the following coupling scheme is adopted: only the coupling connections for the neurons between different layers are considered with memristive synapses, while those neurons in each layer are still bidirectional coupled with the classical electrical synapses. It is found that, by adjusting the coupled strength of electrical synapses and the parameters of memristive synapses, the coexistence behavior of coherent and incoherent states, i.e., the CS, could appear in each layer. It is interesting that the CS are also found in inter-layer memristive synapse network. Furthermore, we have discussed the synchronization behavior in this multilayer memristive neural network, one can find when the whole multilayer network is in a synchronization state, not only the spatiotemporal consistency of the CS in each layer neural networks is observed, but also the memductance of all memristive synapses is completely synchronized. Our results suggest that the electromagnetic induction may play an important role in regulating the dynamic behavior of neural networks, and the introduction of memristive synapse into the biological neural network will provide useful clues for revealing the memory behavior of the neural system in human brain.

Modulated wave formation in myocardial cells under electromagnetic radiation

We exclusively analyze the onset and condition of formation of modulated waves in a diffusive FitzHugh–Nagumo model for myocardial cell excitations. The cells are connected through gap junction coupling. An additive magnetic flux variable is used to describe the effect of electromagnetic induction, while electromagnetic radiation is imposed on the magnetic flux variable as a periodic forcing. We used the discrete multiple scale expansion and obtained, from the model equations, a single differential-difference amplitude nonlinear equation. We performed the linear stability analysis of this equation and found that instability features are importantly influenced by the induced electromagnetic gain. We present the unstable and stable regions of modulational instability (MI). The resulting analytic predictions are confirmed by numerical experiments of the generic equations. The results reveal that due to MI, an initial steady state that consisted of a plane wave with low amplitude evolves into a modulated localized wave patterns, soliton-like in shape, with features of synchronization. Furthermore, the formation of periodic pulse train with breathing motion presents a disappearing pattern in the presence of electromagnetic radiation. This could provide guidance and better understanding of sudden heart failure exposed to heavily electromagnetic radiation.

Stochastic resonance and synchronization behaviors of excitatory-inhibitory small-world network subjected to electromagnetic induction**Project supported by the National Natural Science Foundation of China (Grant No. 11172103).

The phenomenon of stochastic resonance and synchronization on some complex neuronal networks have been investigated extensively. These studies are of great significance for us to understand the weak signal detection and information transmission in neural systems. Moreover, the complex electrical activities of a cell can induce time-varying electromagnetic fields, of which the internal fluctuation can change collective electrical activities of neuronal networks. However, in the past there have been a few corresponding research papers on the influence of the electromagnetic induction among neurons on the collective dynamics of the complex system. Therefore, modeling each node by imposing electromagnetic radiation on the networks and investigating stochastic resonance in a hybrid network can extend the interest of the work to the understanding of these network dynamics. In this paper, we construct a small-world network consisting of excitatory neurons and inhibitory neurons, in which the effect of electromagnetic induction that is considered by using magnetic flow and the modulation of magnetic flow on membrane potential is described by using memristor coupling. According to our proposed network model, we investigate the effect of induced electric field generated by magnetic stimulation on the transition of bursting phase synchronization of neuronal system under electromagnetic radiation. It is shown that the intensity and frequency of the electric field can induce the transition of the network bursting phase synchronization. Moreover, we also analyze the effect of magnetic flow on the detection of weak signals and stochastic resonance by introducing a subthreshold pacemaker into a single cell of the network and we find that there is an optimal electromagnetic radiation intensity, where the phenomenon of stochastic resonance occurs and the degree of response to the weak signal is maximized. Simulation results show that the extension of the subthreshold pacemaker in the network also depends greatly on coupling strength. The presented results may have important implications for the theoretical study of magnetic stimulation technology, thus promoting further development of transcranial magnetic stimulation (TMS) as an effective means of treating certain neurological diseases.

Coupled simulation of electromagnetic induction and induced polarization effects using stretched exponential relaxation

We have developed a new algorithm for 3D time-domain electromagnetic (EM) modeling, taking full account of induced polarization (IP) and the coupling between EM and IP effects. The algorithm can be used to model grounded source IP surveys that indicate EM induction effects and airborne time-domain EM surveys that exhibit IP effects. IP effects are most often approximated as static or modeled in the frequency domain, using frequency-dependent electrical conductivity. It is difficult to translate the frequency-dependent conductivity approach directly to the time domain in a computationally efficient manner. We take an alternative approach in which we model IP relaxations in time using the stretched exponential (SE) function. We incorporate this IP model into a direct time-stepping discretization of the quasistatic time-domain Maxwell equations. We found that modeling of IP effects with this SE approach is asymptotically equivalent to the commonly used Cole-Cole model of IP transformed to the time domain. We have implemented our algorithm using efficient numerical methods that allow it to tackle large-scale problems and are amenable to use in inversion. In particular, we have developed a parallel time-stepping technique that allows us to compute transient electric fields at multiple time steps simultaneously. We demonstrate the behavior of the SE model of IP decay and the efficiency of our algorithm by applying it to synthetic numerical examples that simulate a grounded source IP survey with significant EM effects and a concentric-loop airborne EM sounding over a chargeable body.

Field coupling-induced pattern formation in two-layer neuronal network

The exchange of charged ions across membrane can generate fluctuation of membrane potential and also complex effect of electromagnetic induction. Diversity in excitability of neurons induces different modes selection and dynamical responses to external stimuli. Based on a neuron model with electromagnetic induction, which is described by magnetic flux and memristor, a two-layer network is proposed to discuss the pattern control and wave propagation in the network. In each layer, gap junction coupling is applied to connect the neurons, while field coupling is considered between two layers of the network. The field coupling is approached by using coupling of magnetic flux, which is associated with distribution of electromagnetic field. It is found that appropriate intensity of field coupling can enhance wave propagation from one layer to another one, and beautiful spatial patterns are formed. The developed target wave in the second layer shows some difference from target wave triggered in the first layer of the network when two layers are considered by different excitabilities. The potential mechanism could be pacemaker-like driving from the first layer will be encoded by the second layer.

Synchronous dynamics in neural system coupled with memristive synapse

To study the collective behavior and the regulation mechanism of memristor, the Hindmarsh–Rose neuron cells are selected as the units to construct a coupled system by using the cubic flux-controlled memristor as a connecting synapses, both the firing mode and synchronous behavior in this coupled system are investigated. In this paper, a coefficient is introduced to describe the effect of electromagnetic induction of membrane potential, and the weights of synaptic connection between cells are selected as an adjustable parameter. We have found that, on the one hand, for the certain external current, the firing pattern of neurons could maintain normal state induced by proper electromagnetic induction, and the transition between different firing states could also be observed. On the other hand, both the synchronous effects could be effectively enhanced and the domain of synchronous parameters could be also expanded by tuning the electromagnetic parameters. Particularly, when the coupled system is in the synchronization state, one can see that the firing behavior of neurons will simultaneously change with the varying of the memristance. The similar phenomenon could also be found by introducing the external stimulus signal with a certain frequency. It means that the plasticity of biological synapses could be effectively mimicked by using the memristive synapse and thus the effective memory function of the memristor to the external stimulus signal may be realized. Above results may not only provide some useful clues for understanding the dynamic behavior of neural system coupled with memristive synapses, but also afford us some inspiration to simulate human brain memory, forgetting or some other functions by using the memristor neural network in the future.

Time domain electromagnetic‐induced polarisation: extracting more induced polarisation information from grounded source time domain electromagnetic data

ABSTRACTElectrical induced polarisation surveys are used to detect chargeable materials in the earth. For interpretation of time domain electrical‐induced polarisation data a common procedure is to first invert the direct current data (electric current on time) to recover conductivity and then invert the induced polarisation data (current off‐time) to recover chargeability. This direct current‐induced polarisation inversion procedure assumes that the off time data are free of secondary electromagnetic induction effects. To comply with this, early time data are often discarded or not recorded. For mid‐time data, an electromagnetic decoupling technique, which removes electromagnetic induction in the observations, needs to be implemented. Usually, responses from a half‐space or a layered earth are subtracted. Recent capability in three‐dimensional time domain electromagnetic forward modelling and inversion allows to revisit these procedures. In a Time domain electromagnetic‐induced polarisation survey, a high sampling rate allows early time channels of the electromagnetic data to be recorded. The recovery of chargeability then follows a three‐step workflow: (i) invert early time channel time domain electromagnetic data to recover the three‐dimensional conductivity; (ii) use that conductivity to compute the time domain electromagnetic response at later time channels and subtract this fundamental response from the observations to extract the induced polarisation responses, and (iii) invert the induced polarisation responses to recover a three‐dimensional chargeability. This workflow effectively removes electromagnetic induction effects in the observations and produces better chargeability and conductivity models compared with conventional approaches. In a synthetic example involving a gradient array, we show that the conductivity structure obtained from the early time channel data, which are usually discarded, is superior to that obtained from the steady state direct current voltages. This adds a further reason to collect these electromagnetic data.

Electrical Activity in a Time-Delay Four-Variable Neuron Model under Electromagnetic Induction.

To investigate the effect of electromagnetic induction on the electrical activity of neuron, the variable for magnetic flow is used to improve Hindmarsh–Rose neuron model. Simultaneously, due to the existence of time-delay when signals are propagated between neurons or even in one neuron, it is important to study the role of time-delay in regulating the electrical activity of the neuron. For this end, a four-variable neuron model is proposed to investigate the effects of electromagnetic induction and time-delay. Simulation results suggest that the proposed neuron model can show multiple modes of electrical activity, which is dependent on the time-delay and external forcing current. It means that suitable discharge mode can be obtained by selecting the time-delay or external forcing current, which could be helpful for further investigation of electromagnetic radiation on biological neuronal system.

Mode transition in electrical activities of neuron driven by high and low frequency stimulus in the presence of electromagnetic induction and radiation

The Hindmarsh–Rose (HR) neuron model has been improved to investigate the complex electrophysiological and various physical phenomena at the level of single cell, for example, time-varying action potential can be induced by the exchange of ion currents and the fluctuation of ions concentration in the cell. When the magnetic flux is considered as a new variable associated to magnetic field, the improved HR neuron model can describe the effects of electromagnetic induction and radiation on membrane potential, where a memristor is used to bridge the membrane potential and the magnetic flux. In this paper, considering the magnetic flux driven, respectively, by the periodic high and low frequency electromagnetic radiation and the Gaussian white noise, the improved HR neuron model is employed to study the modes transition in electrical activities of neuron. The thought-provoking phenomena are detected and discussed by using bifurcation analysis on sampled time series of membrane potential. It is found that the electrical modes of HR neuron model under various parameters have different responses to the periodic high–low frequency electromagnetic radiation and the Gaussian white noise.

Dynamical Response of Electrical Activities in Digital Neuron Circuit Driven by Autapse

Neuron models are available for computational neurodynamics and the main dynamical properties can be reproduced in the numerical scheme for further dynamical analysis. During model setting, some important biophysical factors should be considered and thus reliable neuron models can be approached. In this paper, a neuron model driven by autapse connection is investigated with the effect of electromagnetic induction being considered as well. A digital neuronal circuit is designed by using FPGA, the dynamical response and biological function of autapse connection. It is found that positive feedback in autapse can modulate the oscillating behaviors in the digital circuit, which could be effective for further investigation on digital neuronal network.

Self-Powered Nanocomposites under an External Rotating Magnetic Field for Noninvasive External Power Supply Electrical Stimulation.

Electrical stimulation in biology and gene expression has attracted considerable attention in recent years. However, it is inconvenient that the electric stimulation needs to be supplied an implanted power-transported wire connecting the external power supply. Here, we fabricated a self-powered composite nanofiber (CNF) and developed an electric generating system to realize electrical stimulation based on the electromagnetic induction effect under an external rotating magnetic field. The self-powered CNFs generating an electric signal consist of modified MWNTs (m-MWNTs) coated Fe3O4/PCL fibers. Moreover, the output current of the nanocomposites can be increased due to the presence of the magnetic nanoparticles during an external magnetic field is applied. In this paper, these CNFs were employed to replace a bullfrog's sciatic nerve and to realize the effective functional electrical stimulation. The cytotoxicity assays and animal tests of the nanocomposites were also used to evaluate the biocompatibility and tissue integration. These results demonstrated that this self-powered CNF not only plays a role as power source but also can act as an external power supply under an external rotating magnetic field for noninvasive the replacement of injured nerve.

Synchronization between neurons coupled by memristor

Synapse plays an important role in signal exchange and information encoding between neurons. Electric and chemical synapses are often used to investigate the synchronization in electrical activities of neurons. In this paper, memristor is used to connect two neurons and the phase synchronization in electrical activities is discussed. Inter-spike interval (ISI) is calculated from the sampled time series for membrane potential, and the dependence of coupling intensity on phase synchronization of neuron is investigated and the effect of electromagnetic induction is considered. Furthermore, the synchronization stability of network is detected under noise; a statistical synchronization factor is also calculated. It is found synchronization can be enhanced under memristor coupling and appropriate noise is also helpful for synchronization stability.

Numerical evaluation of active source magnetics as a method for imaging high-resolution near-surface magnetic heterogeneity

We have evaluated a geophysical method that uses a low-frequency magnetic source to image subsurface magnetic heterogeneity. This active source approach can be used to image magnetic features at higher resolutions than the conventional passive geomagnetic method. Importantly, this frequency-domain active source approach is independent of the effects of remanent magnetization, which complicates the interpretation of geomagnetic data. We carried out forward modeling of frequency-domain electromagnetic (EM) data and we found that, at frequencies of a few hertz, the magnetostatic response due to the induced magnetization dominates the EM induction response. The result suggests that it is possible to make magnetic interpretation of low-frequency EM data without having to consider the conductivity structure and the corresponding EM induction effect. We compare the anomalous magnetic responses with magnetic noise components and find that the proposed active source magnetic (ASM) method has a depth of investigation of approximately 300 m. Free-space field and inductive noise are considered as the most important issues affecting the depth of investigation. We also determine the potential for linear interpretation of magnetic heterogeneity under 0.1 SI by showing that the low-frequency magnetic response can be approximated by a linear magnetic response. In our synthetic experiments, inversion of the ASM data shows a marked enhancement in resolution, with no effect of the remanent magnetization, in contrast to geomagnetic inversion. These results show that the ASM method is a useful geophysical tool, especially when high-resolution imaging of magnetic susceptibility is required or where strong remanent magnetization complicates the magnetic interpretation.

Electromagnetic induction and radiation-induced abnormality of wave propagation in excitable media

Continuous wave emitting from sinus node of the heart plays an important role in wave propagating among cardiac tissue, while the heart beating can be terminated when the target wave is broken into turbulent states by electromagnetic radiation. In this investigation, local periodical forcing is applied on the media to induce continuous target wave in the improved cardiac model, which the effect of electromagnetic induction is considered by using magnetic flux, then external electromagnetic radiation is imposed on the media. It is found that target wave propagation can be blocked to stand in a local area and the excitability of media is suppressed to approach quiescent but homogeneous state when electromagnetic radiation is imposed on the media. The sampled time series for membrane potentials decrease to quiescent state due to the electromagnetic radiation. It could accounts for the mechanism of abnormality in heart failure exposed to continuous electromagnetic field.

Pattern formation in diffusive excitable systems under magnetic flow effects

We study the spatiotemporal formation of patterns in a diffusive FitzHugh–Nagumo network where the effect of electromagnetic induction has been introduced in the standard mathematical model by using magnetic flux, and the modulation of magnetic flux on membrane potential is realized by using memristor coupling. We use the multi-scale expansion to show that the system equations can be reduced to a single differential-difference nonlinear equation. The linear stability analysis is performed and discussed with emphasis on the impact of magnetic flux. It is observed that the effect of memristor coupling importantly modifies the features of modulational instability. Our analytical results are supported by the numerical experiments, which reveal that the improved model can lead to nonlinear quasi-periodic spatiotemporal patterns with some features of synchronization. It is observed also the generation of pulses and rhythmics behaviors like breathing or swimming which are important in brain researches.