Coherence Resonance Research Articles

Firing patterns transitions and resonance effects of the extended Hindmarsh-Rose neural model with Gaussian noise and transcranial magneto-acousto-electrical stimulation

Considering the fact that the typical three-variable Hindmarsh-Rose(HR) neural model has limitations in describing the complex non-linear features and precise behavior patterns of neuron, the influences of transcranial magneto-acousto-electrical stimulation(TMAES) on firing patterns and resonance effects are analyzed based on an extended HR neural model in this paper. Obtained results show that TMAES can induce transitions in the firing patterns of extended HR neuron, such as spiking and multi-periodic bursting state, etc If appropriate parameters are selected, the multimodal discharge modes can also be observed. Coefficient of variation is calculated to further investigate the effect of TMAES and Gaussian white noise on the firing rhythm of extended HR neuron, and relevant results indicate that TMAES can induce coherent resonance phenomena in HR neuronal systems similar to the effects of Gaussian white noise, which reveals a new mechanism of coherent resonance induced by TMAES. Further more, TMAES can also regulate coefficient of variation to exhibit anti-coherent resonance and multiple anti-coherent resonance structures, exhibiting richer regulatory functions than Gaussian white noise in regulating neuronal firing rhythm. This study seeks to enhance the understanding of the processes that influence the firing patterns and coherence degree of neuron under TMAES in neuroses or psychoses.

Coherence resonance in a memristive map neuron and adaptive energy regulation

Involvement of memristive term and additive physical variables including magnetic flux and charge can enhance the physical description of the biophysical neurons. Neural circuits coupled with memristors can be built and tamed to mimic the intrinsic biophysical characteristics and dynamical properties of biological neurons, and these memristive oscillator models are effective in predicting the mode transition in neural activities and self-organization in collective electric behaviors of neural networks. Any proposal of memristive map neurons requires reliable physical description. For example, the energy definition and self-adaptive working mechanism are crucial to verify the reliability of memristive maps. A capacitive variable is useful to describe the membrane potential, while the complexity of ion channels requires careful evaluation and description by using inductive variables relative to the electromagnetic field. In this work, a charge-controlled memristor is connected to an inductor in series for building a hybrid ion channel, and then a capacitor and a nonlinear resistor are combined to couple the ion channel. As a result, a simple memristive neural circuit is designed to discern the inner effect of electric field and magnetic field synchronously. The energy function is defined and verified with theoretical proof. Furthermore, a linear transformation is applied to convert this memristive neuron into a memristive map with an exact energy description, in which its dynamics and mode transition will be controlled by an adaptive law when its energy is beyond the threshold. Additive noise is imposed to induce coherence resonance, which can be detected by using the statistical analysis and average value for Hamilton energy function during changes in noise intensity. This scheme provides guidance for energy definition in memristive maps and the intrinsic energy regulation mechanism in neural activities is explained from physical and dynamical aspects.

A map neuron with piezoelectric membrane, energy regulation and coherence resonance

The cell membrane has a layered structure, which separates the intracellular and extracellular ions for developing gradient electromagnetic field, and its flexible property enables the capacitance dependence on the shape deformation due to external stimuli. Therefore, piezoelectric membrane can be suitable to describe the biophysical characteristic of cell membrane and equivalent circuit approach becomes important. In this paper, two capacitors are connected via a piezoelectric ceramic and two additive memristive channels are combined to mimic the neural activity response with exact energy description. Furthermore, linear transformation is applied to obtain an equivalent piezoelectric map neuron and energy function is provided to explore the adaptive energy regulation on mode selection in neural activities. Coherence resonance is detected and analyzed. The map neuron can also be considered as an auditory neuron for perceiving acoustic signals and its membrane property is considered from physical aspect.

Aspartic proteases gene family: Identification and expression profiles during stem vascular development in tobacco

Aspartic proteases (APs) constitute a large family in plants and are widely involved in diverse biological processes, like chloroplast metabolism, biotic and abiotic stress responses, and reproductive development. In this study, we focused on overall analysis of the APs genes in tobacco. Our analysis included the phylogeny and cis-elements in the cell wall-associated promoters of these genes. To characterize the expression patterns of APs genes in stem vascular development. The tissue expression analysis showed that NtAED3-like was preferentially expressed in the differentiating xylem and phloem cells of the vascular system. Based on histochemical staining analysis showed that the NtAED3-like gene was specifically expressed in stem vascular tissue, root vascular tissue, and petiole vascular tissue. The TdT-mediated dUTP nick-end labeling (TUNEL) assay illustrated a delayed progression of programmed cell death (PCD) within the xylem of the ko-ntaed3a-like mutant, relative to the wild type. The mutant ko-ntaed3a-like exhibited a phenotype of thinning stem circumference and changed in xylem structure and lignin content. In addition, the two-dimension heteronuclear single quantum coherent nuclear magnetic resonance (2D-HSQC) analysis of three milled wood lignins (MWLs) showed that the content of β-O-4 connection in ko-ntaed3a-like decreased slightly compared with wild type. In conclusion, this study provides our understanding of the regulation of vascular tissue development by the NtAED3-like gene in tobacco and provides a better basis for determining the molecular mechanism of the aspartic protease in secondary cell wall (SCW) development.

Nonlinear Charge Transport and Excitable Phenomena in Semiconductor Superlattices.

Semiconductor superlattices are periodic nanostructures consisting of epitaxially grown quantum wells and barriers. For thick barriers, the quantum wells are weakly coupled and the main transport mechanism is a sequential resonant tunneling of electrons between wells. We review quantum transport in these materials, and the rate equations for electron densities, currents, and the self-consistent electric potential or field. Depending on superlattice configuration, doping density, temperature, voltage bias, and other parameters, superlattices behave as excitable systems, and can respond to abrupt dc bias changes by large transients involving charge density waves before arriving at a stable stationary state. For other parameters, the superlattices may have self-sustained oscillations of the current through them. These oscillations are due to repeated triggering and recycling of charge density waves, and can be periodic in time, quasiperiodic, and chaotic. Modifying the superlattice configuration, it is possible to attain robust chaos due to wave dynamics. External noise of appropriate strength can generate time-periodic current oscillations when the superlattice is in a stable stationary state without noise, which is called the coherence resonance. In turn, these oscillations can resonate with a periodic signal in the presence of sufficient noise, thereby displaying a stochastic resonance. These properties can be exploited to design and build many devices. Here, we describe detectors of weak signals by using coherence and stochastic resonance and fast generators of true random sequences useful for safe communications and storage.

A memristive map neuron under noisy electric field

From a neural circuit to a biophysical neuron model, standard scale transformation requires the combination of physical variables including capacitance and resistance associated with a capacitor C and a resistor R, respectively. The voltage across the capacitor is used to mimic the membrane potential for neurons. Therefore, most of the neuron models contain one or two capacitive variables for the membrane potential. In fact, the reference time scale (RC) can be replaced by combinating parameters for inductor and resistor during approach of scale transformation for physical variables in the neural circuit. In this study, a simple nonlinear circuit is built by applying two inductors, a charge-controlled memristor (CCM), a nonlinear resistor and a resistor. The circuit equations are derived to develop a memristive oscillator with exact description of energy function. Furthermore, an equiavlent memristive map is obtained by applying linear transformation on the sampled variables from the memristive oscillator. The Hamilton energy function for memristive oscillator is calculated by using the Helmholtz's theorem, and then the memristive map is given in energy function in discrete form. The memristive map has rich dynamic characteristics and can identify coherence resonance under applying noisy electric field. This scheme will provide a theoretical guidance for building nonlinear circuits and maps without using capacitors.

Lévy noise-induced coherence resonance in neural maps

In this contribution, we investigate coherence resonance in map-based neural models in the presence of Lévy noise. The impact of different parameters of Lévy noise on the oscillatory activity is analyzed for several neural maps, including the Rulkov map, the Chialvo map, and the Courbage–Nekorkin–Vdovin map. We demonstrate that the coherence of noise-excited oscillations decreases as the stability index decreases. On the other hand, changing the skewness parameter can both reduce and, conversely, enhance coherence. We also explore and establish the peculiarities of coherence resonance in the neural maps under study depending on the Lévy noise parameters.

Spin resonance spectroscopy with an electron microscope

Coherent spin resonance methods, such as nuclear magnetic resonance and electron spin resonance spectroscopy, have led to spectrally highly sensitive, non-invasive quantum imaging techniques. Here, we propose a pump-probe spin resonance spectroscopy approach, designed for electron microscopy, based on microwave pump fields and electron probes. We investigate how quantum spin systems couple to electron matter waves through their magnetic moments and how the resulting phase shifts can be utilized to gain information about the states and dynamics of these systems. Notably, state-of-the-art transmission electron microscopy provides the means to detect phase shifts almost as small as that due to a single electron spin. This could enable state-selective observation of spin dynamics on the nanoscale and indirect measurement of the environment of the examined spin systems, providing information, for example, on the atomic structure, local chemical composition and neighboring spins.

Nonlinear frequency shift caused by asymmetry of the multipeak coherent population trapping resonance

Nonlinear frequency shift caused by asymmetry of the multipeak coherent population trapping resonance

An image-based spatiotemporal approach for detecting coherence resonance in annular model gas-turbine combustor

When exposed to an intermediate level of noise, dynamical systems near a Hopf bifurcation can reveal deterministic information about the impending oscillatory mode. Known as the coherence resonance, this phenomenon can be used as a precursor to thermoacoustic instability, which is detrimental to practical combustors. In this study, we apply a spatiotemporal dimensionality reduction method, namely the sparsity-promoting dynamic mode decomposition, to detect coherence resonance in a combustor that exhibits a transition toward thermoacoustic instability. We stochastically excite the annular model gas-turbine combustor and acquire its high-speed image for spatiotemporal analysis. As a result, we find that the impending mode of instability is best identified with a maximum clustering level at the intermediate noise amplitude, implying the existence of coherence resonance. To the best of our knowledge, this is the first time that an image-based method has been used for the detection of coherence resonance, opening new possibilities for the prediction of combustion instability without requiring embedded sensors.

Firing behaviors of memristor-based Rulkov neuron map using energy method

Neural firing behavior is essentially the energy transmission between neurons, which can represent the information process of nervous system. Consequently, it is crucial to delve deeply into the intricate firing activity and energy characteristics of neurons. The Helmholtz theorem offers an accurate energy description for continuous neurons. However, the energy of discrete neurons remains under-explored due to lacking an effective theoretical framework. In this paper, we introduce a novel Rulkov neuron map that incorporates a charge-controlled memristor for imitating the effect of electromagnetic radiation. The Hamilton energy method is utilized to investigate the firing behaviors of this neuron map. A pivotal step in our analysis is meticulously scaling the parameters and variables to transform the neuron map into a continuous oscillator, which is essential for obtaining its energy function. In the exploration of the Rulkov neuron map, a range of intricate firing behaviors, including mixed-mode firing, bursting firing and coexistence firing, are observed. Notably, the occurrence of coherence resonance is detected under noise excitation. These behaviors are characterized by the time evolution of membrane potential and Hamilton energy spectrum. Furthermore, FPGA-based experiments are conducted to confirm the numerical results.

Noise-induced excitation wave and its size distribution in coupled FitzHugh-Nagumo equationson a square lattice.

There are various research topics such as stochastic resonance, coherent resonance, and neuroavalanche in excitable systems under external noises. We perform numerical simulation of coupled noisy FitzHugh-Nagumo equationson the square lattice. Excitation waves are generated most efficiently at an intermediate noise strength. The cluster size distributions obey a power-law-like distribution at a certain parameter range. However, we consider that this is not a self-organized critical phenomenon, partly because the exponent of the power law is not constant. We have studied the propagation of excitation waves in the coupled noisy FitzHugh-Nagumo equationswith a one-dimensional pacemaker region and found that there is a phase-transition-like phenomenon from the short-range propagation to the whole-system propagation by changing the noise strength T. The power-law distribution is observed most clearly near the phase transition of the propagation of excitation waves in the coupled noisy FitzHugh-Nagumo equationswithout the one-dimensional pacemaker.

Self-injection locked low-noise Brillouin random fiber laser via dynamic fiber grating for QAM coherent communication

A low-noise Brillouin random fiber laser (BRFL) based on dynamic fiber grating (DFG)-assisted self-injection locking (SIL) as a laser carrier for coherent communication is proposed and experimentally demonstrated. The utilization of the DFG-based SIL basically guarantees an innovative purification of ultra-narrow-linewidth laser radiation after the removal of residual random modes from the gain competition, enabling unprecedented long-term frequency-stabilized coherent lasing resonance over a record of 30 s. Consequently, the relative intensity noise of the generated Stokes random laser is significantly suppressed by ∼20 dB, and the frequency/phase noise imposed by random mode hopping is additionally mitigated. Meanwhile, stimulated Brillouin scattering and randomly distributed Rayleigh scattering along the kilometer-long single-mode fiber further suppress laser frequency/phase noise, benefiting the ultra-narrow laser linewidth of 450 Hz. As a proof-of-concept, an 8-Gbaud (32 Gb/s) 16-quadrature amplitude modulation transmission based on the proposed self-injection locked low-noise BRFL as the laser carrier is demonstrated, achieving a low bit error rate of 3.02×10−5. The impact of the laser noise on coherent communication is systematically investigated, highlighting the potential in high-capacity coherent communication.

Effect of correlation time of combustion noise on early warning indicators of thermoacoustic instability.

In this paper, we analyze the effects of finite correlation time (noise color) of combustion noise on noise-induced coherence and early warning indicators (EWIs) via numerical and experimental studies. We consider the Rijke tube as a prototypical combustion system and model combustion noise as an additive Ornstein-Uhlenbeck process while varying noise intensity and correlation time. We numerically investigate corresponding effects on coherence resonance and multi-fractal properties of pressure fluctuations. Subsequently, we experimentally validate results and elucidate the influence of noise color and intensity on trends in coherence resonance and multi-fractal measures that can be expected in a practical scenario using an electroacoustic simulator. We find that the coherence factor, which quantifies the relative contribution of coherent oscillations in a noisy signal, increases as the system approaches the thermoacoustic instability-irrespective of the correlation time. It works at most levels of combustion noise (except for too low and too high noise levels). The Hurst exponent reduces as the system approaches thermoacoustic instability only when the correlation time is small. These results have implications on the prediction and monitoring of thermoacoustic instability in practical combustors.

Coherent‐Resonance Enhancement of Sensing at the Exceptional Points

AbstractThe branch point singularities in the Riemann surface of the parameter space are known as the exceptional points (EPs), at which two or more eigenvalues and their associated eigenvectors simultaneously coalesce. The abrupt bifurcation property around an EP shows a strong spectral response to external perturbations, and hence, is exploited as a new approach to ultrasensitive sensors. Recently, an intriguing proposal for implementing second‐order EPs in optical resonators with external perturbations has shown the superiority of non‐Hermitian degeneracies in the enhancement of sensing. Of particular importance is further improving the sensitivity to even greater extents. To this end, a novel physical mechanism is proposed, namely introducing extra resonances of external Rayleigh scatterers to accomplish strong‐coupling augmentation in sensing. The results, grounded by both theoretical coupled‐mode‐theory calculations and experimental spectral‐domain measurements, show that the EP‐based sensor working at the coherent resonance equips simultaneously substantial perturbation strength and azimuth sensitivity even subjected to extremely weak perturbations. This work paves the way to a new class of highly functional tunable sensors for applications in optics, microwave and acoustics.

Capturing Coherent Magnons by Tip-Assisted Terahertz Spectroscopy.

Our study highlights the versatility of tip-assisted terahertz spectroscopy in probing coherent magnons, the elementary quanta of spin waves in magnetic materials. We identify two distinct coherent magnon types in canted antiferromagnet YFeO3. The remarkable consistency with far-field terahertz spectroscopy in crucial magnon parameters, such as coherence time and resonance frequency, firmly establishes the credibility of tip-assisted terahertz spectroscopy. Notably, we capture more coherent ferromagnetic magnons near the sample surface, underscoring the strength of the technique. This approach paves the way for local, free-standing, and real-space investigations of spin waves in solid magnets.

Contrast of Ramsey-CPT Fringes in Quenching and Depolarizing Gases

Molecular nitrogen is often used as a buffer gas in cells with alkali metals due to its known ability to quench resonant fluorescence. It is widely believed that the suppression of spontaneous emission decreases the width of coherent population trapping resonance. However, our recent results have not confirmed this positive action of molecular nitrogen within the typical range of 87Rb concentrations and buffer gas pressures. On the opposite, we have observed the negative influence of quenching, the decrease in contrast of the coherent population trapping resonance in $${{\sigma }^{ + }}$$–$${{\sigma }^{ + }}$$ configuration. In this work, we further confirm these results implementing the Ramsey spectroscopy, and compare the characteristics of the central fringe in nitrogen and neon, and show that the latter provides a significantly better contrast-to-width ratio.

Forecasting coherence resonance in a stochastic Fitzhugh–Nagumo neuron model using reservoir computing

We delve into the intriguing realm of reservoir computing to predict the intricate dynamics of a stochastic FitzHugh–Nagumo neuron model subjected to external noise. Through innovative reservoir design and training, we unveil the remarkable capacity of a reservoir computer to forecast the behavior of this stochastic system across a wide spectrum of noise intensity variations. Notably, our reservoir computer astutely replicates the intricate phenomenon of coherence resonance in the stochastic FitzHugh–Nagumo neuron, signifying the superior modeling capabilities of this approach. A detailed examination of the microscopic dynamics within the reservoir’s hidden layer reveals the emergence of distinct neuronal clusters, each displaying unique behaviors. Certain neurons within the reservoir are adept at faithfully reproducing the dynamical traits of the neuron, particularly the spike generation mechanism. In contrast, the remaining neurons within the reservoir seem to emulate stochastic influences with remarkable precision, accurately capturing the moments of spike generation in the neuron under the sway of noise. This innovative reservoir design proves to be highly effective across a diverse range of noise control parameters, faithfully replicating the essential characteristics of the original stochastic FitzHugh–Nagumo neuron. These findings illuminate the potential of reservoir computing to model and predict the dynamics of complex stochastic systems, showcasing its adaptability and versatility in understanding and simulating natural phenomena.

A Josephson junction-coupled neuron with double capacitive membranes

The channel currents have distinct magnetic field effect and any changes of the electromagnetic field or electirc stimulus will change the membrane potential effectively. A feasible neuron model considers the distinct physical characteristic is more suitable to mimic the neural activities accompanying with shift in energy level. A Josephson junction (JJ) is connected to a neural circuit for estimating the effect of external magnetic field and two capacitors are connected via a linear resistor for mimicing the capacitive field beside two sides of the cell membrane. Its equivalent Hamilton energy is calculated to show the relation between firing mode and energy level. Noisy disturbance is imposed to predict the occurrence of coherence resonance, and the biophysical neuron is excited to present higher energy level. This new neuron model can address the field effect and the biophysical property of cell membrane considered as combination of capacitive fields in double capacitors. It can mimic the physical property of outer and inner membranes, and energy exchange across the double membranes explains the energy mechanism in neural activities. Time-varying energy diveristy between capacitive field is crucial for supporting continuous firing activities. The JJ channel discerns slight changes in external magnetic field and regularity is stabilized under coherence resonance in presence of noisy excitation on the membrane or ion channels.

Energy controls wave propagation in a neural network with spatial stimuli

Nervous system has distinct anisotropy and some intrinsic biophysical properties enable neurons present various firing modes in neural activities. In presence of realistic electromagnetic fields, non-uniform radiation activates these neurons with energy diversity. By using a feasible model, energy function is obtained to predict the growth of synaptic connections of these neurons. Distribution of average value of the Hamilton energy function vs. intensity of noisy disturbance can predict the occurrence of coherence resonance, which the neural activities show high regularity by applying noisy disturbance with moderate intensity. From physical viewpoint, the average energy value has similar role average power for the neuron. Non-uniform spatial disturbance is applied and energy is injected into the neural network, statistical synchronization factor is calculated to predict the network synchronization stability and wave propagation. The intensity for field coupling is adaptively controlled by energy diversity between adjacent neurons. Local energy balance will terminate further growth of the coupling intensity; otherwise, heterogeneity is formed in the network due to energy diversity. Furthermore, memristive channel current is introduced into the neuron model for perceiving the effect of electromagnetic induction and radiation, and a memristive neuron is obtained. The circuit implement of memristive circuit depends on the connection to a magnetic flux-controlled memristor into the mentioned neural circuit in an additive branch circuit. The connection and activation of this memristive neural network are controlled under external spatial electromagnetic radiation by capturing enough field energy. Continuous energy collection and exchange generate energy diversity and synaptic connection is created to regulate the synchronous firing patterns and energy balance.