Mechanism Of Stochastic Resonance Research Articles

Stochastic resonance: The response to envelope modulation signal for neural networks with different topologies

Neurons receive complex multiple signals from different regions in the biology system, and the neuron information carried by the multi-frequency signals can be detected by the neural system. Noise can induce stochastic resonance and promote the response of the neural network to a weak signal. The responses to weak envelope modulation signals for the neural networks with different topologies are investigated under electromagnetic induction and Gaussian white noise. We analyze and compare the stochastic resonance with tuning system parameters. The results show that in the three different networks, network systems exhibit double resonance with the variation of noise and beat frequency. Appropriate noise and electromagnetic induction can promote the generation of stochastic resonance and affect the response of envelope modulation signal in the neural network. The difference is that the three different networks differ in the noise intensity required to induce stochastic resonance and the effective range of the electrical coupling strength. Unlike the other two networks, the Fourier coefficient in scale-free network increases with the electromagnetic induction, and the number of neurons has no affect on stochastic resonance. Our results are conducive to understanding the mechanism of stochastic resonance in envelope modulation signals and electromagnetic induction.

Diversity-induced decoherence.

We analyze the effect of small-amplitude noise and heterogeneity in a network of coupled excitable oscillators with strong timescale separation. Using mean-field analysis, we uncover the mechanism of a nontrivial effect-diversity-induced decoherence (DIDC)-in which heterogeneity modulates the mechanism of self-induced stochastic resonance to inhibit the coherence of oscillations. We argue that DIDC may offer one possible mechanism via which, in excitable neural systems, generic heterogeneity and background noise can synergistically prevent unwanted resonances that may be related to hyperkinetic movement disorders.

Different Stochastic Resonances Induced by Multiplicative Polynomial Trichotomous Noise in a Fractional Order Oscillator with Time Delay and Fractional Gaussian Noise

A general investigation on the mechanism of stochastic resonance is reported in a time-delay fractional Langevin system, which endues a nonlinear form multiplicative colored noise and fractional Gaussian noise. In terms of theoretical analysis, both the expressions of output steady-state amplitude and that of the first moment of system response are obtained by utilizing stochastic averaging method, fractional Shapiro and Laplace methods. Due to the presence of trichotomous colored noise, the excitation frequency can induce fresh multimodal Bona fide stochastic resonance, exhibiting much more novel dynamical behaviors than the non-disturbance case. It is verified that multimodal pattern only appears with small noise switching rate and memory damping order. The explicit expressions of critical noise intensity corresponding to the generalized stochastic resonance are given for the first time, by which it is determined that nonlinear form colored noise induces much more of a comprehensive resonant phenomena than the linear form. In the case of slow transfer rate noise, a newfangled phenomenon of double hypersensitive response induced by a variation in noise intensity is discovered and verified for the first time, with the necessary range of parameters for this phenomenon given. In terms of numerical scheme, an efficient and feasible algorithm for generating trichotomous noise is proposed, by which an algorithm based on the Caputo fractional derivative are applied. The numerical results match well with the analytical ones.

Non-Gaussian noise and autapse-induced inverse stochastic resonance in bistable Izhikevich neural system under electromagnetic induction

Inverse stochastic resonance refers to the phenomenon that the average firing rate of a neuron is inhibited by noise, of which mechanism is widely used in a variety of biological cells and economic phenomena. In this paper, a bistable Izhikevich neural model and triple-neuron feed-forward loop Izhikevich neural network motifs under the effects of electromagnetic induction are constructed to investigate the phenomenon of inverse stochastic resonance induced by Non-Gaussian colored noise and electrical autapse. It is found that there exists a minimum value of the average firing rate curve caused by intensity of non-Gaussian colored noise, which is the phenomenon of inverse stochastic resonance. Obtained results also show that the inverse stochastic resonance induced by electrical autapse shows a decaying oscillation process with respect to synaptic delay time, and further research indicates that average firing rate has several minimums as a function of time delay of electrical autapse, which is called multiple inverse stochastic resonance. Furthermore, the inverse stochastic resonance in triple-neuron feed-forward loop Izhikevich neural network motifs are also examined, and it is confirmed that the responses of single Izhikevich neuron and neural network motifs to different parameters show consistency under same conditions, but also show some differences. Finally, the effects of electromagnetic induction on inverse stochastic resonance are checked both in single Izhikevich neural model and feed-forward loop network motifs. Electromagnetic induction feedback gain coefficient k1 should not be too large under certain conditions, otherwise it may cause FFL network motifs to loss the function of suppressing the discharge activity. No matter how the values of electromagnetic induction parameter k2 and magnetic flux leakage coefficient k3 change, they basically cannot affect the ISR in the feed-forward loop neural network motifs The conclusions of this paper may help researchers understanding how using unique mechanism of inverse stochastic resonance to find advantages and avoid disadvantages in biomedical field and many interdisciplinary research.

Noisy Galvanic Vestibular Stimulation Combined With a Multisensory Balance Program in Older Adults With Moderate to High Fall Risk: Protocol for a Feasibility Study for a Randomized Controlled Trial.

BackgroundReduced mobility and falls are common among older adults. Balance retraining programs are effective in reducing falls and in improving balance and mobility. Noisy galvanic vestibular stimulation is a low-level electrical stimulation used to reduce the threshold for the firing of vestibular neurons via a mechanism of stochastic resonance.ObjectiveThis study aims to determine the feasibility of using noisy galvanic vestibular stimulation to augment a balance training program for older adults at risk of falls. We hypothesize that noisy galvanic vestibular stimulation will enhance the effects of balance retraining in older adults at risk of fallsMethodsIn this 3-armed randomized controlled trial, community dwelling older adults at risk of falling will be randomly assigned to a noisy galvanic vestibular stimulation plus balance program (noisy galvanic vestibular stimulation group), sham plus balance program (sham group), or a no treatment group (control). Participants will attend the exercise group twice a week for 8 weeks with assessment of balance and gait pretreatment, posttreatment, and at 3 months postintervention. Primary outcome measures include postural sway, measured by center of pressure velocity, area and root mean square, and gait parameters such as speed, step width, step variability, and double support time. Spatial memory will also be measured using the triangle completion task and the 4 Mountains Test.ResultsRecruitment began in November 2020. Data collection and analysis are expected to be completed by December 2022.ConclusionsThis study will evaluate the feasibility of using noisy galvanic vestibular stimulation alongside balance retraining in older adults at risk of falls and will inform the design of a fully powered randomized controlled trial.Trial RegistrationNew Zealand Clinical Trials Registry (ACTRN12620001172998); https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=379944International Registered Report Identifier (IRRID)DERR1-10.2196/32085

No evidence for stochastic resonance effects on standing balance when applying noisy galvanic vestibular stimulation in young healthy adults

Noisy galvanic vestibular stimulation (nGVS) at imperceptible levels has been shown to reduce body sway. This reduction was commonly attributed to the mechanism of stochastic resonance (SR). However, it has never been explicitly tested whether nGVS-induced effects on body sway consistently follow a SR-like bell-shaped performance curve with maximal reductions in a particular range of noise intensities. To test this, body sway in 21 young healthy participants was measured during varying nGVS amplitudes while standing with eyes closed in 3 conditions (quiet stance, sway referencing, sinusoidal platform tilts). Presence of SR-like response dynamics in each trial was assessed (1) by a goodness-of-fit analysis using an established SR-curve model and (2) by ratings from 3 human experts. In accordance to theory, we found reductions of body sway at one nGVS amplitude in most trials (75–95%). However, only few trials exhibited SR-like bell-shaped performance curves with increasing noise amplitudes (10–33%). Instead, body sway measures rather fluctuated randomly across nGVS amplitudes. This implies that, at least in young healthy adults, nGVS effects on body sway are incompatible with SR. Thus, previously reported reductions of body sway at particular nGVS intensities more likely result from inherent variations of the performance metric or by other yet unknown mechanisms.

Unified mechanism of inverse stochastic resonance for monostability and bistability in Hindmarsh-Rose neuron.

Noise is ubiquitous and has been verified to play constructive roles in various systems, among which the inverse stochastic resonance (ISR) has aroused much attention in contrast to positive effects such as stochastic resonance. The ISR has been observed in both bistable and monostable systems for which the mechanisms are revealed as noise-induced biased switching and noise-enhanced stability, respectively. In this paper, we investigate the ISR phenomenon in the monostable and bistable Hindmarsh-Rose neurons within a unified framework of large deviation theory. The critical noise strengths for both cases can be obtained by matching the timescales between noise-induced boundary crossing and the limit cycle. Furthermore, different stages of ISR are revealed by the bursting frequency distribution, where the gradual increase of the peak bursting frequency can also be explained within the same framework. The perspective and results in this paper may shed some light on the understanding of the noise-induced complex phenomena in stochastic dynamical systems.

Optimal Self-Induced Stochastic Resonance in Multiplex Neural Networks: Electrical vs. Chemical Synapses.

Electrical and chemical synapses shape the dynamics of neural networks, and their functional roles in information processing have been a longstanding question in neurobiology. In this paper, we investigate the role of synapses on the optimization of the phenomenon of self-induced stochastic resonance in a delayed multiplex neural network by using analytical and numerical methods. We consider a two-layer multiplex network in which, at the intra-layer level, neurons are coupled either by electrical synapses or by inhibitory chemical synapses. For each isolated layer, computations indicate that weaker electrical and chemical synaptic couplings are better optimizers of self-induced stochastic resonance. In addition, regardless of the synaptic strengths, shorter electrical synaptic delays are found to be better optimizers of the phenomenon than shorter chemical synaptic delays, while longer chemical synaptic delays are better optimizers than longer electrical synaptic delays; in both cases, the poorer optimizers are, in fact, worst. It is found that electrical, inhibitory, or excitatory chemical multiplexing of the two layers having only electrical synapses at the intra-layer levels can each optimize the phenomenon. Additionally, only excitatory chemical multiplexing of the two layers having only inhibitory chemical synapses at the intra-layer levels can optimize the phenomenon. These results may guide experiments aimed at establishing or confirming to the mechanism of self-induced stochastic resonance in networks of artificial neural circuits as well as in real biological neural networks.

Stochastic resonance via parametric adaptation: Experiments and numerics.

In contrast with the conventionally observed mechanism of stochastic resonance (SR) wherein the level of additive noise is systematically varied with a fixed set-point parameter, in this work we report the emergence of the SR phenomena in an electrochemical system maintaining the same level of noise and varying the parametric distance from a homoclinic bifurcation inherent to the system. The experimental system involves the corrosion of a metal disk in an acidic medium under potentiostatic conditions. The applied potential is used as a control parameter and the anodic current generated during the electrodissolution of the metal is the accessible system variable. In the presence of noise, it was observed that the system was able to enhance its output's fidelity with a weak subthreshold input signal when the set point was kept at an optimal parametric distance from the bifurcation. Numerical simulations were performed on a model for this system to corroborate the experimental observations. This type of SR may be critical in scenarios where a biological entity has control only on its sensory parameters and not on the environmental noise amplitude.

Sine-Wiener bounded noise-induced logical stochastic resonance in a two-well potential system

Abstract Some noisy nonlinear system can be used to realize reliable logic operation based on the mechanism of logical stochastic resonance (LSR). However, most previous studies focus mainly on Gaussian noise-driven system. In this paper, the effect of non-Gaussian sine-Wiener (SW) bounded noise on the reliability and agility of logic system is explored based on a SW noise-driven two-potential well system. The success probability P of obtaining desired reliable logic operation increases quickly, reaches a maximum of P = 1, and then decrease with the increase of noise intensity or self-correlation time of SW noise, showing the occurrence of LSR. Furthermore, with increasing self-correlation time of SW noise, the optimal window of noise intensity moves toward left and becomes narrower. For too long self-correlation time, SW noise cannot induce LSR. The optimal parameter regions of SW noise are strongly dependent on bias b of logic system. Therefore, adjusting bias b can realize the control of noise and let noise produce constructive effect. Additionally, the agility of reliable logic gate can be improved by properly increasing noise intensity. Taken together, the results presented here are beneficial to the design of new logic devices based on LSR.

P306 Motor cortex tRNS reduce pain and improve affective and cognitive impairment in patients with fibromyalgia: Preliminary results of a randomized sham-controlled trial

Background Fibromyalgia (FMS) is a clinical syndrome characterized by widespread musculoskeletal pain, chronic fatigue, cognitive deficit, sleep and mood disorders. Most pharmacological therapies showed limited effectiveness and there’s need for new effective and well tolerated therapeutic tools. Recently, transcranial direct-current stimulation (tDCS) of motor cortex showed able to reduce pain, while dorsolateral prefrontal cortex (DLPFC) tDCS improved anxiety, depression and cognitive impairment in FMS. A new application, random noise stimulation (tRNS), using randomly changing alternating currents, showed very recently ability to ameliorate working memory and pain in limited series of FMS and neuropathic pain patients. Here we explored the clinical effects of primary motor cortex (M1) tRNS in FMS patients. Materials and methods Twenty female patients with FMS between the ages of 26 and 67 were randomized into two treatment groups undergoing daily stimulation sessions for two weeks (weekend free): one received active, real tRNS and the other one sham, placebo tRNS. Each patient was evaluated, before and after treatment, through Visual Analogue Scale (VAS), Fibromyalgia Impact Questionnaire (FIQ), Mini-Mental State Examination (MMSE), Hospital Anxiety and Depression Scale (HADS) and other specific neurophychological tests, such as Trail Making Test (TMT), Rey Auditory Verbal Learning Test (AVLT), Forward and Backward Digit Span, FAS verbal fluency test. Results M1 active tRNS, compared to sham, induced a general improvement of FMS clinical picture: pain, depression, anxiety and FIQ scores showed significant reduction. Even TMT A, AVLT and FAS scores improved significantly. Discussion and conclusions These findings suggest M1 tRNS can be very effective in relieving symptoms of fibromyalgia. Differently from motor cortex tDCS, tRNS seems able to counteract not only pain but also cognitive disturbance in these patients. This could follow to the invoked mechanism of stochastic resonance that would bring to a synchronization of neural firing and so more spreading and lasting effects.

Analysis of Audiometric Differences of Patients with and without Tinnitus in a Large Clinical Database.

Human hearing loss (HL) and comorbidities like tinnitus pose serious problems for people’s daily life, which in most severe cases may lead to social isolation, depression, and suicide. Here, we investigate the relationship between hearing deficits and tinnitus. To this end, we conducted a retrospective study on anonymized pure tone and speech audiometric data from patients of the ENT hospital Erlangen in which we compare audiometric data between patients with and without tinnitus. Overall data from 37,661 patients with sensorineural (SHL) or conductive HL (CHL) with (T, 9.5%) or without (NT, 90.5%) a tinnitus percept in different age groups and with different tinnitus pitches were included in this study. The results of the pure tone audiometry comparisons showed significant differences in T patients compared to NT patients. In young patients, we generally found lower hearing thresholds in T compared to NT patients. In adult patients, differences were more heterogeneous: hearing thresholds in T patients were lower in low frequency ranges, while they were higher at high frequencies. Furthermore, lower thresholds were more often found in CHL patients and could rarely be detected in SHL patients. In speech audiometry, only CHL patients with high-pitched tinnitus showed lower thresholds compared to NT patients’ thresholds. The results of this study may point to a biologically plausible functional benefit on hearing thresholds in HL tinnitus patients. We hypothesize that the physiological mechanism of stochastic resonance counteracts HL by adding neuronal noise to the system. This neuronal noise may induce changes in the auditory pathway and finally—as a side effect of threshold improvement—lead to the development of a tinnitus percept. We propose a general model of changed hearing thresholds in T patients, being either decreased or increased compared to NT patients.

Comparison of Bistable Systems and Matched Filters in Non-Gaussian Noise

In this paper, we report that for a weak signal buried in the heavy-tailed noise, the bistable system can outperform the matched filter, yielding a higher output signal-to-noise ratio (SNR) or a lower probability of error. Moreover, by adding mutually independent internal noise components to an array of bistable systems, the output SNR or the probability of error can be further improved via the mechanism of stochastic resonance (SR). These comparison results demonstrate the potential capability of bistable systems for detecting weak signals in non-Gaussian noise environments.

Stochastic resonance in multi-scale bistable array

Abstract This Letter explores a new mechanism of stochastic resonance (SR) that is induced by the multi-scale noise decomposed from the input signal, which is promising in signal detection and processing under heavy background noise. The input signal is firstly decomposed to multi-scale signals by orthogonal wavelet transform. Then, the approximate signal, which contains the driving signal, is processed by an uncoupled parallel bistable array with the detailed signal of each scale as the internal noise. At last, a SR mechanism combining the effects of colored noise and array SR is proposed. The simulation results show that a high quality output signal can be obtained by the new mechanism. The proposed model is more adaptive to input signal with high noise intensity than single bistable SR system, which can be seen from the signal-to-noise ratio curves and average noise intensity curves.

Mechanism of Stochastic Resonance in a Quorum Sensing Network Regulated by Small RNAs

Bacterial quorum sensing (QS) is an important process of cell communication and more and more attention is paid to it. Moreover, the noises are ubiquitous in nature and often play positive role. In this paper, we investigate how the noise enhances the QS though the stochastic resonance (SR) and explain the mechanism of SR in this quorum sensing network. In addition, we also discuss the interaction between the small RNA and the other genes in this network and discover the biological importance.

Overdamped fractional Langevin equation and its stochastic resonance

By choosing an appropriate damping kernel function of generalized Langevin equation, fractional Langevin equation (FLE) is derived in the case of overdamped condition. With the theory of anomalous diffusion and the memory of fractional derivatives, the physical meaning of FLE is discussed. Moreover, the internal mechanism of stochastic resonance about FLE is obtained. Finally, the numerical simulation shows that in a certain range of the order, stochastic resonance appears in FLE, and it is evident that the SNR gain in fractional Langevin equation is better than that of the integer-order situation.

Energetics of stochastic resonance

In this paper, we discuss the motion of a Brownian particle in a double-well potential driven by a periodic force in terms of energies delivered by the periodic and the noise forces and energy dissipated into the viscous environment. It is shown that, while the power delivered by the periodic force to the Brownian particle is controlled by the strength of the noise, the power delivered by the noise itself is independent of the amplitude and frequency of the periodic force. The implications of this result for the mechanism of stochastic resonance in an equilibrium system is that it is not energy from the noise force which enhances a small periodic force, but rather an increase of energy delivered by the periodic force, regulated by the strength of the noise. We further re-evaluate the frequency dependence of stochastic resonance in terms of energetic terms including efficiency.

Stochastic resonance modulates neural synchronization within and between cortical sources.

Neural synchronization is a mechanism whereby functionally specific brain regions establish transient networks for perception, cognition, and action. Direct addition of weak noise (fast random fluctuations) to various neural systems enhances synchronization through the mechanism of stochastic resonance (SR). Moreover, SR also occurs in human perception, cognition, and action. Perception, cognition, and action are closely correlated with, and may depend upon, synchronized oscillations within specialized brain networks. We tested the hypothesis that SR-mediated neural synchronization occurs within and between functionally relevant brain areas and thus could be responsible for behavioral SR. We measured the 40-Hz transient response of the human auditory cortex to brief pure tones. This response arises when the ongoing, random-phase, 40-Hz activity of a group of tuned neurons in the auditory cortex becomes synchronized in response to the onset of an above-threshold sound at its “preferred” frequency. We presented a stream of near-threshold standard sounds in various levels of added broadband noise and measured subjects' 40-Hz response to the standards in a deviant-detection paradigm using high-density EEG. We used independent component analysis and dipole fitting to locate neural sources of the 40-Hz response in bilateral auditory cortex, left posterior cingulate cortex and left superior frontal gyrus. We found that added noise enhanced the 40-Hz response in all these areas. Moreover, added noise also increased the synchronization between these regions in alpha and gamma frequency bands both during and after the 40-Hz response. Our results demonstrate neural SR in several functionally specific brain regions, including areas not traditionally thought to contribute to the auditory 40-Hz transient response. In addition, we demonstrated SR in the synchronization between these brain regions. Thus, both intra- and inter-regional synchronization of neural activity are facilitated by the addition of moderate amounts of random noise. Because the noise levels in the brain fluctuate with arousal system activity, particularly across sleep-wake cycles, optimal neural noise levels, and thus SR, could be involved in optimizing the formation of task-relevant brain networks at several scales under normal conditions.

Stochastic resonance: from climate to biology

Abstract. In this lecture, I will review some basic aspects of the mechanism of stochastic resonance, first introduced as a possible mechanism to explain long term climatic variation. Since then, there have been many applications of stochastic resonance in physical and biological systems. I will show that in complex system, stochastic resonance can substantially change as a function of the "system complexity". Also, I will briefly mention how to apply stochastic resonance for the case of Brownian motors.

Stochastic resonance–a nonlinear control theory interpretation

Stochastic resonance (SR) is an effect that has been known (Benzi, R., Sutera, A., and Vulpiani, A. (1981), ‘The Mechanism of Stochastic Resonance’, Journal of Physics, A14, L453–L457) for almost three decades and has been extensively studied in biology, statistics, signal processing and in numerous other eclectic areas (Wiesenfeld, K., and Moss, F. (1995), ‘Stochastic Resonance and the Benefits of Noise: From Ice Ages to Crayfish and Squids’, Nature, 373, 33–36). Herein, a nonlinear control theory analysis is conducted on how to better understand the class of systems that may exhibit the SR effect. Using nonlinear control theory methods, equilibrium points are manipulated to create the SR response (similar to shaping dynamical response in a phase plane). From this approach, a means of synthesising and designing the appropriate class of nonlinear systems is introduced. New types of nonlinear dynamics that demonstrate the SR effects are discovered, which may have utility in control theory as well as in many diverse applications. A numerical simulation illustrates some powerful attributes of these systems.