Electrical Noise Research Articles

A fast Markovian method for modeling channel noise in neurons

Channel noise results from rapid transitions of protein channels from closed to open state and is generally considered as the most dominant source of electrical noise causing membrane-potential fluctuations even in the absence of synaptic inputs. The simulation of a realistic channel noise remains a source of possible error. Although the Markovian method is considered as the golden standard for appropriate description of channel noise, its computation time increasing exponentially with the number of channels, it is poorly suitable to simulate realistic features. We describe here a novel algorithm at discrete time unit for simulating ion channel noise based on Markov chains (MC). Although this new algorithm refers to a Monte-Carlo process, it only needs few random numbers whatever the number of channels involved. Our fast MC (FMC) model does not exhibit the drawbacks due to approximations based on stochastic differential equations and the values of spike jitter are comparable to those obtained with the true Markovian method. In fact, we show here, that these drawbacks can be highlighted in the approximation based on stochastic differential equation methods even for a high number of channels (standard deviation of the 5th spike is about two-fold larger than that of MCF or true Markovian method for 5000 sodium channels). The FMC model appears therefore as the most accurate method to simulate channel noise with a fast execution time that does not depend on the channel number.

PDZD8-mediated endoplasmic reticulum–mitochondria associations regulate sympathetic drive and blood pressure through the intervention of neuronal mitochondrial homeostasis in stress-induced hypertension

Neuronal hyperexcitation in the rostral ventrolateral medulla (RVLM) drives heightened sympathetic nerve activity and contributes to the etiology of stress-induced hypertension (SIH). Maintenance of mitochondrial functions is central to neuronal homeostasis. PDZD8, an endoplasmic reticulum (ER) transmembrane protein, tethers ER to mitochondria. However, the mechanisms of PDZD8-mediated ER–mitochondria associations regulating neuronal mitochondrial functions and thereby mediating blood pressure (BP) in the RVLM of SIH were largely unknown. SIH rats were subjected to intermittent electric foot shocks plus noise for 2 h twice daily for 15 consecutive days. The underlying mechanisms of PDZD8 were investigated through in vitro experiments by using small interfering RNA and through in vivo experiments, such as intra-RVLM microinjection and Western blot analysis. The function of PDZD8 on BP regulation in the RVLM was determined in vivo via the intra-RVLM microinjection of adeno-associated virus (AAV)2-r-Pdzd8. We found that the c-Fos-positive RVLM tyrosine hydroxylase (TH) neurons, renal sympathetic nerve activity (RSNA), plasma norepinephrine (NE) level, BP, and heart rate (HR) were elevated in SIH rats. ER–mitochondria associations in RVLM neurons were significantly reduced in SIH rats. PDZD8 was mainly expressed in RVLM neurons, and mRNA and protein levels were markedly decreased in SIH rats. In N2a cells, PDZD8 knockdown disrupted ER–mitochondria associations and mitochondrial structure, decreased mitochondrial membrane potential (MMP) and respiratory metabolism, enhanced ROS levels, and reduced catalase (CAT) activity. These effects suggested that PDZD8 dysregulation induced mitochondrial malfunction. By contrast, PDZD8 upregulation in the RVLM of SIH rats could rescue neuronal mitochondrial function, thereby suppressing c-Fos expression in TH neurons and decreasing RSNA, plasma NE, BP, and HR. Our results indicated that the dysregulation of PDZD8-mediated ER–mitochondria associations led to the loss of the activity homeostasis of RVLM neurons by disrupting mitochondrial functions, thereby participating in the regulation of SIH pathology.

Considering Electric Measurements for NVH - Voltage, Current, and Torque Ripple

The study of electronics, motors and mechanical noise and vibration have often been treated as separate fields in R&D, Production, and test/validation. In electric powertrains, the electric and mechanical must be designed and tested in parallel to really understand the machine operation. This paper will highlight an existing link between the electrical and mechanical noise in electric powertrains and what measurement/analysis tools are available for performing an NVH analysis including electrical measurements.

Fast NVH Prediction for E-Motor Noise and Validation

Fast NVH analysis from the start of the motor design is important to speed up product development by avoiding major NVH issues later. In this paper, different methods for electric motor noise and vibration predictions are proposed for different stages in the design process. The results are compared to previous experimental validation [1]. The first method combines electromagnetic finite element analysis to calculate the forces, with analytical models for the stator vibration and acoustic response [2]. This requires minimal structural design data, and can be carried out rapidly, allowing the exploration of many design possibilities in the early design stage, and is implemented in Ansys Motor-CAD. This is compared with the full structural finite element analysis (FEA) [1]. It is shown that the fast analytical NVH process provides a good prediction for the motor noise level early in the design process. The structural FEA approach provides additional information about the structural response at a later stage to refine the design. The use of frequency response functions (FRF) derived from the structural FEA are demonstrated, allowing the rapid calculation of noise results from the electromagnetic forces.

Robustness of electrocardiographic imaging in the presence of electrical noise

Abstract Funding Acknowledgements Type of funding sources: Other. Main funding source(s): Ministerio de Ciencia, Innovación y Universidades; Generalitat Valenciana Background Electrocardiographic Imaging (ECGI) allows evaluating the cardiac substrate non-invasively. However, the inverse solution is strongly dependent on the noise of the body surface signals. Zero-order Tikhonov regularization together with L-curve optimization, widely used in ECGI studies, allows minimizing the sources of noise to find an optimal solution. Objective Evaluate the effect of electrical and noise on the L-curve optimization in computer simulations of atrial activity and in the physiological interpretation of the inverse solution. Methods Five atrial simulations of regular rhythms (3 atrial flutter and 2 sinus rhythm) were used for computing the ECGI with five signal-to-noise ratios (SNR) (from 3dB to 40dB). ECGI was computed by zero-order Tikhonov regularization and L-curve optimization. The regularization parameter (λ) and the shape of the curve were analysed. Electrical noise was added to signals from an atrial flutter patient to study the effect on real ECGI signals. Local activation time (LAT) maps were obtained to evaluate the quality of the ECGI in front of noise. Correlation coefficient (CC) of LAT maps was computed between simulated electrograms and ECGIs. Results The level of noise was found to be correlated with the optimal regularization parameter (λ), (SNR 3dB, λ>10-6, SNR 40dB, λ<10-9). LAT maps for the optimal λ and higher were able to find relevant information about the simulation and patient independently on the SNR (CC3dB-EGM = 0.66±0.1, CC40dB-EGM = 0.76±0.1). Similar behaviour was observed in real atrial flutter signals (CC3dB-Ref = 0.83, CC10dB-Ref = 0.93). LAT maps from the atrial flutter were able to accurately locate the reentry circuit, as found in the electroanatomical map independently on the SNR. Conclusion LAT maps derived from ECGI are robust against the presence of noise. Although the reconstructed potentials are smoother with increasing levels of noise, LAT maps with and without noise show a good correlation thanks to an increased regularization. Figure 1. A. Local activation times (LAT) map of a reference electrogram (EGM) from a computer simulation of sinus rhythm in the atria. B. Electrocardiographic imaging maps (ECGI) obtained from the simulation A for different levels of electrical noise and regularization parameters. C. Example of LAT map obtained with ECGI and the endocardial map of a typical atrial flutter together with LAT maps with increased electrical noise but comparable results (CC > 0.83).

Autocapture algorithm behaviour in left bundle branch pacing

Abstract Funding Acknowledgements Type of funding sources: None. Introduction Autocapture algorithms allow remote monitoring of patients, reducing the need for face-to-face hospital consultations and increasing patient safety. These algorithms are widely validated in patients with conventional pacing and are not recommended in His bundle pacing, but there is no information on its behaviour in left bundle branch pacing (LBBP). Purpose The objective was to analyse the autocapture algorithm behaviour in LBBP and its differences with conventional pacing and manual thresholds during device first programming (acute phase), after 1-7 days (subacute phase) and 1-3 months later (chronic phase). Methods Prospective non-randomised single-centre comparative study. Consecutive patients with an indication for cardiac pacing were included. The implant was performed in the left bundle branch area or in the right ventricle endocardium (outflow tract or apex) at the discretion of the operator. LBBP was considered according to published criteria. Autocapture algorithm was activated in both groups whenever allowed by the device. The thresholds manual measurement was performed with the intracavitary electrogram and the surface electrocardiogram. Results Sixty patients were included, 40 with LBBP and 20 with conventional pacing. Baseline characteristics of patients are shown in Figure 1. The feasibility of the autocapture algorithm was more frequent in late phases, with a favourable trend toward bipolar pacing (Figure 2A). The failures in the activation of the algorithm were mainly due to the absence of adequate safety margins (90.9% in conventional pacing and 86.6% in LBBP), the rest were attributable to atrial tachyarrhythmias (9.1 and 6.7%, respectively) and electrical noise (remaining 6.7% in LBBP). In LBBP group, there were not statistically significant differences between manual and automatic thresholds except in subacute phase (unipolar) (Figure 2B), and both remained stable during follow-up (average increase of 0.46V). Conclusions Autocapture algorithm is feasible in LBBP, with a favourable trend toward bipolar pacing. Automatic thresholds are similar to manual in patients with LBBP, and they remain stable during follow-up.

The Role of Electrolytes in the Relaxation of Near-Surface Spin Defects in Diamond.

Quantum sensing with spin defects in diamond, such as the nitrogen vacancy (NV) center, enables the detection of various chemical species on the nanoscale. Molecules or ions with unpaired electronic spins are typically probed by their influence on the NV center's spin relaxation. Whereas it is well-known that paramagnetic ions reduce the NV center's relaxation time (T1), here we report on the opposite effect for diamagnetic ions. We demonstrate that millimolar concentrations of aqueous diamagnetic electrolyte solutions increase the T1 time of near-surface NV center ensembles compared to pure water. To elucidate the underlying mechanism of this surprising effect, single and double quantum NV experiments are performed, which indicate a reduction of magnetic and electric noise in the presence of diamagnetic electrolytes. In combination with ab initio simulations, we propose that a change in the interfacial band bending due to the formation of an electric double layer leads to a stabilization of fluctuating charges at the interface of an oxidized diamond. This work not only helps to understand noise sources in quantum systems but could also broaden the application space of quantum sensors toward electrolyte sensing in cell biology, neuroscience, and electrochemistry.

Deep diffusion models for seismic processing

Seismic data processing involves techniques to deal with undesired effects that occur during acquisition and pre-processing. These effects mainly comprise coherent artefacts such as multiples, non-coherent signals such as electrical noise, and loss of signal information at the receivers that leads to incomplete traces. In the past years, there has been a remarkable increase of machine-learning-based solutions that have addressed the aforementioned issues. In particular, deep-learning practitioners have usually relied on heavily fine-tuned, customized discriminative algorithms. Although, these methods can provide solid results, they seem to lack semantic understanding of the provided data. Motivated by this limitation, in this work, we employ a generative solution, as it can explicitly model complex data distributions and hence, yield to a better decision-making process. In particular, we introduce diffusion models for three seismic applications: demultiple, denoising and interpolation. To that end, we run experiments on synthetic and on real data, and we compare the diffusion performance with standardized algorithms. We believe that our pioneer study not only demonstrates the capability of diffusion models, but also opens the door to future research to integrate generative models in seismic workflows.

Electromagnetic noise analysis of servomotor used in secondary water supply equipment

The servo motor has the characteristics of high precision, high speed and stable operation, but it will produce large electromagnetic noise in the process of using, which will affect the user experience. Based on the ANSYS multi-physics finite element analysis platform, this paper studies the electrical noise characteristics of servo motors used in a secondary water supply equipment under different speed conditions. The time and space distribution characteristics of the radial electromagnetic force acting on the motor stator are obtained by electromagnetic field analysis and modal analysis respectively. The natural frequency of the motor structure modal mode can also be obtained from these analyses. The causes of large electromagnetic noise were analyzed from the aspects of radial electromagnetic force and structural mode, and the position distribution of large vibration on the motor is obtained by harmonic response analysis. On this basis, the sound pressure level distribution of servo electromagnetic noise at different frequencies is obtained through acoustic simulation, which has referential significance for noise prediction and shell noise reduction optimization of related servo motors.

An Optical Technique to Produce Embedded Quantum Structures in Semiconductors.

The performance of a semiconductor quantum-electronic device ultimately depends on the quality of the semiconductor materials it is made of and on how well the device is isolated from electrostatic fluctuations caused by unavoidable surface charges and other sources of electric noise. Current technology to fabricate quantum semiconductor devices relies on surface gates which impose strong limitations on the maximum distance from the surface where the confining electrostatic potentials can be engineered. Surface gates also introduce strain fields which cause imperfections in the semiconductor crystal structure. Another way to create confining electrostatic potentials inside semiconductors is by means of light and photosensitive dopants. Light can be structured in the form of perfectly parallel sheets of high and low intensity which can penetrate deep into a semiconductor and, importantly, light does not deteriorate the quality of the semiconductor crystal. In this work, we employ these important properties of structured light to form metastable states of photo-sensitive impurities inside a GaAs/AlGaAs quantum well structure in order to create persistent periodic electrostatic potentials at large predetermined distances from the sample surface. The amplitude of the light-induced potential is controlled by gradually increasing the light fluence at the sample surface and simultaneously measuring the amplitude of Weiss commensurability oscillations in the magnetoresistivity.

Improved African Buffalo Optimization-Based Takagi–Sugeno–Kang Fuzzy PI Controller for Speed Control in BLDC Motor

The aim of this article is to design an improved African buffalo optimization-based Takagi–Sugeno–Kang Fuzzy PI Controller (IABO-TSKFPIC) Model for brushless direct current motors to achieve better and more reliable speed control under various load and speed conditions. Due to the nonlinearity properties of BLDC motor drives, such as extended power fluctuation, settling time, fluctuation of steady-state error, and other effects, controllability is poor. To overcome these issues, the proposed model uses an improved Takagi–Sugeno–Kang fuzzy PI controller that incorporates the IABO algorithm in a self-tuned regulator structure to minimize the supplied objective function and meet inequality requirements. The use of Hall sensors in speed control is restricted due to component failure, low dependability, the need for specialized mechanical mounting solutions, and electrical noise issues. The performance of the IABO-TSKFPIC Model is evaluated in terms of rise time, peak overshoot, settling time, and steady-state error. Simulation results show that the suggested IABO-tuned TSK Fuzzy PI controller outperforms traditional and metaheuristic algorithms in terms of time-domain characteristics, control effort, and performance indices. The IABO-TSKFPIC Model improves the performance of the optimized fuzzy PI controller with greater output voltage compared to state-of-the-art studies.

PO-04-136 VISUALIZATION OF LOW AMPLITUDE ELECTROGRAMS WITH A NOVEL RECORDING SYSTEM IN THE ELECTROPHYSIOLOGY LABORATORY

Electrical noise, particularly power line noise at 50-60 Hz, is frequently generated by electrical equipment present in the electrophysiology (EP) laboratory. High frequency, low amplitude intracardiac unipolar and bipolar electrograms (EGMs) are particularly susceptible to distortion from this type of noise. The aim of this study was to evaluate a novel EP recording system developed to address this issue. To measure baseline noise and the smallest identifiable EGMs in a range of procedures. We enrolled patients undergoing catheter ablation of atrial flutter, atrial fibrillation (de novo as well as redo), SVT and conduction system pacemaker implantation. The peak-to-peak noise and intracardiac unipolar and bipolar signal amplitude was measured manually, initially side-by-side using the LabSystem (LS) Pro (Boston Scientific) and ECGenius System (CathVision) systems and then ECGenius System alone (Figure). EGMs were chosen to represent the lowest peak to peak amplitude of signals where information was of interest. A total of 37 consecutive patients were enrolled. The amplitude of the baseline noise measured on the LSPro system using standard filtering including a 60Hz notch filter was > 0.040 mV bipolar and > 0.100 mV unipolar. In contrast, on the ECGenius System the measured peak to peak baseline noise amplitude had a mean value of 0.010 ± 0.004 mV and 0.014 ± 0.018 mV for bipolar and unipolar EGMs, respectively using standard filter settings (Table). A mean noise level of 0.011 mV ± 0.005 was measured through a pacing lead allowing signals as low as 0.023 mV to be observed. The ability to visualize low amplitude intracardiac EGMs is essential during EP procedures. However, EGMs, especially unipolar EGMs, can be obscured by noise, most commonly due to power line interference. We demonstrate that a novel EP recording system had a mean noise level of only 0.010 mV with a low-end of 0.004 mV for bipolar signals. This allowed identification of intracardiac EGMs with a peak-to-peak voltage as low as 8 μV. These characteristics offer the possibility of improving outcomes during EP procedures and may also facilitate physiologic pacing through the identification of small His and bundle branch potentials.Tabled 1ECGenius measurements of baseline noise and lowest clinical EGM amplitude observed by catheter and trace typeBaselineLowest ClinicalminmeanStd.Dev.minmeanSt.Dev.Catheter TypeOrion0.0050.0130.0140.0270.0560.085Pentarray0.0040.0090.0050.0080.0290.035Ablation0.0050.0110.0030.0230.0370.015Pacemaker Lead0.0080.0110.0050.0230.0950.149EGM TypeUnipolar0.0080.0140.0180.0350.0900.138EGM TypeBipolar0.0040.0100.0040.0080.0330.026 Open table in a new tab

Probing hidden conduction mechanisms in diced silicon solar cells by low frequency noise analysis

Electrical low frequency noise (LFN) measurements are utilized in this work, in order to identify parasitic low voltage transport mechanisms in diced aluminum back surface field (Al-BSF) photovoltaic (PV) cells, which cannot be identified by I–V characterization. A detailed method of LFN analysis is proposed, taking into account the co-existence of various transport mechanisms, that typically occur in parallel in such devices. A precise examination of the 1/f noise level dependency with both current and voltage, combined with static I–V characterizations at various temperatures, enabled the identification of two flicker noise sources appearing at different current regions: one attributed to local linear shunt mechanisms and a second one to a fluctuation in a nonlinear parasitic conductivity localized at the samples’ edges. The latter source exhibits noise signatures resembling either tunneling or Poole-Frenkel transport, while excluding Shockley-Read-Hall recombination processes. This hypothesis was further verified by measurements of thermal activation energies typical of tunnel and Poole-Frenkel conduction mechanisms.

Effect of bias conditions on transient anode current for quasi-double-sided silicon drift detector with high energy resolution

Silicon drift detectors (SDDs) are widely applied for x-ray detection due to their remarkable energy resolution. The main contribution to energy resolution is the electrical noise of the measurement system. In addition, imperfect charge collection and ballistic deficits also deteriorate the energy resolution, which is closely related to bias conditions. However, bias condition's effect on energy resolution has been rarely reported. To investigate its effect on energy resolution, we proposed a quasi-double-sided silicon drift detector (QD-SDD) with a drift ring structure designed on the back side. QD-SDDs was simulated and verified to have more flexibility in bias conditions, achieving better electron collection efficiency and shorter collection time at higher bias voltages. Finally, the fabricated QD-SDDs were characterized using a55Fe radioactive source. The experimental results show that the energy resolution of the QD-SDD varies with different bias conditions. The optimal energy resolution is 170 eV under the optimized bias conditions.

Convolutional Deep Neural Network and Full Connectivity for Speech Enhancement

The speech signal that is received in real-time has background noise and reverberations, which have an impact on the quality of speech. Therefore, it is crucial to reduce or eliminate the noise and increase the intelligibility and quality of speech signals. In this study, a proposed method that is the most effective and challenging in a low SNR environment for three types of noise are removed, including washing machine, traffic noise, and electric fan noise, and clean speech is recovered. with three samples of noise which are mixed and added to the clean speech signal with a lower level of SNR value fixed at (-5, 0, 5) dBs, that noise source takes equal weights. The enhancement of the corrupted speech signal is done by applying a fully connected and convolutional neural network-based denoising algorithm and comparing their performance. The proposed network shows that a fully connected network (FCN) has less elapsed time than a convolutional network (CNN) while still achieving better performance, demonstrating its applicability for an embedded system. Also, the results obtained show that, overall, the CNN is better than the FCN regarding maximum coloration, PSNR, MES, and STOI.

Investigation of topological regime in Bi2Se3 thin films through low-frequency electric noise

Topological insulators are considered new states of quantum matter that cannot be systematically related to conventional insulators and semiconductors. Among them, Bi2Se3 has attracted an increasing interest due to a simple surface band structure and due to a strong contribution of the surface to transport. While the dc electric transport properties have been extensively studied, intrinsic fluctuations and their effect on the surface conduction have received less attention. In order to better investigate these aspects, a detailed characterization of the low-frequency noise, also known as noise spectroscopy, has been made in Bi2Se3 thin films. The experimental results have been obtained for different samples thickness and geometry, in a temperature range from 300 down to 8 K, and as a function of dc bias current and gate voltage. While the observed spectral noise shows a typical thermal and shot noise part, an unusual reduction of the 1/f noise component is found, especially in the low-temperature region. A correlation of this behavior with structural and dc electric transport investigations suggests that it could be an indication of the occurrence of the topological regime. Flicker noise measurements, therefore, could be considered as a valid alternative technique to standard topological surface state spectroscopy.

Proposal of a Bioinspired Patch for the Real-time Monitoring of Neurological Trauma

Neurological trauma, damage to the brain, spine, or nerves, can happen as a result of both neurosurgery and vehicle accidents. It may be risky to cause damage during surgery because of humans' ineffective capacity to repair neurons; as a result, the market may require a medical device specifically made to address this problem. The accessory limb model (ALM), one of the most typical regeneration models, has demonstrated that it has significant potential to improve regeneration outcomes and might be used for the regeneration of human brain tissues. Nowadays, many implanted medical devices interact with circuitry outside the body via cables or wireless RF telemetry. However, wires usually result in a huge chance to increase surgical difficulties such as wire breakage, infection, and electrical noise. Intrabody communication, as a result, became a preferable and safer way of communication in the design of implantable medical devices, and is used in the device introduced in this article.

Modeling the Variability of Au/Ti/h-BN/Au Memristive Devices

The variability of memristive devices using multilayer hexagonal boron nitride (h-BN) coupled with Ti and Au electrodes (i.e., Au/Ti/h-BN/Au) is analyzed in depth using different numerical techniques. We extract the reset voltage using three different methods, quantify its cycle-to-cycle variability, calculate the charge and flux that allows to minimize the effects of electric noise and the inherent stochasticity of resistive switching, describe the device variability using time series analyses to assess the “memory” effect, and employ a circuit breaker simulator to understand the formation and rupture of the percolation paths that produce the switching. We conclude that the cycle-to-cycle variability of the Au/Ti/h-BN/Au devices presented here is higher than that previously observed in Au/h-BN/Au devices, and hence, they may be useful for data encryption.

SK and Kv4 Channels Limit Spike Timing Perturbations in Pacemaking Dopamine Neurons.

Midbrain dopamine (DA) neurons are among the best characterized pacemaker neurons, having intrinsic, rhythmic firing activity even in the absence of synaptic input. However, the mechanisms of DA neuron pacemaking have not been systematically related to how these cells respond to synaptic input. The input-output properties of pacemaking neurons can be characterized by the phase-resetting curve (PRC), which describes the sensitivity of interspike interval (ISI) length to inputs arriving at different phases of the firing cycle. Here we determined PRCs of putative DA neurons in the substantia nigra pars compacta in brain slices from male and female mice using gramicidin-perforated current-clamp recordings with electrical noise stimuli applied through the patch pipette. On average, and compared with nearby putative GABA neurons, DA neurons showed a low, nearly constant level of sensitivity across most of the ISI, but individual cells had PRCs showing relatively greater sensitivity at early or late phases. Pharmacological experiments showed that DA neuron PRCs are shaped by small-conductance calcium-activated potassium and Kv4 channels, which limit input sensitivity across early and late phases of the ISI. Our results establish the PRC as a tractable experimental measurement of individual DA neuron input-output relationships and identify two of the major ionic conductances that limit perturbations to rhythmic firing. These findings have applications in modeling and for identifying biophysical changes in response to disease or environmental manipulations.

Noise in the brain: Transcranial random noise stimulation and perceptual noise act on a stochastic resonance-like mechanism.

Stochastic resonance (SR) is a phenomenon in which a certain amount of random noise added to a weak subthreshold stimulus can enhance signal detectability. It is unknown how external noise interacts with neural noise in producing an SR-like phenomenon and whether this interaction results in a modulation of either network efficiency or the efficiency of single neurons. Using random dot motion stimuli and noninvasive brain stimulation, we attempted to unveil the specific mechanism of action of the SR-like phenomenon in motion perception, if present. We aimed to determine whether signal integration efficiency changes with external noise (random dot numerosity) and how electrical transcranial random noise stimulation (tRNS) can affect the peak performance. The participants performed a coherent motion detection task in which the random dot numerosity varied, whereas the signal-to-noise ratio (SNR) remained constant. We applied placebo or tRNS with an amplitude of either 1 or 2mA during task execution. We found peaks in participants' performance both in the case of placebo stimulation and in the case of 1-mA tRNS. In the latter case (i.e., with an additional noise source), the peak emerged at lower random dot numerosity levels than when no additional noise was added (placebo). No clear peak was observed with 2-mA tRNS. An equivalent noise (EN) analysis confirmed that SR arises from a modulation of the network efficiency underlying motion signal integration. These results indicate a joint contribution of external and neural noise (modulated by tRNS) in eliciting an SR-like phenomenon.