Neural Noise Research Articles

Increased scale-free and aperiodic neural activity during sensorimotor integration-a novel facet in Tourette syndrome.

Tourette syndrome is a common neurodevelopmental disorder defined by multiple motor and phonic tics. Tics in Tourette syndrome resemble spontaneously occurring movements in healthy controls and are therefore sometimes difficult to distinguish from these. Tics may in fact be mis-interpreted as a meaningful action, i.e. a signal with social content, whereas they lack such information and could be conceived a surplus of action or ‘motor noise’. These and other considerations have led to a ‘neural noise account’ of Tourette syndrome suggesting that the processing of neural noise and adaptation of the signal-to-noise ratio during information processing is relevant for the understanding of Tourette syndrome. So far, there is no direct evidence for this. Here, we tested the ‘neural noise account’ examining 1/f noise, also called scale-free neural activity as well as aperiodic activity, in n = 74 children, adolescents and adults with Tourette syndrome and n = 74 healthy controls during task performance using EEG data recorded during a sensorimotor integration task. In keeping with results of a previous study in adults with Tourette syndrome, behavioural data confirmed that sensorimotor integration was also stronger in this larger Tourette syndrome cohort underscoring the relevance of perceptual-action processes in this disorder. More importantly, we show that 1/f noise and aperiodic activity during sensorimotor processing is increased in patients with Tourette syndrome supporting the ‘neural noise account’. This implies that asynchronous/aperiodic neural activity during sensorimotor integration is stronger in patients with Tourette syndrome compared to healthy controls, which is probably related to abnormalities of GABAergic and dopaminergic transmission in these patients. Differences in 1/f noise and aperiodic activity between patients with Tourette syndrome and healthy controls were driven by high-frequency oscillations and not lower-frequency activity currently discussed to be important in the pathophysiology of tics. This and the fact that Bayesian statistics showed that there is evidence for the absence of a correlation between neural noise and clinical measures of tics, suggest that increased 1/f noise and aperiodic activity are not directly related to tics but rather represents a novel facet of Tourette syndrome.

Capsaicin burn increases thickness discrimination thresholds independently of chronic chili intake

The trigeminal nerve transduces both chemical irritation and textural sensations suggesting that perception in one may influence perception in the other. Little is known about how the oral burn of capsaicin may affect texture sensitivity. The aim of this study was to determine the effect of burning sensations on thickness discrimination thresholds in liquid foods assessed by consumers who vary in habitual spicy food intake. Forty-seven Caucasian participants (31 females and 16 males; mean age: 25.0 ± 5.7 yrs; mean BMI: 21.5 ± 2.6 kg/m2) were recruited in the Netherlands. Chili pepper intake frequency and preference for chili peppers and spicy foods were assessed using questionnaires. Perceived burn and disliking/liking of bouillon soups thickened with xanthan gum (concentrations ranging from 0.06 to 0.21 g/mL; viscosity at 50 s−1 (η50s-1) ranging from 11 to 48 mPas) containing varying amounts of capsaicin (0, 1, or 10 ppm) were determined using generalized scales (gLMS and gDOL). Estimates of thickness discrimination thresholds were determined using the 2-Alternative Forced Choice ascending staircase method. Capsaicin was applied in two ways: (i) capsaicin was added directly to the soups or (ii) a pre-rinse of a capsaicin solution was held in mouth before evaluating soups without capsaicin. As expected, frequent chili pepper consumers reported significantly lower burn intensity and higher hedonic ratings compared to infrequent consumers. Thickness discrimination thresholds (i.e., BET expressed as Δη50s-1) increased significantly from 11.3 mPas at 0 ppm to 16.1 mPas at 1 ppm (42% increase) to 21.4 mPas at 10 ppm capsaicin (89% increase) on average across all participants. Similar modification of thickness discrimination thresholds were observed regardless of whether capsaicin was added to the soup or was applied as a pre-rinse. No significant differences in thickness discrimination thresholds were observed between infrequent and frequent chili consumers. We conclude that oral burn caused by capsaicin affects thickness discrimination independently of reported chili pepper intake. Also, we suggest the ability of capsaicin to alter thickness discrimination may be due to increased neural noise, attentional effects or cross-modal interactions.

A New Neuron Ion Channel Model Under Time Varying Input Currents

In recent years, it has been argued and shown experimentally that ion channel noise in neurons can cause fundamental effects on the neuron’s dynamical behavior. Most profoundly, ion channel noise was seen to be able to cause spontaneous firing and stochastic resonance. However, Hodgkin-Huxley model affected when inserting some colored noise terms inside the conductance’s, where those effects captured by colored noise due to the gate multiplicity. This paper presents a new ion channel model under time varying periodic input currents. It firstly introduced the effect of without, with (colored noise), on the proposed model and the comparison of ion channel based on HH, Fox- Lu, and Linaro models. Additionally, in order to overcome the limitations of other parameter estimation methods, the proposed method fully constraints their models and obtains all models capabilities of reproducing the data. Finally, the relationship between the sequence of colored noise and the spike frequency are simulated efficiently each gate compared with microscopic simulations of the stochastic Markov process method. The simulation results revealed that above a critical value of the input frequency and also below a certain amplitude value, the colored terms play a very prominent role on the firing statistics. In addition, the spiking rate generated from the proposed model very close to microscopic simulations and doesn’t effect by the membrane size.

The unbiased estimation of the fraction of variance explained by a model.

The correlation coefficient squared, r2, is commonly used to validate quantitative models on neural data, yet it is biased by trial-to-trial variability: as trial-to-trial variability increases, measured correlation to a model's predictions decreases. As a result, models that perfectly explain neural tuning can appear to perform poorly. Many solutions to this problem have been proposed, but no consensus has been reached on which is the least biased estimator. Some currently used methods substantially overestimate model fit, and the utility of even the best performing methods is limited by the lack of confidence intervals and asymptotic analysis. We provide a new estimator, [Formula: see text], that outperforms all prior estimators in our testing, and we provide confidence intervals and asymptotic guarantees. We apply our estimator to a variety of neural data to validate its utility. We find that neural noise is often so great that confidence intervals of the estimator cover the entire possible range of values ([0, 1]), preventing meaningful evaluation of the quality of a model's predictions. This leads us to propose the use of the signal-to-noise ratio (SNR) as a quality metric for making quantitative comparisons across neural recordings. Analyzing a variety of neural data sets, we find that up to ∼ 40% of some state-of-the-art neural recordings do not pass even a liberal SNR criterion. Moving toward more reliable estimates of correlation, and quantitatively comparing quality across recording modalities and data sets, will be critical to accelerating progress in modeling biological phenomena.

Between Action and Emotional Survival During the COVID-19 era: Sensorimotor Pathways as Control Systems of Transdiagnostic Anxiety-Related Intolerance to Uncertainty.

Objectives: The COVID-19 pandemic and aligned social and physical distancing regulations increase the sense of uncertainty, intensifying the risk for psychopathology globally. Anxiety disorders are associated with intolerance to uncertainty. In this review we describe brain circuits and sensorimotor pathways involved in human reactions to uncertainty. We present the healthy mode of coping with uncertainty and discuss deviations from this mode.Methods: Literature search of PubMed and Google Scholar.Results: As manifestation of anxiety disorders includes peripheral reactions and negative cognitions, we suggest an integrative model of threat cognitions modulated by sensorimotor regions: “The Sensorimotor-Cognitive-Integration-Circuit.” The model emphasizes autonomic nervous system coupling with the cortex, addressing peripheral anxious reactions to uncertainty, pathways connecting cortical regions and cost-reward evaluation circuits to sensorimotor regions, filtered by the amygdala and basal ganglia. Of special interest are the ascending and descending tracts for sensory-motor crosstalk in healthy and pathological conditions. We include arguments regarding uncertainty in anxiety reactions to the pandemic and derive from our model treatment suggestions which are supported by scientific evidence. Our model is based on systematic control theories and emphasizes the role of goal conflict regulation in health and pathology. We also address anxiety reactions as a spectrum ranging from healthy to pathological coping with uncertainty, and present this spectrum as a transdiagnostic entity in accordance with recent claims and models.Conclusions: The human need for controllability and predictability suggests that anxiety disorders reactive to the pandemic's uncertainties reflect pathological disorganization of top-down bottom-up signaling and neural noise resulting from non-pathological human needs for coherence in life.

Noise-Induced Hearing Loss in Gerbil: Round Window Assays of Synapse Loss.

Previous work in animals with recovered hearing thresholds but permanent inner hair cell synapse loss after noise have suggested initial vulnerability of low spontaneous rate (SR) auditory nerve fibers (ANF). As these fibers have properties of response that facilitate robust sound coding in continuous noise backgrounds, their targeted loss would have important implications for function. To address the issue of relative ANF vulnerabilities after noise, we assessed cochlear physiologic and histologic consequences of temporary threshold shift-producing sound over-exposure in the gerbil, a species with well-characterized distributions of auditory neurons by SR category. The noise exposure targeted a cochlear region with distributed innervation (low-, medium- and high-SR neurons). It produced moderate elevations in outer hair cell-based distortion-product otoacoustic emission and whole nerve compound action potential thresholds in this region, with accompanying reductions in suprathreshold response amplitudes, quantified at 24 h. These parameters of response recovered well with post-exposure time. Chronic synapse loss was maximum in the frequency region initially targeted by the noise. Cochlear round window recorded mass potentials (spontaneous neural noise and sound-driven peri-stimulus time responses, PSTR) reflected parameters of the loss not detected by the conventional assays. Spontaneous activity was acutely reduced. Steady-state (PSTR plateau) activity was correlated with synapse loss in frequency regions with high concentrations of low-SR neurons, whereas the PSTR onset peak and spontaneous round window noise, both dominated by high-SR fiber activity, were relatively unaltered across frequency in chronic ears. Together, results suggest that acute targets of noise were of mixed SR subtypes, but chronic targets were predominantly low-SR neurons. PSTRs captured key properties of the auditory nerve response and vulnerability to injury that should yield important diagnostic information in hearing loss etiologies producing cochlear synaptic and neural loss.

Improving motor imagery classification during induced motor perturbations

Objective. Motor imagery is the mental simulation of movements. It is a common paradigm to design brain-computer interfaces (BCIs) that elicits the modulation of brain oscillatory activity similar to real, passive and induced movements. In this study, we used peripheral stimulation to provoke movements of one limb during the performance of motor imagery tasks. Unlike other works, in which induced movements are used to support the BCI operation, our goal was to test and improve the robustness of motor imagery based BCI systems to perturbations caused by artificially generated movements. Approach. We performed a BCI session with ten participants who carried out motor imagery of three limbs. In some of the trials, one of the arms was moved by neuromuscular stimulation. We analysed 2-class motor imagery classifications with and without movement perturbations. We investigated the performance decrease produced by these disturbances and designed different computational strategies to attenuate the observed classification accuracy drop. Main results. When the movement was induced in a limb not coincident with the motor imagery classes, extracting oscillatory sources of the movement imagination tasks resulted in BCI performance being similar to the control (undisturbed) condition; when the movement was induced in a limb also involved in the motor imagery tasks, the performance drop was significantly alleviated by spatially filtering out the neural noise caused by the stimulation. We also show that the loss of BCI accuracy was accompanied by weaker power of the sensorimotor rhythm. Importantly, this residual power could be used to predict whether a BCI user will perform with sufficient accuracy under the movement disturbances. Significance. We provide methods to ameliorate and even eliminate motor related afferent disturbances during the performance of motor imagery tasks. This can help improving the reliability of current motor imagery based BCI systems.

Computational models to delineate 3D gaze-shift strategies in Parkinson’s disease

Objective: Parkinson’s disease (PD) frequently affects vergence eye movements interfering with the perception of depth and dimensionality critical for mitigating falls. We examined neural strategies that compensate for abnormal vergence and their mechanistic underpinning in PD. Approach: The a priori hypothesis was that impaired vergence is compensated by incorporating rapid eye movements (saccades) to accomplish gaze shifts at different depths. Our experiments examined the hypothesis by simulating biologically plausible computational models of saccade-vergence interactions in PD and validating predictions in the actual patient data. Main results: We found four strategies to accomplish 3D gaze shift; pure vergence eye movements, pure saccadic eye movements, combinations of vergence followed by a saccade, and combination of saccade followed by vergence. The gaze shifting strategy of the two eyes was incongruent in PD. The latency of vergence was prolonged, and it was more so when the saccades preceded the vergence or when the saccades only made 3D gaze shift. Computational models predicted at least two possible mechanisms triggering saccades along with vergence. One is based on the lack of foveal accuracy when the vergence gain is suboptimal. The second mechanism reflects the noise in the gating mechanism, the omnipause neurons, for vergence and saccades. None of the two model predictions alone were completely supported by the patient data. However, a combined model incorporating both abnormal vergence velocity gain and impaired gating accurately simulated the results from PD patients. Significance: The combined strategy is biologically plausible for two reasons: (a) The basal ganglia that is prominently affected in PD projects to the vergence velocity neurons in the midbrain via the cerebellum. The projection directly affects the vergence velocity gain. (b) The basal ganglia, via superior colliculus, influences the pattern of omnipause neuronal activity. Abnormal basal ganglia activity may introduce noise in the omnipause neurons.

Non-stimulus-evoked activity as a measure of neural noise in the frequency-following response

BackgroundThe frequency-following response, or FFR, is a neurophysiologic response that captures distinct aspects of sound processing. Like all evoked responses, FFR is susceptible to electric and myogenic noise contamination during collection. Click-evoked auditory brainstem response collection standards have been adopted for FFR collection, however, whether these standards sufficiently limit FFR noise contamination is unknown. Thus, a critical question remains: to what extent do distinct FFR components reflect noise contamination? This is especially relevant for prestimulus amplitude (i.e., activity preceding the evoked response), as this measure has been used to index both noise contamination and neural noise. New methodWe performed two experiments. First, using >1000 young-adult FFRs, we ran regressions to determine the variance explained by myogenic and electrical noise, as indexed by artifact rejection count and electrode impedance, on each FFR component. Second, we reanalyzed prestimulus amplitude differences attributed to athletic experience and socioeconomic status, adding covariates of artifact rejection and impedance. ResultsWe found that non-neural noise marginally contributed to FFR components and could not explain group differences on prestimulus amplitude. Comparison with existing methodPrestimulus amplitude has been considered a measure of non-neural noise contamination. However, non-neural noise was not the sole contributor to variance in this measure and did not explain group differences. ConclusionsResults from the two experiments suggest that the effects of non-neural noise on FFR components are minimal and do not obscure individual differences in the FFR and that prestimulus amplitude indexes neural noise.

Synaptic Plasticity in Memristive Artificial Synapses and Their Robustness Against Noisy Inputs.

Emerging brain-inspired neuromorphic computing paradigms require devices that can emulate the complete functionality of biological synapses upon different neuronal activities in order to process big data flows in an efficient and cognitive manner while being robust against any noisy input. The memristive device has been proposed as a promising candidate for emulating artificial synapses due to their complex multilevel and dynamical plastic behaviors. In this work, we exploit ultrastable analog BiFeO3 (BFO)-based memristive devices for experimentally demonstrating that BFO artificial synapses support various long-term plastic functions, i.e., spike timing-dependent plasticity (STDP), cycle number-dependent plasticity (CNDP), and spiking rate-dependent plasticity (SRDP). The study on the impact of electrical stimuli in terms of pulse width and amplitude on STDP behaviors shows that their learning windows possess a wide range of timescale configurability, which can be a function of applied waveform. Moreover, beyond SRDP, the systematical and comparative study on generalized frequency-dependent plasticity (FDP) is carried out, which reveals for the first time that the ratio modulation between pulse width and pulse interval time within one spike cycle can result in both synaptic potentiation and depression effect within the same firing frequency. The impact of intrinsic neuronal noise on the STDP function of a single BFO artificial synapse can be neglected because thermal noise is two orders of magnitude smaller than the writing voltage and because the cycle-to-cycle variation of the current–voltage characteristics of a single BFO artificial synapses is small. However, extrinsic voltage fluctuations, e.g., in neural networks, cause a noisy input into the artificial synapses of the neural network. Here, the impact of extrinsic neuronal noise on the STDP function of a single BFO artificial synapse is analyzed in order to understand the robustness of plastic behavior in memristive artificial synapses against extrinsic noisy input.

Effect of acute alcohol intoxication on scale-free neural activity in the lateral septum in rats

Electrophysiological recordings of brain activity show both oscillatory dynamics that typically are analyzed in the time-frequency domain to describe brain oscillatory phenomena and scale-free arrhythmic activity defined as neural noise. Recent studies consider this arrhythmic fractal dynamics of neural noise as a sensitive biomarker of a number of cognitive processes, activity of neurotransmitter systems, changes that accompany neurodegenerative and psychiatric disorders including alcohol use disorder. We tested the changes in neural noise induced by acute alcohol intoxication in the lateral septum for the entire spectrum (1–200 Hz) of local field potential signal and for frequency specific ranges (delta, theta, beta, gamma and epsilon bands). Five male Wistar rats were implanted with intracranial electrodes and local field potential signal was measured for baseline activity and activity induced by acute ethanol intoxication (2 g/kg). Change in neural noise dynamics was assessed as a change in the slope of linear regression fit of power spectral density curves in double logarithmic scale. In our study alcohol resulted in lower incline of scale-free activity in the lateral septum for high frequency range and for the whole spectrum, which is interpreted generally as increase in neural noise and change in neuronal processing in a more stochastic way initiated by the acute alcohol intoxication. At the same time, we observed decrease in neural noise for low frequency range. The observed changes may be related to the shift of the excitatory-inhibitory balance towards inhibition and changes in neurotransmission mostly in the GABAergic system. Scale-free activity was sensitive in the conditions of acute alcohol intoxication, therefore to understand its role in alcohol use disorder we need more data and studies on the underlying processes. Future studies should include simultaneous recordings and analysis of arrhythmic dynamics with the oscillatory and multiunit spiking activity in the lateral septum. It can reveal the contribution of different-scale processes in changes driven by acute alcohol intoxication and clarify the specific electrophysiological mechanisms.

Evaluating a Convolutional Neural Network Noise Reduction Method When Applied to CT Images Reconstructed Differently Than Training Data.

The aim of this study was to evaluate a narrowly trained convolutional neural network (CNN) denoising algorithm when applied to images reconstructed differently than training data set. A residual CNN was trained using 10 noise inserted examinations. Training images were reconstructed with 275 mm of field of view (FOV), medium smooth kernel (D30), and 3 mm of thickness. Six examinations were reserved for testing; these were reconstructed with 100 to 450 mm of FOV, smooth to sharp kernels, and 1 to 5 mm of thickness. When test and training reconstruction settings were not matched, there was either reduced denoising efficiency or resolution degradation. Denoising efficiency was reduced when FOV was decreased or a smoother kernel was used. Resolution loss occurred when the network was applied to an increased FOV, sharper kernel, or decreased image thickness. The CNN denoising performance was degraded with variations in FOV, kernel, or decreased thickness. Denoising performance was not affected by increased thickness.

No Evidence of Reduced Contrast Sensitivity in Migraine-with-Aura for Large, Narrowband, Centrally Presented Noise-Masked Stimuli.

Individuals with migraine aura show differences in visual perception compared to control groups. Measures of contrast sensitivity have suggested that people with migraine aura are less able to exclude external visual noise, and that this relates to higher variability in neural processing. The current study compared contrast sensitivity in migraine with aura and control groups for narrow-band grating stimuli at 2 and 8 cycles/degree, masked by Gaussian white noise. We predicted that contrast sensitivity would be lower in the migraine with aura group at high noise levels. Contrast sensitivity was higher for the low spatial frequency stimuli, and decreased with the strength of the masking noise. We did not, however, find any evidence of reduced contrast sensitivity associated with migraine with aura. We propose alternative methods as a more targeted assessment of the role of neural noise and excitability as contributing factors to migraine aura.

Novelty and imitation within the brain: a Darwinian neurodynamic approach to combinatorial problems

Efficient search in vast combinatorial spaces, such as those of possible action sequences, linguistic structures, or causal explanations, is an essential component of intelligence. Is there any computational domain that is flexible enough to provide solutions to such diverse problems and can be robustly implemented over neural substrates? Based on previous accounts, we propose that a Darwinian process, operating over sequential cycles of imperfect copying and selection of neural informational patterns, is a promising candidate. Here we implement imperfect information copying through one reservoir computing unit teaching another. Teacher and learner roles are assigned dynamically based on evaluation of the readout signal. We demonstrate that the emerging Darwinian population of readout activity patterns is capable of maintaining and continually improving upon existing solutions over rugged combinatorial reward landscapes. We also demonstrate the existence of a sharp error threshold, a neural noise level beyond which information accumulated by an evolutionary process cannot be maintained. We introduce a novel analysis method, neural phylogenies, that displays the unfolding of the neural-evolutionary process.

Modeling and characterizing stochastic neurons based on in vitro voltage-dependent spike probability functions

Neurons in the nervous system are submitted to distinct sources of noise, such as ionic-channel and synaptic noise, which introduces variability in their responses to repeated presentations of identical stimuli. This motivates the use of stochastic models to describe neuronal behavior. In this work, we characterize an intrinsically stochastic neuron model based on a voltage-dependent spike probability function. We determine the effect of the intrinsic noise in single neurons by measuring the spike time reliability and study the stochastic resonance phenomenon. The model was able to show increased reliability for non-zero intrinsic noise values, according to what is known from the literature, and the addition of intrinsic stochasticity in it enhanced the region in which stochastic resonance is present. We proceeded to the study at the network level where we investigated the behavior of a random network composed of stochastic neurons. In this case, the addition of an extra dimension, represented by the intrinsic noise, revealed dynamic states of the system that could not be found otherwise. Finally, we propose a method to estimate the spike probability curve from in vitro electrophysiological data.

Clinical evaluation of a phantom-based deep convolutional neural network for whole-body-low-dose and ultra-low-dose CT skeletal surveys.

This study evaluated the clinical utility of a phantom-based convolutional neural network noise reduction framework for whole-body-low-dose CT skeletal surveys. The CT exams of ten patients with multiple myeloma were retrospectively analyzed. Exams were acquired with routine whole-body-low-dose CT protocol and projection noise insertion was used to simulate 25% dose exams. Images were reconstructed with either iterative reconstruction or filtered back projection with convolutional neural network post-processing. Diagnostic quality and structure visualization were blindly rated (subjective scale ranging from 0 [poor] to 100 [excellent]) by three musculoskeletal radiologists for iterative reconstruction and convolutional neural network images at routine whole-body-low-dose and 25% dose CT. For the diagnostic quality rating, the convolutional neural network outscored iterative reconstruction at routine whole-body-low-dose CT (convolutional neural network: 95 ± 5, iterative reconstruction: 85 ± 8) and at the 25% dose level (convolutional neural network: 79 ± 10, iterative reconstruction: 22 ± 13). Convolutional neural network applied to 25% dose was rated inferior to iterative reconstruction applied to routine dose. Similar trends were observed in rating experiments focusing on structure visualization. Results indicate that the phantom-based convolutional neural network noise reduction framework can improve visualization of critical structures within CT skeletal surveys. At matched dose level, the convolutional neural network outscored iterative reconstruction for all conditions studied. The image quality improvement of convolutional neural network applied to 25% dose indicates a potential for dose reduction; however, the 75% dose reduction condition studied is not currently recommended for clinical implementation.

Migraine Visual Aura and Cortical Spreading Depression-Linking Mathematical Models to Empirical Evidence.

This review describes the subjective experience of visual aura in migraine, outlines theoretical models of this phenomenon, and explores how these may be linked to neurochemical, electrophysiological, and psychophysical differences in sensory processing that have been reported in migraine with aura. Reaction–diffusion models have been used to model the hallucinations thought to arise from cortical spreading depolarisation and depression in migraine aura. One aim of this review is to make the underlying principles of these models accessible to a general readership. Cortical spreading depolarisation and depression in these models depends on the balance of the diffusion rate between excitation and inhibition and the occurrence of a large spike in activity to initiate spontaneous pattern formation. We review experimental evidence, including recordings of brain activity made during the aura and attack phase, self-reported triggers of migraine, and psychophysical studies of visual processing in migraine with aura, and how these might relate to mechanisms of excitability that make some people susceptible to aura. Increased cortical excitability, increased neural noise, and fluctuations in oscillatory activity across the migraine cycle are all factors that are likely to contribute to the occurrence of migraine aura. There remain many outstanding questions relating to the current limitations of both models and experimental evidence. Nevertheless, reaction–diffusion models, by providing an integrative theoretical framework, support the generation of testable experimental hypotheses to guide future research.

Serial Detection with Neural Network-Based Noise Prediction for Bit-Patterned Media Recording Systems

Ultra-high density data storage has gained high significance given the increasing amounts of data; many technologies have been proposed to achieve a high density. Among them, bit-pattern media recording (BPMR) is a promising technology. In BPMR systems, data are stored on magnetic islands. Therefore, high densities can be achieved by reducing the distance between the magnetic islands. Because of the closeness between the magnetic islands, the readback signal is distorted by two-dimensional (2D) interference, which includes the intersymbol interference according to the down-track direction and the intertrack interference according to the cross-track direction. A simple and effective serial detection algorithm was recently proposed to mitigate the 2D interference. However, serial detection utilizes the hard output in inner detection, and this degrades the serial detection performance. To resolve this problem, a subsequent study used feedback to estimate the noise and used this noise signal to create a soft output for inner detection. Following up, in this paper we propose a model that utilizes a neural network for noise prediction. The proposed neural network-based model and the model with the feedback line were compared in terms of bit error rate (BER). The results show that the proposed model achieves a gain of approximately 1 dB at a BER of 10−6.

Diverse ocean noise classification using deep learning

The alarming rise in the ocean noises due to the ramping up of anthropogenic activities had adversely impacted the marine fauna and altered the soundscape of the oceans. In recent times, Deep learning has gained high significance in assessing this pervasive underwater noise data. Studying the underwater soundscape is crucial in conserving the ocean health and in achieving a “quiet ocean”. As an effort to it, in this paper, we present a deep neural network architecture, Convolutional Neural Network-based ocean noise classification cum recognition system capable of classifying vocalization of cetaceans, fishes, marine invertebrates, anthropogenic sounds, natural sounds, and the unidentified ocean sounds from passive acoustic ocean noise recordings. The challenge is to classify these noises amidst the highly non-stationary sound spectrum of short low grunts to long high peaked vocals, limited sensor receiving range, and the need for a large annotated training data. The proposed method can self-learn the features from the training audio data with no need for feature extraction proving better adaptability to complex audio acoustic signals. Experimental results prove that the proposed system is befitting for classifying ocean noises with 96.1% accuracy. Classification helps in distinguishing natural acoustic systems from artificial acoustic systems.

Sensorless force estimation for industrial robots using disturbance observer and neural learning of friction approximation

Contact force estimation enables robots to physically interact with unknown environments and to work with human operators in a shared workspace. Most heavy-duty industrial robots without built-in force/torque sensors rely on the inverse dynamics for the sensorless force estimation. However, this scheme suffers from the serious model uncertainty induced by the nonnegligible noise in the estimation process. This paper proposes a sensorless scheme to estimate the unknown contact force induced by the physical interaction with robots. The model-based identification scheme is initially used to obtain dynamic parameters. Then, neural learning of friction approximation is designed to enhance estimation performance for robotic systems subject with the model uncertainty. The external force exerted on the robot is estimated by a disturbance observer which models the external disturbance. A momentum observer is modified to develop a disturbance Kalman filter-based approach for estimating the contact force. The neural network-based model uncertainty and measurement noise level are analysed to guarantee the robustness of the Kalman filter-based force observer. The proposed scheme is verified by the measurement data from a heavy-duty industrial robot with 6 degrees of freedom (KUKA AUGLIS six). The experimental results are used to demonstrate the estimation performance of the proposed approach by the comparison with the existing schemes. • A novel sensorless scheme is developed to estimate unknown contact force for robots. • NN learning of friction compensates model uncertainties with good approximation. • Disturbance Kalman filter observer is to achieve accurate contact force estimation • Observers and NN learning of friction show robust observation to small contact force.