The effect of Gaussian noise on the threshold, dynamic range, and loudness of analogue cochlear implant stimuli

Stochastic resonance in the sensory systems and its applications in neural prosthetics

Noise is generally considered to be detrimental. In the right conditions, however, noise can improve signal detection or information transmission. This counterintuitive phenomenon is called stochastic resonance (SR). SR has generated significant interdisciplinary interest, particularly in physics, engineering, and medical and environmental sciences. In this review, we discuss a growing empirical literature that suggests that noise at the right intensity may improve the detection and processing of auditory, sensorimotor, and visual stimuli. We focus particularly on applications of SR in sensory biology and investigate whether SR-based technologies present a pathway to improve outcomes for individuals living with sensory impairments. We conclude that there is considerable evidence supporting the application of SR in developing sensory prosthetics. However, the progression of SR-based technologies is variable across the sensory modalities. We suggest opportunities for further advancements in each modality, considering the best approaches to maximise benefits and capitalise on progress already made. Overall, SR can offer opportunities to improve existing technologies or to motivate innovations.

Enhanced brainstem phase-locking in low-level noise reveals stochastic resonance in the frequency-following response (FFR)

In nonlinear systems, the inclusion of low-level noise can paradoxically improve signal detection, a phenomenon known as stochastic resonance (SR). SR has been observed in human hearing whereby sensory thresholds (e.g., signal detection and discrimination) are enhanced in the presence of noise. Here, we asked whether subcortical auditory processing (neural phase locking) shows evidence of SR. We recorded brainstem frequency-following-responses (FFRs) in young, normal-hearing listeners to near-electrophysiological-threshold (40 dB SPL) complex tones composed of 10 iso-amplitude harmonics of 150 Hz fundamental frequency (F0) presented concurrent with low-level noise (+20 to -20 dB SNRs). Though variable and weak across ears, some listeners showed improvement in auditory detection thresholds with subthreshold noise confirming SR psychophysically. At the neural level, low-level FFRs were initially eradicated by noise (expected masking effect) but were surprisingly reinvigorated at select masker levels (local maximum near ∼ 35 dB SPL). These data suggest brainstem phase-locking to near threshold periodic stimuli is enhanced in optimal levels of noise, the hallmark of SR. Our findings provide novel evidence for stochastic resonance in the human auditory brainstem and suggest that under some circumstances, noise can actually benefit both the behavioral and neural encoding of complex sounds.

Auditory noise improves balance control by cross-modal stochastic resonance

It is known that enhanced somatosensory function leads to improved balance, and somatosensory function can be enhanced by the appropriate level of mechanical, visual, or auditory noise. In this study, we tested the potential benefit of an auditory noise on balance control. We first assessed static balance by measuring 10 times the duration of standing on the toes of one leg with closed eyes. For the 18 healthy adult participants, the median standing times ranged from 2.1 to 45.6 s, and the median of the distribution was 9.9 s. From the above, the participants were divided into two groups: lower (below 10 s, n = 9) and higher (above 10 s, n = 9) balance groups. We then investigated the effect on balance control of an auditory white noise emitted at the detection threshold. Each individual performed 20 trials. The auditory noise was applied in half the trials, while the remaining trials were conducted without noise. The order of the noise and no-noise trials was quasi-random. In the lower-balance group, the median standing time significantly increased during the noise trials (10.3 s) compared with the time in the no-noise controls (5.2 s). On the other hand, noise had no significant effect in the higher-balance group, presumably because of a ceiling effect. These findings suggest that static balance in the lower-balance participants can be improved by applying a weak noise through cross-modal stochastic resonance.

Use of Research Interfaces for Psychophysical Studies With Cochlear-Implant Users

A growing number of laboratories are using research interfaces to conduct experiments with cochlear-implant (CI) users. Because these interfaces bypass a subject’s clinical sound processor, several concerns exist regarding safety and stimulation levels. Here we suggest best-practice approaches for how to safely and ethically perform this type of research and highlight areas of limited knowledge where further research is needed to help clarify safety limits. The article is designed to provide an introductory level of technical detail about the devices and the effects of electrical stimulation on perception and neurophysiology. From this, we summarize what should be the best practices in the field, based on the literature and our experience. Findings from the review of the literature suggest that there are three main safety concerns: (a) to prevent biological or neural damage, (b) to avoid presentation of uncomfortably loud sounds, and (c) to ensure that subjects have control over stimulus presentation. Researchers must pay close attention to the software–hardware interface to ensure that the three main safety concerns are closely monitored. An important area for future research will be the determination of the amount of biological damage that can occur from electrical stimulation from a CI placed in the cochlea, not in direct contact with neural tissue. As technology used in research with CIs evolve, some of these approaches may change. However, the three main safety principles outlined here are not anticipated to undergo change with technological advances.

Modeling electrical stimulation of retinal ganglion cell with optimizing additive noises for reducing threshold and energy consumption

BACKGROUND

Epiretinal prosthesis is one device for the treatment of blindness, which target retinal ganglion cells (RGCs) by electrodes on retinal surface. The stimulating current of epiretinal prosthesis is an important factor that influences the safety threshold and visual perception. Stochastic resonance (SR) can be used to enhance the detection and transmission of subthreshold stimuli in neurons. Here, it was assumed that SR was a potential way to improve the performance of epiretinal prosthesis. The effect of noises on the response of RGCs to electrical stimulation and the energy of stimulating current was studied based on a RGC model.

METHODS

The RGC was modeled as a multi-compartment model consisting of dendrites and its branches, soma and axon. To evoke SR, a subthreshold signal, a series of bipolar rectangular pulse sequences, plus stochastic biphasic pulse sequences as noises, were used as a stimulus to the model. The SR-type behavior in the model was characterized by a "power norm" measure. To decrease energy consumption of the stimulation waveform, the stochastic biphasic pulse sequences were only added to the cathode and anode phase of the subthreshold pulse and the noise parameters were optimized by using a genetic algorithm (GA).

RESULTS

When certain intensity of noise is added to the subthreshold signal, RGC model can fire. With the noise's RMS amplitudes increased, more spikes were elicited and the curve of power norm presents the inverted U-like graph. The larger pulse width of stochastic biphasic pulse sequences resulted in higher power norm. The energy consumption and charges of the single bipolar rectangular pulse without noise in threshold level are 468.18 pJ, 15.30 nC, and after adding optimized parameters's noise to the subthreshold signal, they became 314.8174 pJ, 11.9281 nC and were reduced by 32.8 and 22.0%, respectively.

CONCLUSIONS

The SR exists in the RGC model and can enhance the representation of RGC model to the subthreshold signal. Adding the stochastic biphasic pulse sequences to the cathode and anode phase of the subthreshold signal helps to reduce stimulation threshold, energy consumption and charge of RGC stimulation. These may be helpful for improving the performance of epiretinal prosthesis.

Stochastic information transfer from cochlear implant electrodes to auditory nerve fibers

Cochlear implants, also called bionic ears, are implanted neural prostheses that can restore lost human hearing function by direct electrical stimulation of auditory nerve fibers. Previously, an information-theoretic framework for numerically estimating the optimal number of electrodes in cochlear implants has been devised. This approach relies on a model of stochastic action potential generation and a discrete memoryless channel model of the interface between the array of electrodes and the auditory nerve fibers. Using these models, the stochastic information transfer from cochlear implant electrodes to auditory nerve fibers is estimated from the mutual information between channel inputs (the locations of electrodes) and channel outputs (the set of electrode-activated nerve fibers). Here we describe a revised model of the channel output in the framework that avoids the side effects caused by an "ambiguity state" in the original model and also makes fewer assumptions about perceptual processing in the brain. A detailed comparison of how different assumptions on fibers and current spread modes impact on the information transfer in the original model and in the revised model is presented. We also mathematically derive an upper bound on the mutual information in the revised model, which becomes tighter as the number of electrodes increases. We found that the revised model leads to a significantly larger maximum mutual information and corresponding number of electrodes compared with the original model and conclude that the assumptions made in this part of the modeling framework are crucial to the model's overall utility.

Information theoretic inference of the optimal number of electrodes for future cochlear implants using a spiral cochlea model

Contemporary cochlear implants stimulate the auditory nerve with an array of up to 22 electrodes. More electrodes do not typically provide improved hearing performance. Given that this limitation is primarily due to current spread, and that newly developing kinds of electrodes may enable more focused stimulation, we recently proposed an information theoretic modeling framework for estimating how many electrodes might achieve optimal hearing performance under a range of assumptions about electrodes and their placement relative to the nerve. Here, we extend this approach by introducing more realistic three-dimensional spiral geometries for the cochlea and array, and comparing the optimal number of electrodes predicted by our model for this case with that in our original model, which used a linear geometry.

Stochastic facilitation of artificial tactile sensation in primates

Artificial sensation via electrical or optical stimulation of brain sensory areas offers a promising treatment for sensory deficits. For a brain-machine-brain interface, such artificial sensation conveys feedback signals from a sensorized prosthetic limb. The ways neural tissue can be stimulated to evoke artificial sensation and the parameter space of such stimulation, however, remain largely unexplored. Here we investigated whether stochastic facilitation (SF) could enhance an artificial tactile sensation produced by intracortical microstimulation (ICMS). Two rhesus monkeys learned to use a virtual hand, which they moved with a joystick, to explore virtual objects on a computer screen. They sought an object associated with a particular artificial texture (AT) signaled by a periodic ICMS pattern delivered to the primary somatosensory cortex (S1) through a pair of implanted electrodes. During each behavioral trial, aperiodic ICMS (i.e., noise) of randomly chosen amplitude was delivered to S1 through another electrode pair implanted 1 mm away from the site of AT delivery. Whereas high-amplitude noise worsened AT detection, moderate noise clearly improved the detection of weak signals, significantly raising the proportion of correct trials. These findings suggest that SF could be used to enhance prosthetic sensation.

Intensity coding in electric hearing: effects of electrode configurations and stimulation waveforms

OBJECTIVES

Current cochlear implants typically stimulate the auditory nerve with biphasic pulses and monopolar electrode configurations. Tripolar stimulation can increase spatial selectivity and potentially improve place pitch related perception but requires higher current levels to elicit the same loudness as monopolar stimulation. The present study combined delayed pseudomonophonasic pulses, which produce lower thresholds, with tripolar stimulation in an attempt to solve the power-performance tradeoff problem.

DESIGN

The present study systematically measured thresholds, dynamic range, loudness growth, and intensity discrimination using either biphasic or delayed pseudomonophonasic pulses under both monopolar and tripolar stimulation. Participants were five Clarion cochlear implant users. For each subject, data from apical, middle, and basal electrode positions were collected when possible.

RESULTS

Compared with biphasic pulses, delayed pseudomonophonasic pulses increased the dynamic range by lowering thresholds while maintaining comparable maximum allowable levels under both electrode configurations. However, delayed pseudomonophonasic pulses did not change the shape of loudness growth function and actually increased intensity discrimination limens, especially at lower current levels.

CONCLUSIONS

The present results indicate that delayed pseudomonophonasic pulses coupled with tripolar stimulation cannot provide significant power savings nor can it increase the functional dynamic range. Whether this combined stimulation could improve functional spectral resolution remains to be seen.

Effective stochastic resonance of coupled excitable elements under noise of unequal amplitude

Effective stochastic resonance (SR) is numerically studied using a model of two coupled excitable FitzHugh-Nagumo elements exposed to noise of unequal amplitude. Compared to previous SR models of a single excitable element and of two coupled excitable elements exposed to noise of equal amplitude, the present model exhibits a more intense resonance to the external signal. To extract the key dynamics of this effective SR, the idea of triggered excitation is introduced. Triggered excitation means a process in which the noise-free element is excited synchronously to the external signal with the triggering action of another element that randomly fluctuates asynchronously to the external signal. A deterministic simulation simply proving the triggered excitation process supports our idea.

Enhancement of neural stochastic firing in cochlear implant stimulation by the addition of noise: a computational study of the influence of stimulation settings and spontaneous activity

It is known that, in cochlear implants (CIs), the addition of noise to CI stimuli may enhance the stochastic firing in the auditory nerve (AN). The aim of this study was to investigate, by using a model of a fiber of the AN the influence of CI stimulation settings (i.e., stimulation rate and stimulus magnitude) and fiber spontaneous activity on the enhancement of stochastic firing when CI stimuli are combined with noise. Results showed that the stimulation rate had an effect on the enhancement of stochastic firing in the AN, whereas the stimulus magnitude and the spontaneous firing rate had no influence.

Effects of high-rate pulse trains on electrode discrimination in cochlear implant users

Overcoming issues related to abnormally high neural synchrony in response to electrical stimulation is one aspect in improving hearing with a cochlear implant. Desynchronization of electrical stimuli have shown benefits in neural encoding of electrical signals and improvements in psychophysical tasks. In the present study, 10 participants with either CII or HiRes 90k Advanced Bionics devices were tested for the effects of desynchronizing constant-amplitude high-rate (5,000 Hz) pulse trains on electrode discrimination of sinusoidal stimuli (1,000 Hz). When averaged across the sinusoidal dynamic range, overall improvements in electrode discrimination with high-rate pulses were found for 8 of 10 participants. This effect was significant for the group (p = .003). Nonmonotonic patterns of electrode discrimination as a function of sinusoidal stimulation level were observed. By providing additional spectral channels, it is possible that clinical implementation of constant-amplitude high-rate pulse trains in a signal processing strategy may improve performance with the device.

What is stochastic resonance? Definitions, misconceptions, debates, and its relevance to biology

Stochastic resonance is said to be observed when increases in levels of unpredictable fluctuations--e.g., random noise--cause an increase in a metric of the quality of signal transmission or detection performance, rather than a decrease. This counterintuitive effect relies on system nonlinearities and on some parameter ranges being "suboptimal". Stochastic resonance has been observed, quantified, and described in a plethora of physical and biological systems, including neurons. Being a topic of widespread multidisciplinary interest, the definition of stochastic resonance has evolved significantly over the last decade or so, leading to a number of debates, misunderstandings, and controversies. Perhaps the most important debate is whether the brain has evolved to utilize random noise in vivo, as part of the "neural code". Surprisingly, this debate has been for the most part ignored by neuroscientists, despite much indirect evidence of a positive role for noise in the brain. We explore some of the reasons for this and argue why it would be more surprising if the brain did not exploit randomness provided by noise--via stochastic resonance or otherwise--than if it did. We also challenge neuroscientists and biologists, both computational and experimental, to embrace a very broad definition of stochastic resonance in terms of signal-processing "noise benefits", and to devise experiments aimed at verifying that random variability can play a functional role in the brain, nervous system, or other areas of biology.

Suprathreshold stochastic resonance in visual signal detection

The present study aimed to demonstrate stochastic resonance (SR) for a visual signal above the threshold by using a two-alternative forced-choice protocol. The percent correct response enhanced at non-zero intensity of noise, while decreased with more intense noise. SR for the suprathreshold signals was found for signals with relatively low intensity above the threshold, and disappeared for more intense signals. These findings show the suprathreshold SR in the visual perception.

Optimal stimulus and noise distributions for information transmission via suprathreshold stochastic resonance

Suprathreshold stochastic resonance (SSR) is a form of noise-enhanced signal transmission that occurs in a parallel array of independently noisy identical threshold nonlinearities, including model neurons. Unlike most forms of stochastic resonance, the output response to suprathreshold random input signals of arbitrary magnitude is improved by the presence of even small amounts of noise. In this paper, the information transmission performance of SSR in the limit of a large array size is considered. Using a relationship between Shannon's mutual information and Fisher information, a sufficient condition for optimality, i.e., channel capacity, is derived. It is shown that capacity is achieved when the signal distribution is Jeffrey's prior, as formed from the noise distribution, or when the noise distribution depends on the signal distribution via a cosine relationship. These results provide theoretical verification and justification for previous work in both computational neuroscience and electronics.