Broadband neural encoding in the cricket cercal sensory system enhanced by stochastic resonance

Importance of Modulating Kynurenic Acid Metabolism-Approaches for the Treatment of Dementia

In this article, we focus on kynurenic acid metabolism in neuropsychiatric disorders and the biochemical processes involved in memory and cognitive impairment, followed by different approaches in the fight against dementia. Kynurenic acid-a biochemical part of L-tryptophan catabolism-is synthesized from L-kynurenine by kynurenine aminotransferases. Experimental pharmacological studies have shown that elevated levels of kynurenic acid in the brain are associated with impaired learning and that lowering kynurenic acid levels can improve these symptoms. The discovery of new compounds with the ability to block kynurenine aminotransferases opens new therapeutic avenues for the treatment of memory impairment and dementia. The newly developed Helix pomatia snail model of memory can be used for the assessment of novel pharmacological approaches. Dietary supplementation with natural molecular/herbal extracts, exercise, and physical activity have significant impacts on endogenous pharmacology by reducing kynurenic acid synthesis, and these factors are likely to significantly modulate steady-state biological conditions and delay the negative consequences of aging, including the onset of pathological processes.

Auditory Noise Facilitates Lower Visual Reaction Times in Humans

Noise is commonly seen as a disturbance but can influence any system it interacts with. This influence may not always be desirable, but sometimes it can improve the system's performance. For example, stochastic resonance is a phenomenon where adding the right amount of noise to a weak signal makes it easier to detect. This is known as sub-threshold detection. This sub-threshold detection's natural fingerprint is the fact that the threshold values follow an inverse U-shaped curve as the noise intensity increases. The minimum threshold value is the point of maximum sensitivity and represents the optimal point that divides the dynamics in two. Below that point, we can find the beneficial noise branch, where the noise can facilitate better detection. Above that point, the common detrimental noise concept can be found: adding noise hinders signal detection. The nervous system controls the movements and bodily functions in the human body. By reducing the sensory thresholds, we can improve the balance of these functions. Additionally, researchers have wondered if noise could be applied to different senses or motor mechanisms to enhance our abilities. In this work, noise is used to improve human reaction times. We tested the hypothesis that visual reaction times decrease significantly when the subject's perception is in the beneficial noise branch and closer to the optimal point than outside of this condition. Auditory noise was introduced in 101 human subjects using an interface capable of searching for the right amount of noise to place the subject in the beneficial noise branch close to the optimal point. When comparing the results, the reaction times decreased when the subjects were at the optimal point compared to when the subjects were outside of such conditions. These results reveal the possibility of using this approach to enhance human performance in tasks requiring faster reaction times, such as sports.

Detecting stochastic multiresonance in neural networks via statistical complexity measure

This paper employs statistical complexity measure (SCM) to investigate the occurrence of stochastic multiresonance (SMR) induced by noise and time delay in small-world neural networks coupled with FitzHugh-Nagumo (FHN) neurons. Our findings reveal that SCM exhibits four local maxima at four optimal noise levels, providing evidence for the occurrence of quadruple stochastic resonances. When time delay τ is taken into account in the information transmission, under moderate noise levels, SCM shows several local maxima when τ = n T e with n being a positive integer and T e being the period of subthreshold signal. This indicates the appearance of delay-induced SMR at the multiples of the period of subthreshold signal. Intriguingly, at low noise levels, a strong coherence between time delay and neuronal firing dynamics emerges at τ = n T e - 2 , as confirmed by a series of SCM maxima at these time delays. Furthermore, the study demonstrates that by adjusting the degrees and sizes of small-world networks, as well as the coupling strength, it is possible to optimize the strength of delay-induced SMR, thus maximizing the detection capability of subthreshold signal. The research results may provide us with an effective approach for understanding the role of time delay in signal detection and information transmission.

Stochastic resonance in sparse neuronal network: functional role of ongoing activity to detect weak sensory input in awake auditory cortex of rat

The awake cortex is characterized by a higher level of ongoing spontaneous activity, but it has a better detectability of weak sensory inputs than the anesthetized cortex. However, the computational mechanism underlying this paradoxical nature of awake neuronal activity remains to be elucidated. Here, we propose a hypothetical stochastic resonance, which improves the signal-to-noise ratio (SNR) of weak sensory inputs through nonlinear relations between ongoing spontaneous activities and sensory-evoked activities. Prestimulus and tone-evoked activities were investigated via in vivo extracellular recording with a dense microelectrode array covering the entire auditory cortex in rats in both awake and anesthetized states. We found that tone-evoked activities increased supralinearly with the prestimulus activity level in the awake state and that the SNR of weak stimulus representation was optimized at an intermediate level of prestimulus ongoing activity. Furthermore, the temporally intermittent firing pattern, but not the trial-by-trial reliability or the fluctuation of local field potential, was identified as a relevant factor for SNR improvement. Since ongoing activity differs among neurons, hypothetical stochastic resonance or "sparse network stochastic resonance" might offer beneficial SNR improvement at the single-neuron level, which is compatible with the sparse representation in the sensory cortex.

Vasomotion heterogeneity and spectral characteristics in diabetic and hypertensive patients

Vasomotion refers to the spontaneous oscillation of blood vessels within a frequency range of 0.01 to 1.6 Hz. Various disease states, including hypertension and diabetes, have been associated with alterations in vasomotion at the finger, indicating potential impairment of skin microcirculation. Due to the non-linear nature of human vasculature, the modification of vasomotion may vary across different locations for different diseases. In this study, Laser Doppler Flowmetry was used to measure blood flow motion at acupoints LU8, LU5, SP6, and PC3 among 49 participants with or without diabetes and/or hypertension. Fast Fourier Transformation was used to analyze noise type while Hilbert-Huang Transformation and wavelet analysis were applied to assess Signal Noise Ratio (SNR) results. Statistical analysis revealed that different acupoints exhibit distinct spectral characteristics of vasomotion not only among healthy individuals but also among patients with diabetes and/or hypertension. The results showed strong heterogeneity of vasomotion among blood vessels, indicating that the vasomotion measured at a certain point may not reflect the real status of microcirculation.

Detecting a periodic signal by a population of spiking neurons in the weakly nonlinear response regime

Motivated by experimental observations, we investigate a variant of the cocktail party problem: the detection of a weak periodic stimulus in the presence of fluctuations and another periodic stimulus which is stronger than the periodic signal to be detected. Specifically, we study the response of a population of stochastic leaky integrate-and-fire (LIF) neurons to two periodic signals and focus in particular on the question, whether the presence of one of the stimuli can be detected from the population activity. As a detection criterion, we use a simple threshold-crossing of the population activity over a certain time window. We show by means of the receiver operating characteristics (ROC) that the detectability depends only weakly on the time window of observation but rather strongly on the stimulus amplitude. Counterintuitively, the detection of the weak periodic signal can be facilitated by the presence of a strong periodic input current depending on the frequencies of the two signals and on the dynamical regime in which the neurons operate. Beside numerical simulations of the model, we present an analytical approximation for the ROC curve that is based on the weakly nonlinear response theory for a stochastic LIF neuron.

Effects of stochastic coding on olfactory discrimination in flies and mice

Sparse coding can improve discrimination of sensory stimuli by reducing overlap between their representations. Two factors, however, can offset sparse coding's benefits: similar sensory stimuli have significant overlap and responses vary across trials. To elucidate the effects of these 2 factors, we analyzed odor responses in the fly and mouse olfactory regions implicated in learning and discrimination-the mushroom body (MB) and the piriform cortex (PCx). We found that neuronal responses fall along a continuum from extremely reliable across trials to extremely variable or stochastic. Computationally, we show that the observed variability arises from noise within central circuits rather than sensory noise. We propose this coding scheme to be advantageous for coarse- and fine-odor discrimination. More reliable cells enable quick discrimination between dissimilar odors. For similar odors, however, these cells overlap and do not provide distinguishing information. By contrast, more unreliable cells are decorrelated for similar odors, providing distinguishing information, though these benefits only accrue with extended training with more trials. Overall, we have uncovered a conserved, stochastic coding scheme in vertebrates and invertebrates, and we identify a candidate mechanism, based on variability in a winner-take-all (WTA) inhibitory circuit, that improves discrimination with training.

Mutual information measure of visual perception based on noisy spiking neural networks

Note that images of low-illumination are weak aperiodic signals, while mutual information can be used as an effective measure for the shared information between the input stimulus and the output response of nonlinear systems, thus it is possible to develop novel visual perception algorithm based on the principle of aperiodic stochastic resonance within the frame of information theory. To confirm this, we reveal this phenomenon using the integrate-and-fire neural networks of neurons with noisy binary random signal as input first. And then, we propose an improved visual perception algorithm with the image mutual information as assessment index. The numerical experiences show that the target image can be picked up with more easiness by the maximal mutual information than by the minimum of natural image quality evaluation (NIQE), which is one of the most frequently used indexes. Moreover, the advantage of choosing quantile as spike threshold has also been confirmed. The improvement of this research should provide large convenience for potential applications including video tracking in environments of low illumination.

Contrast detection is enhanced by deterministic, high-frequency transcranial alternating current stimulation with triangle and sine waveform

Stochastic resonance (SR) describes a phenomenon where an additive noise (stochastic carrier-wave) enhances the signal transmission in a nonlinear system. In the nervous system, nonlinear properties are present from the level of single ion channels all the way to perception and appear to support the emergence of SR. For example, SR has been repeatedly demonstrated for visual detection tasks, also by adding noise directly to cortical areas via transcranial random noise stimulation (tRNS). When dealing with nonlinear physical systems, it has been suggested that resonance can be induced not only by adding stochastic signals (i.e., noise) but also by adding a large class of signals that are not stochastic in nature that cause "deterministic amplitude resonance" (DAR). Here, we mathematically show that high-frequency, deterministic, periodic signals can yield resonance-like effects with linear transfer and infinite signal-to-noise ratio at the output. We tested this prediction empirically and investigated whether nonrandom, high-frequency, transcranial alternating current stimulation (tACS) applied to the visual cortex could induce resonance-like effects and enhance the performance of a visual detection task. We demonstrated in 28 participants that applying 80-Hz triangular-waves or sine-waves with tACS reduced the visual contrast detection threshold for optimal brain stimulation intensities. The influence of tACS on contrast sensitivity was equally effective to tRNS-induced modulation, demonstrating that both tACS and tRNS can reduce contrast detection thresholds. Our findings suggest that a resonance-like mechanism can also emerge when deterministic electrical waveforms are applied via tACS.NEW & NOTEWORTHY Our findings extend our understanding of neuromodulation induced by noninvasive electrical stimulation. We provide the first evidence showing acute online benefits of transcranial alternating current stimulation (tACS)triangle and tACSsine targeting the primary visual cortex (V1) on visual contrast detection in accordance with the resonance-like phenomenon. The "deterministic" tACS and "stochastic" high-frequency-transcranial random noise stimulation (tRNS) are equally effective in enhancing visual contrast detection.

Auditory, tactile, and multimodal noise reduce balance variability

Auditory and somatosensory white noise can stabilize standing balance. However, the differential effects of auditory and tactile noise stimulation on balance are unknown. Prior work on unimodal noise stimulation showed gains in balance with white noise through the auditory and tactile modalities separately. The current study aims to examine whether multimodal noise elicits similar responses to unimodal noise. We recorded the postural sway of healthy young adults who were presented with continuous white noise through the auditory or tactile modalities and through a combination of both (multimodal condition) using a wearable device. Our results replicate previous work that showed that auditory or tactile noise reduces sway variability with and without vision. Additionally, we show that multimodal noise also reduces the variability of sway. Analysis of different frequency bands of sway is typically used to separate open-loop exploratory (< 0.3 Hz) and feedback-driven (> 0.3 Hz) sway. We performed this analysis and showed that unimodal and multimodal white noise affected postural sway variability similarly in both timescales. These results support that the sensory noise effects on balance are robust across unimodal and multimodal conditions and can affect both mechanisms of sway represented in the frequency spectrum. In future work, the parameters of acoustic/tactile manipulation should be optimized for the most effective balance stabilization, and multimodal therapies should be explored for older adults with typical age-related balance instabilities.

Effects of stochastic resonance whole-body vibration on sensorimotor function in elderly individuals-A systematic review

Introduction

Due to demographic changes, falls are increasingly becoming a focus of health care. It is known that within six months after a fall, two thirds of fallers will fall again. Therefore, therapeutic procedures to improve balance that are simple and can be performed in a short time are needed. Stochastic resonance whole-body vibration (SR-WBV) may be such a procedure.

Method

An electronic search to assess the effectiveness of SR-WBV on balance in the elderly was conducted using databases that included CINAHL Cochrane, PEDro, and PubMed. Included studies were assessed using the Collaboration Risk of Bias Tool by two independent reviewers.

Results

Nine studies showing moderate methodological quality were included. Treatment parameters were heterogeneous. Vibration frequency ranged from 1 to 12 Hz. Six studies found statistically significant improvements of balance from baseline to post measurement after SR-WBV interventions. One article found clinical relevance of the improvement in total time of the "Expanded Time to Get Up and Go Test".

Discussion

Physiological adaptations after balance training are specific and may explain some of the observed heterogeneity. Two out of nine studies assessed reactive balance and both indicated statistically significant improvements after SR-WBV. Therefore, SR-WBV represents a reactive balance training.

Stimulator compensation and generation of Gaussian noise stimuli with defined amplitude spectra for studying input–output relations of sensory systems

Gaussian noise is an important stimulus for the study of biological systems, especially sensory and neural systems. Since these systems are inherently nonlinear, the properties of the noise strongly influence the outcome of the analysis. Therefore, it is crucial to use a well-defined and controlled noise stimulus. In this paper, we first use the example of an insect filiform sensillum, a simple mechanoreceptor with a single sensory cell, to show that changes in the amplitude and spectral properties of the noise stimulus indeed affect the linear transfer function of the sensillum. We then explain step-by-step how to use the inverse fast Fourier transform to generate a Gaussian noise that has an arbitrary user-defined amplitude spectrum, including a band-limited white noise with a perfectly sharp cutoff edge. Finally, we demonstrate how such a perfect band-limited Gaussian white noise stimulus can also be generated with a non-perfect stimulator using a simple procedure that compensates for the filtering properties of the stimulator. With this approach, one can generate well-defined Gaussian noise stimuli that can be adapted to any application. For example, one can generate visual, sound, or vibrational stimuli for experimental research in visual physiology, auditory physiology, and biotremology, as well as inputs for testing various models in theoretical research.

Thermally driven single-electron stochastic resonance

Stochastic resonance (SR) in a single-electron system is expected to allow information to be correctly carried and processed by single electrons in the presence of thermal fluctuations. Here, we comprehensively study thermally driven single-electron SR. The response of the system to a weak voltage signal is formulated by considering the single-electron tunneling rate, instead of the Kramers' rate generally used in conventional SR models. The model indicates that the response of the system is maximized at finite temperature and that the peak position is determined by the charging energy. This model quantitatively reproduces the results of a single-electron device simulator. Single-electron SR is also demonstrated using a GaAs-based single-electron system that integrates a quantum dot and a high-sensitivity charge detector. The developed model will contribute to our understanding of single-electron SR and will facilitate accurate prediction, design, and control of single-electron systems.

Intrinsic Noise Improves Speech Recognition in a Computational Model of the Auditory Pathway

Noise is generally considered to harm information processing performance. However, in the context of stochastic resonance, noise has been shown to improve signal detection of weak sub- threshold signals, and it has been proposed that the brain might actively exploit this phenomenon. Especially within the auditory system, recent studies suggest that intrinsic noise plays a key role in signal processing and might even correspond to increased spontaneous neuronal firing rates observed in early processing stages of the auditory brain stem and cortex after hearing loss. Here we present a computational model of the auditory pathway based on a deep neural network, trained on speech recognition. We simulate different levels of hearing loss and investigate the effect of intrinsic noise. Remarkably, speech recognition after hearing loss actually improves with additional intrinsic noise. This surprising result indicates that intrinsic noise might not only play a crucial role in human auditory processing, but might even be beneficial for contemporary machine learning approaches.

Noise helps cochlear implant listeners to categorize vowels

Theoretical studies demonstrate that controlled addition of noise can enhance the amount of information transmitted by a cochlear implant (CI). The present study is a proof-of-principle for whether stochastic facilitation can improve the ability of CI users to categorize speech sounds. Analogue vowels were presented to CI users through a single electrode with independent noise on multiple electrodes. Noise improved vowel categorization, particularly in terms of an increase in information conveyed by the first and second formant. Noise, however, did not significantly improve vowel recognition: the miscategorizations were just more consistent, giving the potential to improve with experience.

White-noise-induced double coherence resonances in reduced Hodgkin-Huxley neuron model near subcritical Hopf bifurcation

Coherence resonance (CR) describes a counterintuitive phenomenon in which the optimal oscillatory responses in nonlinear systems are shaped by a suitable noise amplitude. This phenomenon has been observed in neural systems. In this research, the generation of double coherence resonances (DCRs) due to white noise is investigated in a three-dimensional reduced Hodgkin-Huxley neuron model with multiple-timescale feature. We show that additive white noise can induce DCRs from the resting state near a subcritical Hopf bifurcation. The appearance of DCRs is related to the changes of the firing pattern aroused by the increases of the noise amplitude. The underlying dynamical mechanisms for the appearance of the DCRs and the changes of the firing pattern are interpreted using the phase space analysis and the dynamics of the stable focus-node near the subcritical Hopf bifurcation. We find that the multiple-timescale dynamics is essential for generating the DCRs and different firing patterns. The results not only present a case in which noise can induce DCRs near a Hopf bifurcation but also provide its dynamical mechanism, which enriches the phenomena in nonlinear dynamics and provides further understanding on the roles of noise in neural systems with multiple-timescale feature.

Stochastic Resonance in Organic Electronic Devices

Stochastic Resonance (SR) is a phenomenon in which noise improves the performance of a system. With the addition of noise, a weak input signal to a nonlinear system, which may exceed its threshold, is transformed into an output signal. In the other words, noise-driven signal transfer is achieved. SR has been observed in nonlinear response systems, such as biological and artificial systems, and this review will focus mainly on examples of previous studies of mathematical models and experimental realization of SR using poly(hexylthiophene)-based organic field-effect transistors (OFETs). This phenomenon may contribute to signal processing with low energy consumption. However, the generation of SR requires a noise source. Therefore, the focus is on OFETs using materials such as organic materials with unstable electrical properties and critical elements due to unidirectional signal transmission, such as neural synapses. It has been reported that SR can be observed in OFETs by application of external noise. However, SR does not occur under conditions where the input signal exceeds the OFET threshold without external noise. Here, we present an example of a study that analyzes the behavior of SR in OFET systems and explain how SR can be made observable. At the same time, the role of internal noise in OFETs will be explained.

Emergence of stochastic resonance in a two-compartment hippocampal pyramidal neuron model

In vitro studies have shown that hippocampal pyramidal neurons employ a mechanism similar to stochastic resonance (SR) to enhance the detection and transmission of weak stimuli generated at distal synapses. To support the experimental findings from the perspective of multicompartment model analysis, this paper aimed to elucidate the phenomenon of SR in a noisy two-compartment hippocampal pyramidal neuron model, which was a variant of the Pinsky-Rinzel neuron model with smooth activation functions and a hyperpolarization-activated cation current. With a bifurcation analysis of the model, we demonstrated the underlying dynamical structure responsible for the occurrence of SR. Furthermore, using a stochastically generated biphasic pulse train and broadband noise generated by the Orenstein-Uhlenbeck process as noise perturbation, both SR and suprathreshold SR were observed and quantified. Spectral analysis revealed that the distribution of spectral power under noise perturbations, in addition to inherent neurodynamics, is the main factor affecting SR behavior. The research results suggested that noise enhances the transmission of weak stimuli associated with elongated dendritic structures of hippocampal pyramidal neurons, thereby providing support for related laboratory findings.

Parallel Processing of Olfactory and Mechanosensory Information in the Honey Bee Antennal Lobe

In insects, neuronal responses to clean air have so far been reported only episodically in moths. Here we present results obtained by fast two-photon calcium imaging in the honey bee Apis mellifera, indicating a substantial involvement of the antennal lobe, the first olfactory neuropil, in the processing of mechanical stimuli. Clean air pulses generate a complex pattern of glomerular activation that provides a code for stimulus intensity and dynamics with a similar level of stereotypy as observed for the olfactory code. Overlapping the air pulses with odor stimuli reveals a superposition of mechanosensory and odor response codes with high contrast. On the mechanosensitive signal, modulations were observed in the same frequency regime as the oscillatory motion of the antennae, suggesting a possible way to detect odorless airflow directions. The transduction of mechanosensory information via the insect antennae has so far been attributed primarily to Johnston's organ in the pedicel of the antenna. The possibility that the antennal lobe activation by clean air originates from Johnston's organ could be ruled out, as the signal is suppressed by covering the surfaces of the otherwise freely moving and bending antennae, which should leave Johnston's organ unaffected. The tuning curves of individual glomeruli indicate increased sensitivity at low-frequency mechanical oscillations as produced by the abdominal motion in waggle dance communication, suggesting a further potential function of this mechanosensory code. The discovery that the olfactory system can sense both odors and mechanical stimuli has recently been made also in mammals. The results presented here give hope that studies on insects can make a fundamental contribution to the cross-taxa understanding of this dual function, as only a few thousand neurons are involved in their brains, all of which are accessible by in vivo optical imaging.

Analytical solution of stochastic resonance in the nonadiabatic regime

We generalize stochastic resonance to the nonadiabatic limit by treating the double-well potential using two quadratic potentials. We use a singular perturbation method to determine an approximate analytical solution for the probability density function that asymptotically connects local solutions in boundary layers near the two minima with those in the region of the maximum that separates them. The validity of the analytical solution is confirmed numerically. Free from the constraints of the adiabatic limit, the approach allows us to predict the escape rate from one stable basin to another for systems experiencing a more complex periodic forcing.

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.

Demonstration of Stochastic Resonance, Population Coding, and Population Voting Using Artificial MoS2 Based Synapses

Fast detection of weak signals at low energy expenditure is a challenging but inescapable task for the evolutionary success of animals that survive in resource constrained environments. This task is accomplished by the sensory nervous system by exploiting the synergy between three astounding neural phenomena, namely, stochastic resonance (SR), population coding (PC), and population voting (PV). In SR, the constructive role of synaptic noise is exploited for the detection of otherwise invisible signals. In PC, the redundancy in neural population is exploited to reduce the detection latency. Finally, PV ensures unambiguous signal detection even in the presence of excessive noise. Here we adopt a similar strategies and experimentally demonstrate how a population of stochastic artificial neurons based on monolayer MoS₂ field effect transistors (FETs) can use an optimum amount of white Gaussian noise and population voting to detect invisible signals at a frugal energy expenditure (∼10s of nano-Joules). Our findings can aid remote sensing in the emerging era of the Internet of things (IoT) that thrive on energy efficiency.

Stochastic and sinusoidal electrical stimuli increase the irregularity and gain of Type A and B medial vestibular nucleus neurons, in vitro

Galvanic vestibular stimulation (GVS) has been shown to improve vestibular function potentially via stochastic resonance, however, it remains unknown how central vestibular nuclei process these signals. In vivo work applying electrical stimuli to the vestibular apparatus of animals has shown changes in neuronal discharge at the level of the primary vestibular afferents and hair cells. This study aimed to determine the cellular impacts of stochastic, sinusoidal, and stochastic + sinusoidal stimuli on individual medial vestibular nucleus (MVN) neurons of male and female C57BL/6 mice. All stimuli increased the irregularity of MVN neuronal discharge, while differentially affecting neuronal gain. This suggests that the heterogeneous MVN neuronal population (marked by differential expression of ion channels), may influence the impact of electrical stimuli on neuronal discharge. Neuronal subtypes showed increased variability of neuronal firing, where Type A and B neurons experienced the largest gain changes in response to stochastic and sinusoidal stimuli. Type C neurons were the least affected regarding neuronal firing variability and gain changes. The membrane potential (MP) of neurons was altered by sinusoidal and stochastic + sinusoidal stimuli, with Type B and C neuronal MP significantly affected. These results indicate that GVS-like electrical stimuli impact MVN neuronal discharge differentially, likely as a result of heterogeneous ion channel expression.

Stochastic and vibrational resonance in complex networks of neurons

The concept of resonance in nonlinear systems is crucial and traditionally refers to a specific realization of maximum response provoked by a particular external perturbation. Depending on the system and the nature of perturbation, many different resonance types have been identified in various fields of science. A prominent example is in neuroscience where it has been widely accepted that a neural system may exhibit resonances at microscopic, mesoscopic and macroscopic scales and benefit from such resonances in various tasks. In this context, the two well-known forms are stochastic and vibrational resonance phenomena which manifest that detection and propagation of a feeble information signal in neural structures can be enhanced by additional perturbations via these two resonance mechanisms. Given the importance of network architecture in proper functioning of the nervous system, we here present a review of recent studies on stochastic and vibrational resonance phenomena in neuronal media, focusing mainly on their emergence in complex networks of neurons as well as in simple network structures that represent local behaviours of neuron communities. From this perspective, we aim to provide a secure guide by including theoretical and experimental approaches that analyse in detail possible reasons and necessary conditions for the appearance of stochastic resonance and vibrational resonance in neural systems. This article is part of the theme issue 'Vibrational and stochastic resonance in driven nonlinear systems (part 2)'.

Aperiodic stochastic resonance in neural information processing with Gaussian colored noise

The aim of this paper is to explore the phenomenon of aperiodic stochastic resonance in neural systems with colored noise. For nonlinear dynamical systems driven by Gaussian colored noise, we prove that the stochastic sample trajectory can converge to the corresponding deterministic trajectory as noise intensity tends to zero in mean square, under global and local Lipschitz conditions, respectively. Then, following forbidden interval theorem we predict the phenomenon of aperiodic stochastic resonance in bistable and excitable neural systems. Two neuron models are further used to verify the theoretical prediction. Moreover, we disclose the phenomenon of aperiodic stochastic resonance induced by correlation time and this finding suggests that adjusting noise correlation might be a biologically more plausible mechanism in neural signal processing.

Stochastic resonance in MoS2 photodetector

In this article, we adopt a radical approach for next generation ultra-low-power sensor design by embracing the evolutionary success of animals with extraordinary sensory information processing capabilities that allow them to survive in extreme and resource constrained environments. Stochastic resonance (SR) is one of those astounding phenomena, where noise, which is considered detrimental for electronic circuits and communication systems, plays a constructive role in the detection of weak signals. Here, we show SR in a photodetector based on monolayer MoS₂ for detecting ultra-low-intensity subthreshold optical signals from a distant light emitting diode (LED). We demonstrate that weak periodic LED signals, which are otherwise undetectable, can be detected by a MoS₂ photodetector in the presence of a finite and optimum amount of white Gaussian noise at a frugal energy expenditure of few tens of nano-Joules. The concept of SR is generic in nature and can be extended beyond photodetector to any other sensors.

Here, the authors take advantage of stochastic resonance in a photodetector based on monolayer MoS₂ for measuring otherwise undetectable, ultra-low-intensity, subthreshold optical signals from a distant light emitting diode in the presence of a finite and optimum amount of white Gaussian noise.

Talin folding as the tuning fork of cellular mechanotransduction

Cells continually sample their mechanical environment using exquisite force sensors such as talin, whose folding status triggers mechanotransduction pathways by recruiting binding partners. Mechanical signals in biology change quickly over time and are often embedded in noise; however, the mechanics of force-sensing proteins have only been tested using simple force protocols, such as constant or ramped forces. Here, using our magnetic tape head tweezers design, we measure the folding dynamics of single talin proteins in response to external mechanical noise and cyclic force perturbations. Our experiments demonstrate that talin filters out external mechanical noise but detects periodic force signals over a finely tuned frequency range. Hence, talin operates as a mechanical band-pass filter, able to read and interpret frequency-dependent mechanical information through its folding dynamics. We describe our observations in the context of stochastic resonance, which we propose as a mechanism by which mechanosensing proteins could respond accurately to force signals in the naturally noisy biological environment.

Coherence resonance for neuronal bursting with spike undershoot

Although the bursting patterns with spike undershoot are involved with the achievement of physiological or cognitive functions of brain with synaptic noise, noise induced-coherence resonance (CR) from resting state or subthreshold oscillations instead of bursting has been widely identified to play positive roles in information process. Instead, in the present paper, CR characterized by the increase firstly and then decease of peak value of power spectrum of spike trains is evoked from a bursting pattern with spike undershoot, which means that the minimal membrane potential within burst is lower than that of the subthreshold oscillations between bursts, while CR cannot be evoked from the bursting pattern without spike undershoot. With bifurcations and fast-slow variable dissection method, the bursting patterns with and without spike undershoot are classified into "Sub-Hopf/Fold" bursting and "Fold/Homoclinic" bursting, respectively. For the bursting with spike undershoot, the trajectory of the subthreshold oscillations is very close to that of the spikes within burst. Therefore, noise can induce more spikes from the subthreshold oscillations and modulate the bursting regularity, which leads to the appearance of CR. For the bursting pattern without spike undershoot, the trajectory of the quiescent state is not close to that of the spikes within burst, and noise cannot induce spikes from the quiescent state between bursts, which is cause for non-CR. The result provides a novel case of CR phenomenon and extends the scopes of CR concept, presents that noise can enhance rather than suppress information of the bursting patterns with spike undershoot, which are helpful for understanding the dynamics and the potential physiological or cognitive functions of the nerve fiber or brain neurons with such bursting patterns.

Stochastic Resonance Based Visual Perception Using Spiking Neural Networks

Our aim is to propose an efficient algorithm for enhancing the contrast of dark images based on the principle of stochastic resonance in a global feedback spiking network of integrate-and-fire neurons. By linear approximation and direct simulation, we disclose the dependence of the peak signal-to-noise ratio on the spiking threshold and the feedback coupling strength. Based on this theoretical analysis, we then develop a dynamical system algorithm for enhancing dark images. In the new algorithm, an explicit formula is given on how to choose a suitable spiking threshold for the images to be enhanced, and a more effective quantifying index, the variance of image, is used to replace the commonly used measure. Numerical tests verify the efficiency of the new algorithm. The investigation provides a good example for the application of stochastic resonance, and it might be useful for explaining the biophysical mechanism behind visual perception.

Ionic channel blockage in stochastic Hodgkin-Huxley neuronal model driven by multiple oscillatory signals

Ionic channel blockage and multiple oscillatory signals play an important role in the dynamical response of pulse sequences. The effects of ionic channel blockage and ionic channel noise on the discharge behaviors are studied in Hodgkin-Huxley neuronal model with multiple oscillatory signals. It is found that bifurcation points of spontaneous discharge are altered through tuning the amplitude of multiple oscillatory signals, and the discharge cycle is changed by increasing the frequency of multiple oscillatory signals. The effects of ionic channel blockage on neural discharge behaviors indicate that the neural excitability can be suppressed by the sodium channel blockage, however, the neural excitability can be reversed by the potassium channel blockage. There is an optimal blockage ratio of potassium channel at which the electrical activity is the most regular, while the order of neural spike is disrupted by the sodium channel blockage. In addition, the frequency of spike discharge is accelerated by increasing the ionic channel noise, the firing of neuron becomes more stable if the ionic channel noise is appropriately reduced. Our results might provide new insights into the effects of ionic channel blockages, multiple oscillatory signals, and ionic channel noises on neural discharge behaviors.

SIGNAL-TO-NOISE RATIO GAIN VIA CORRELATED NOISE IN AN ENSEMBLE OF NOISY NEURONS

The collective response of an ensemble of leaky integrate-and-fire neurons induced by local correlated noise is investigated theoretically. Based on the linear response theory, we derive the analytic expression of signal-to-noise ratio (SNR). Numerical results show that the amplitude of internal noise can be increased up to an optimal value where the output SNR reaches a maximum value. Interestingly, we find that the correlated noise between the nearest neurons could lead to the obvious SNR gain. We also show that the SNR can reach unity under condition that the correlated noise between the nearest neurons is negative. This nonlinear amplification of SNR gain in an ensemble of noisy neurons can be related to the array stochastic resonance (SR) phenomenon. Furthermore, we also show that the SNR gain can also be optimized by tuning the number of neuron units, frequency and amplitude of the weak periodic signal.

Resonance induced by coupling diversity in globally coupled bistable oscillators

We investigate the collective response of an ensemble of bistable oscillators to an external periodic signal, where the coupling strength between oscillators is diverse. We find that there exists an optimal level of coupling diversity, at which the collective response of the system can be largely improved, i.e., resonance induced by coupling diversity. We also observe that the system splits into three oscillation clusters when this resonance happens. We finally propose a reduced model based on the three oscillation clusters, which can well predict the collective response of the system.

New Type of Spectral Nonlinear Resonance Enhances Identification of Weak Signals

Some nonlinear systems possess innate capabilities of enhancing weak signal transmissions through a unique process called Stochastic Resonance (SR). However, existing SR mechanism suffers limited signal enhancement from inappropriate entraining signals. Here we propose a new and effective implementation, resulting in a new type of spectral resonance similar to SR but capable of achieving orders of magnitude higher signal enhancement than previously reported. By employing entraining frequency in the range of the weak signal, strong spectral resonances can be induced to facilitate nonlinear modulations and intermodulations, thereby strengthening the weak signal. The underlying physical mechanism governing the behavior of spectral resonances is examined, revealing the inherent advantages of the proposed spectral resonances over the existing implementation of SR. Wide range of parameters have been found for the optimal enhancement of any given weak signal and an analytical method is established to estimate these required parameters. A reliable algorithm is also developed for the identifications of weak signals using signal processing techniques. The present work can significantly improve existing SR performances and can have profound practical applications where SR is currently employed for its inherent technological advantages.

Robustness against additional noise in cellular information transmission

Fluctuations in intracellular reactions (intrinsic noise) reduce the information transmitted from an extracellular input to a cellular response. However, recent studies have demonstrated that the decrease in the transmitted information with respect to extracellular input fluctuations (extrinsic noise) is smaller when the intrinsic noise is larger. Therefore, it has been suggested that robustness against extrinsic noise increases with the level of the intrinsic noise. We call this phenomenon intrinsic noise-induced robustness (INIR). As previous studies on this phenomenon have focused on complex biochemical reactions, the relation between INIR and the input-output of a system is unclear. Moreover, the mechanism of INIR remains elusive. In this paper, we address these questions by analyzing simple models. We first analyze a model in which the input-output relation is linear. We show that the robustness against extrinsic noise increases with the intrinsic noise, confirming the INIR phenomenon. Moreover, the robustness against the extrinsic noise is more strongly dependent on the intrinsic noise when the variance of the intrinsic noise is larger than that of the input distribution. Next, we analyze a threshold model in which the output depends on whether the input exceeds the threshold. When the threshold is equal to the mean of the input, INIR is realized, but when the threshold is much larger than the mean, the threshold model exhibits stochastic resonance, and INIR is not always apparent. The robustness against extrinsic noise and the transmitted information can be traded off against one another in the linear model and the threshold model without stochastic resonance, whereas they can be simultaneously increased in the threshold model with stochastic resonance.

Mechanics to pre-process information for the fine tuning of mechanoreceptors

Non-nervous auxiliary structures play a significant role in sensory biology. They filter the stimulus and transform it in a way that fits the animal's needs, thereby contributing to the avoidance of the central nervous system's overload with meaningless stimuli and a corresponding processing task. The present review deals with mechanoreceptors mainly of invertebrates and some remarkable recent findings stressing the role of mechanics as an important source of sensor adaptedness, outstanding performance, and diversity. Instead of organizing the review along the types of stimulus energy (force) taken up by the sensors, processes associated with a few basic and seemingly simple mechanical principles like lever systems, viscoelasticity, resonance, traveling waves, and impedance matching are taken as the guideline. As will be seen, nature makes surprisingly competent use of such "simple mechanics".

Phase Diagram and Optimal Information Use in a Collective Sensing System

We consider a population of individuals living in an uncertain environment. Individuals are able to make noisy observations of the environment and communicate using signals. We show that the model shows an order-disorder transition from an ordered phase in low communication noise in which a consensus about the environmental state is formed to a disordered phase in high communication noise in which no consensus is formed. There are different consensus states: informed consensus in which consensus on the correct belief about the environmental state is formed, and misinformed consensus in which consensus on a wrong belief is formed. Based on the consensus state reached, the ordered phase is decomposed into multistable states separated by first order transitions. We show that the inference capability of the population in a changing environment is maximized on the edge of bistability: on the border between an informed consensus phase and a bistable phase in which both informed and misinformed consensuses are stable. In addition, we show that an optimal level of noise in communication increases the responsiveness of the population to environmental changes in a resonancelike phenomenon. Furthermore, the beneficial effect of noise is the most crucial in a fast changing environment.

Extraction of Bridge Fundamental Frequencies Utilizing a Smartphone MEMS Accelerometer

Smartphone MEMS (Micro Electrical Mechanical System) accelerometers have relatively low sensitivity and high output noise density. Therefore, it cannot be directly used to track feeble vibrations such as structural vibrations. This article proposes an effective increase in the sensitivity of the smartphone accelerometer utilizing the stochastic resonance (SR) phenomenon. SR is an approach where, counter-intuitively, feeble signals are amplified rather than overwhelmed by the addition of noise. This study introduces the 2D-frequency independent underdamped pinning stochastic resonance (2D-FI-UPSR) technique, which is a customized SR filter that enables identifying the frequencies of weak signals. To validate the feasibility of the proposed SR filter, an iPhone device is used to collect bridge acceleration data during normal traffic operation and the proposed 2D-FI-UPSR filter is used to process these data. The first four fundamental bridge frequencies are successfully identified from the iPhone data. In parallel to the iPhone, a highly sensitive wireless sensing network consists of 15 accelerometers (Silicon Designs accelerometers SDI-2012) is installed to validate the accuracy of the extracted frequencies. The measurement fidelity of the iPhone device is shown to be consistent with the wireless sensing network data with approximately 1% error in the first three bridge frequencies and 3% error in the fourth frequency.

Pattern selection and regulation using noise in a liquid metal

Electric forcing can be used to select and to regulate the shape of liquid metals. In this work, we present a transition among different patterns in a liquid mercury drop regulated by noise. A stochastic resonancelike phenomenon was observed for two different structural transitions of the liquid metal. In the first set of experiments, the transition from irregular (I) → triangular (T) → irregular (I) patterns was obtained by increasing the amplitude of biased white noise. In the second part, we observed the transition from irregular (I) → elliptical (E) → irregular (I) patterns using the same kind of noise. Periodic stochastic resonance was corroborated in our experiments by employing the cross-correlation coefficient technique.

High-Frequency Stochastic Switching of Graphene Resonators Near Room Temperature

Stochastic switching between the two bistable states of a strongly driven mechanical resonator enables detection of weak signals based on probability distributions, in a manner that mimics biological systems. However, conventional silicon resonators at the microscale require a large amount of fluctuation power to achieve a switching rate in the order of a few hertz. Here, we employ graphene membrane resonators of atomic thickness to achieve a stochastic switching rate of 4.1 kHz, which is 100 times faster than current state-of-the-art. The (effective) temperature of the fluctuations is approximately 400 K, which is 3000 times lower than the state-of-the-art. This shows that these membranes are potentially useful to transduce weak signals in the audible frequency domain. Furthermore, we perform numerical simulations to understand the transition dynamics of the resonator and use analytical expressions to investigate the relevant scaling parameters that allow high-frequency, low-temperature stochastic switching to be achieved in mechanical resonators.

Random pulse induced synchronization and resonance in uncoupled non-identical neuron models

Random pulses contribute to stochastic resonance in neuron models, whereas common random pulses cause stochastic-synchronized excitation in uncoupled neuron models. We studied concurrent phenomena contributing to phase synchronization and stochastic resonance following induction by a weak common random pulse in uncoupled non-identical Hodgkin-Huxley type neuron models. The common random pulse was selected from a gamma distribution and the degree of synchronization depended on the corresponding shape parameter. Specifically, a low shape parameter of the weak random pulse induced well-synchronized spiking in uncoupled neuron models, whereas a high shape parameter of the weak random pulse or a weak periodic pulse caused low degrees of synchronization. These were improved by concurrent inputs of periodic and random pulses with high shape parameters. Finally, the output pulse was synchronized with the periodic pulse, and the common random pulse revealed periodic responses in the present neuron models.

Stochastic resonance improves visuomotor temporal integration in healthy young adults

Mechanical and electrical noise stimulation to the body is known to improve the sensorimotor system. This improvement is related to stochastic resonance (SR), a phenomenon described as a "noise benefit" to various sensory and motor systems. The current study investigated the influence of SR on visuomotor temporal integration and hand motor function under delayed visual feedback in healthy young adults. The purpose of this study was to measure the usefulness of SR as a neurorehabilitation device for disorders of visuomotor temporal integration. Thirty healthy volunteers underwent detection tasks and hand motor function tests under delayed visual feedback, with or without SR. Of the 30 participants, 15 carried out the tasks under delayed visual feedback in the order of SR on-condition, off-condition, off-condition, and on-condition. The remaining 15 participants conducted the experimental tasks in the order of SR off-condition, on-condition, on-condition, and off-condition. Comparisons of the delay detection threshold (DDT), steepness of the delay detection probability curves, box and block test (BBT) scores, and nine-hole peg test (NHPT) scores between the SR on- and off-conditions were performed. The DDT under the SR on-condition was significantly shortened compared with the SR off-condition. There was no significant difference between the SR on- and off-conditions for the steepness of the delay detection probability curves, BBT scores, and NHPT scores. SR improved visuomotor temporal integration in healthy young adults, and may therefore improve movement disorders in patients with impaired visuomotor temporal integration. However, because the current results showed that SR did not improve hand motor function under delayed visual feedback, it may not improve motor function when a large distortion of visuomotor temporal integration is present. Further studies are required considering several limitations of the current study, and future clinical trials are necessary to verify the effects of motor training using SR for the treatment of visuomotor temporal integration disorders.

Drive-By Bridge Frequency Identification under Operational Roadway Speeds Employing Frequency Independent Underdamped Pinning Stochastic Resonance (FI-UPSR)

Recently, drive-by bridge inspection has attracted increasing attention in the bridge monitoring field. A number of studies have given confidence in the feasibility of the approach to detect, quantify, and localize damages. However, the speed of the inspection truck represents a major obstacle to the success of this method. High speeds are essential to induce a significant amount of kinetic energy to stimulate the bridge modes of vibration. On the other hand, low speeds are necessary to collect more data and to attenuate the vibration of the vehicle due to the roughness of the road and, hence, magnify the bridge influence on the vehicle responses. This article introduces Frequency Independent Underdamped Pinning Stochastic Resonance (FI-UPSR) as a new technique, which possesses the ability to extract bridge dynamic properties from the responses of a vehicle that passes over the bridge at high speed. Stochastic Resonance (SR) is a phenomenon where feeble information such as weak signals can be amplified through the assistance of background noise. In this study, bridge vibrations that are present in the vehicle responses when it passes over the bridge are the feeble information while the noise counts for the effect of the road roughness on the vehicle vibration. UPSR is one of the SR models that has been chosen in this study for its suitability to extract the bridge vibration. The main contributions of this article are: (1) introducing a Frequency Independent-Stochastic Resonance model known as the FI-UPSR and (2) implementing this model to extract the bridge vibration from the responses of a fast passing vehicle.

A proposal to explain how the circatidal rhythm of the Arabidopsis thaliana root elongation rate could be mediated by the lunisolar gravitational force: a quantum physical approach

Background and Aims

Roots of Arabidopsis thaliana exhibit a 24.8 h oscillation of elongation rate when grown under free-running conditions. This growth rhythm is synchronized with the time course of the local lunisolar tidal acceleration. The present study aims at a physiological/physical model to describe the interaction of weak gravitational fields with cellular water dynamics that mediate rhythmic root growth profiles.

Methods

Fundamental physical laws are applied to model the water dynamics within single plant cells in an attempt to mimic the 24.8 h oscillations in root elongation growth. In particular, a quantum gravitational description of the time course in root elongation is presented, central to which is the formation of coherent assemblies of mass due to the lunisolar gravitational field. Mathematical equations that characterize lunisolar gravity-induced coherent assemblies of water molecules are derived and related to the mass of cellular water within roots of A. thaliana.

Key Results

The derived physical model of gravitationally modulated water assemblies is capable of accounting for the experimentally observed arabidopsis root growth kinetics under free-running conditions. The close analogy between the derived time-dependent lunisolar effect upon coherent molecular states of water within single cells and the coherent assemblies of electrons that characterize the quantum Hall effect is emphasized.

Conclusions

The dynamics of the lunisolar-induced variation in coherent water assemblies provide a possible mechanism to describe the observed 24.8 h oscillation of root growth rate of A. thaliana. Therefore, this mechanism could function as an independent timekeeper to control cell elongation.

Nonlinearization: naturalistic stimulation and nonlinear dynamic behavior in a spider mechanoreceptor

In a previous study, we used linear frequency response analysis to show that naturalistic stimulation of spider primary mechanosensory neurons produced different response dynamics than the commonly used Gaussian random noise. We isolated this difference to the production of action potentials from receptor potential and suggested that the different distribution of frequency components in the naturalistic signal increased the nonlinearity of action potential encoding. Here, we tested the relative contributions of first- and second-order processes to the action potential signal by measuring linear and quadratic coherence functions. Naturalistic stimulation shifted the linear coherence toward lower frequencies, while quadratic coherence was always higher than linear coherence and increased with naturalistic stimulation. In an initial attempt to separate the order of time-dependent and nonlinear processes, we fitted quadratic frequency response functions by two block-structured models consisting of a power-law filter and a static second-order nonlinearity in alternate cascade orders. The same cascade models were then fitted to the original time domain data by conventional numerical analysis algorithms, using a polynomial function as the static nonlinearity. Quadratic models with a linear filter followed by a static nonlinearity were favored over the reverse order, but with weak significance. Polynomial nonlinear functions indicated that rectification is a major nonlinearity. A complete quantitative description of sensory encoding in these primary mechanoreceptors remains elusive but clearly requires quadratic and higher nonlinear operations on the input signal to explain the sensitivity of dynamic behavior to different input signal patterns.

Improvement of signal-to-noise ratio in parallel neuron arrays with spatially nearest neighbor correlated noise

We theoretically investigate the signal-to-noise ratio (SNR) of a parallel array of leaky integrate-and-fire (LIF) neurons that receives a weak periodic signal and uses spatially nearest neighbor correlated noise. By using linear response theory, we derive the analytic expression of the SNR. The results show that the amplitude of internal noise can be increased up to an optimal value, which corresponds to a maximum SNR. Given the existence of spatially nearest neighbor correlated noise in the neural ensemble, the SNR gain of the collective ensemble response can exceed unity, especially for a negative correlation. This nonlinear collective phenomenon of SNR gain amplification may be related to the array stochastic resonance. In addition, we show that the SNR can be improved by varying the number of neurons, frequency, and amplitude of the weak periodic signal. We expect that this investigation will be useful for both controlling the collective response of neurons and enhancing weak signal transmission.