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

Manipulating conductivity and noise for transitioning between stochastic and inverse stochastic resonances in liquid-crystal electroconvection

Noise can play a constructive role in nature and various engineering systems. Over the past four decades, noise-induced stochastic resonances (SRs) have been extensively documented, showing enhancement in system performance. Additionally, inverse SR has been observed in various systems. Typically, these resonances were studied independently. A transition between these resonances was recently observed in an alternating current-driven liquid-crystal electroconvection (EC) system using combined amplitude and phase noises. This study uses internal (material) and external (noise) parameters to demonstrate the control of this transition. Specifically, the nonmonotonic threshold voltage behavior of the EC system, indicative of the resonances, was numerically examined using additional parameters. Experimental tests were conducted to confirm the effects of these parameters. The findings reveal that the transition between these resonances can be systematically controlled to meet specific needs, whether desirable or undesirable system performances. Notably, this study illustrates how to modify the behavior of both resonances in colored noise by adjusting its cutoff frequency and steepness and phase noise, which is often overlooked. Moreover, this study provides valuable insights for various noise-related applications.

Behavioural stochastic resonance across the lifespan

Stochastic resonance (SR) is the phenomenon wherein the introduction of a suitable level of noise enhances the detection of subthreshold signals in non linear systems. It manifests across various physical and biological systems, including the human brain. Psychophysical experiments have confirmed the behavioural impact of stochastic resonance on auditory, somatic, and visual perception. Aging renders the brain more susceptible to noise, possibly causing differences in the  SR phenomenon between young and elderly individuals. This study investigates the impact of noise on motion detection accuracy throughout the lifespan, with 214 participants ranging in age from 18 to 82. Our objective was to determine the optimal noise level to induce an SR-like response in both young and old populations. Consistent with existing literature, our findings reveal a diminishing advantage with age, indicating that the efficacy of noise addition progressively diminishes. Additionally, as individuals age, peak performance is achieved with lower levels of noise. This study provides the first insight into how SR changes across the lifespan of healthy adults and establishes a foundation for understanding the pathological alterations in perceptual processes associated with aging.

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.

Transcranial Electrical Stimulation: Clinical Implication and Practice for Treatment of Psychiatric Illness

Brain electrical stimulation, particularly non-invasive brain stimulation (NIBS) techniques such as transcranial electrical stimulation (tES), have emerged as a promising treatment for various psychiatric disorders, including depression, anxiety, and post-traumatic stress disorder. tES techniques, such as transcranial direct current stimulation (tDCS), transcranial alternating current stimulation (tACS), and transcranial random noise stimulation (tRNS), are cost-effective and safe interventions that are designed to affect neuronal circuits in the brain using various modalities. Although tES has shown effectiveness in the treatment of psychiatric disorders, there is a lack of comprehensive papers that consider its clinical implications. Therefore, this review aims to evaluate the clinical implications of tES and provide practical guidance for the treatment of psychiatric illnesses. Moreover, this review provides an overview of tES techniques and their mechanisms of action and summarizes recent clinical studies that have examined the use of tES for psychiatric disorders.

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.

A novel memristive neuron model and its energy characteristics

The functional neurons are basic building blocks of the nervous system and are responsible for transmitting information between different parts of the body. However, it is less known about the interaction between the neuron and the field. In this work, we propose a novel functional neuron by introducing a flux-controlled memristor into the FitzHugh-Nagumo neuron model, and the field effect is estimated by the memristor. We investigate the dynamics and energy characteristics of the neuron, and the stochastic resonance is also considered by applying the additive Gaussian noise. The intrinsic energy of the neuron is enlarged after introducing the memristor. Moreover, the energy of the periodic oscillation is larger than that of the adjacent chaotic oscillation with the changing of memristor-related parameters, and same results is obtained by varying stimuli-related parameters. In addition, the energy is proved to be another effective method to estimate stochastic resonance and inverse stochastic resonance. Furthermore, the analog implementation is achieved for the physical realization of the neuron. These results shed lights on the understanding of the firing mechanism for neurons detecting electromagnetic field.

Cycling and spiral-wave modes in an active cyclic Potts model

We studied the nonequilibrium dynamics of a cycling three-state Potts model using simulations and theory. This model can be tuned from thermal-equilibrium to far-from-equilibrium conditions. At low cycling energy, the homogeneous dominant state cycles via nucleation and growth, while spiral waves are formed at high energy. For large systems, a discontinuous transition occurs from these cyclic homogeneous phases to spiral waves, while the opposite transition is absent. Conversely, these two modes can coexist for small systems. The waves can be reproduced by a continuum theory, and the transition can be understood from the competition between nucleation and growth.

Network motifs in cellular neurophysiology

Concepts from network science and graph theory, including the framework of network motifs, have been frequently applied in studying neuronal networks and other biological complex systems. Network-based approaches can also be used to study the functions of individual neurons, where cellular elements such as ion channels and membrane voltage are conceptualized as nodes within a network, and their interactions are denoted by edges. Network motifs in this context provide functional building blocks that help to illuminate the principles of cellular neurophysiology. In this review we build a case that network motifs operating within neurons provide tools for defining the functional architecture of single-neuron physiology and neuronal adaptations. We highlight the presence of such computational motifs in the cellular mechanisms underlying action potential generation, neuronal oscillations, dendritic integration, and neuronal plasticity. Future work applying the network motifs perspective may help to decipher the functional complexities of neurons and their adaptation during health and disease.

Multifractal perturbations to multiplicative cascades promote multifractal nonlinearity with asymmetric spectra

Biological and psychological processes have been conceptualized as emerging from intricate multiplicative interactions among component processes across various spatial and temporal scales. Among the statistical models employed to approximate these intricate nonlinear interactions across scales, one prominent framework is that of cascades. Despite decades of empirical work using multifractal formalisms, several fundamental questions persist concerning the proper interpretations of multifractal evidence of nonlinear cross-scale interactivity. Does multifractal spectrum width depend on multiplicative interactions, constituent noise processes participating in those interactions, or both? We conducted numerical simulations of cascade time series featuring component noise processes characterizing a range of nonlinear temporal correlations: nonlinearly multifractal, linearly multifractal (obtained via the iterative amplitude adjusted wavelet transform of nonlinearly multifractal), phase-randomized linearity (obtained via the iterative amplitude adjustment Fourier transform of nonlinearly multifractal), and phase and amplitude randomized (obtained via shuffling of nonlinearly multifractal). Our findings show that the multiplicative interactions coordinate with the nonlinear temporal correlations of noise components to dictate emergent multifractal properties. Multiplicative cascades with stronger nonlinear temporal correlations make multifractal spectra more asymmetric with wider left sides. However, when considering multifractal spectral differences between the original and surrogate time series, even multiplicative cascades produce multifractality greater than in surrogate time series, even with linearized multifractal noise components. In contrast, additivity among component processes leads to a linear outcome. These findings provide a robust framework for generating multifractal expectations for biological and psychological models in which cascade dynamics flow from one part of an organism to another.

Multi-Type Stochastic Resonances for Noise-Enhanced Mechanical, Optical, and Acoustic Sensing

Stochastic resonance (SR) typically manifests in nonlinear systems, wherein the detection of a weak signal is bolstered by the addition of noise. Since its first discovery in a study of ice ages on Earth, various types of SRs have been observed in biological and physical systems and have been implemented in sensors to benefit from noise. However, a universally designed sensor architecture capable of accommodating different types of SRs has not been proposed, and the widespread applications of SRs in daily environments have not yet been demonstrated. Here, we propose a sensor architecture to simultaneously realize multi-type SRs and demonstrate their wide applications in mechanical, optical, and acoustic sensing domains. In particular, we find the coexistence of excitable SR and bistable SR in a sensor architecture composed of wirelessly coupled inductor-capacitor resonators connected to a nonlinearly saturable amplifier. In both types of SRs, adding noise to the system leads to a characteristic noise-enhanced signal-to-noise ratio (SNR). We further validate our findings through mechanical, optical, and acoustic sensing experiments and obtain noise-enhanced SNR by 9 dB, 3 dB, and 7 dB, respectively, compared to the standard methods devoid of SR integration. Our findings provide a general strategy to design various types of SRs and pave the way for the development of a distinctive class of sensors leveraging environmental noise, with potential applications ranging from biomedical devices to ambient sensing.

More than 40 years of cochlear implant research: A bibliometric analysis

OBJECTIVES

Cochlear implantation is the most effective treatment for patients with severe-to-profound sensorineural hearing loss. Much scientific work has been published since their inception. There is a need for a critical reflection on how and what we publish on cochlear implantation.

METHODS

All Science Citation Index Expanded featured articles between 1980 and 2022 with the word 'cochlear implants' or 'cochlear implantation' were collected from the Web of Science database. Separate characteristics, such as the publication dates, the journals, the number of citations, the countries of origin, the authors, the institutions and co-occurring keywords, were assessed.

RESULTS

13,934 articles were included in the data analysis. The journals of of Otology and Neurotology, Ear and Hearing and of Pediatric Otorhinolaryngology represent the top three most publishing journals. Hannover Medical School, the University of Melbourne and the University of Northern Iowa represent the top three most publishing institutions.

DISCUSSION

The amount of scientific publications on cochlear implant technology has increased for the last 40 years. Besides the focus on speech perception, the research landscape on cochlear implantation is broad and diverse. The number of countries and institutions contributing to these publications is limited.

CONCLUSION

This bibliometric analysis serves as a quantitative overview of the research landscape on cochlear implantation.

Decreased long-range temporal correlations in the resting-state functional magnetic resonance imaging blood-oxygen-level-dependent signal reflect motor sequence learning up to 2 weeks following training

Decreased long-range temporal correlations (LRTC) in brain signals can be used to measure cognitive effort during task execution. Here, we examined how learning a motor sequence affects long-range temporal memory within resting-state functional magnetic resonance imaging signal. Using the Hurst exponent (HE), we estimated voxel-wise LRTC and assessed changes over 5 consecutive days of training, followed by a retention scan 12 days later. The experimental group learned a complex visuomotor sequence while a complementary control group performed tightly matched movements. An interaction analysis revealed that HE decreases were specific to the complex sequence and occurred in well-known motor sequence learning associated regions including left supplementary motor area, left premotor cortex, left M1, left pars opercularis, bilateral thalamus, and right striatum. Five regions exhibited moderate to strong negative correlations with overall behavioral performance improvements. Following learning, HE values returned to pretraining levels in some regions, whereas in others, they remained decreased even 2 weeks after training. Our study presents new evidence of HE's possible relevance for functional plasticity during the resting-state and suggests that a cortical subset of sequence-specific regions may continue to represent a functional signature of learning reflected in decreased long-range temporal dependence after a period of inactivity.

Bilayer LDPC Codes Combined with Perturbed Decoding for MLC NAND Flash Memory

This paper presents a coding scheme based on bilayer low-density parity-check (LDPC) codes for multi-level cell (MLC) NAND flash memory. The main feature of the proposed scheme is that it exploits the asymmetric properties of an MLC flash channel and stores the extra parity-check bits in the lower page, which are activated only after the decoding failure of the upper page. To further improve the performance of the error correction, a perturbation process based on the genetic algorithm (GA) is incorporated into the decoding process of the proposed coding scheme, which can convert uncorrectable read sequences into error-correctable regions of the corresponding decoding space by introducing GA-trained noises. The perturbation decoding process is particularly efficient at low program-and-erase (P/E) cycle regions. The simulation results suggest that the proposed bilayer LDPC coding scheme can extend the lifetime of MLC NAND flash memory up to 10,000 P/E cycles. The proposed scheme can achieve a better balance between performance and complexity than traditional single LDPC coding schemes. All of these findings indicate that the proposed coding scheme is suitable for practical purposes in MLC NAND flash memory.

Engineering Transport via Collisional Noise: A Toolbox for Biology Systems

The study of noise assisted-transport in quantum systems is essential in a wide range of applications, from near-term NISQ devices to models for quantum biology. Here, we study a generalized XXZ model in the presence of stochastic collision noise, which allows describing environments beyond the standard Markovian formulation. Our analysis through the study of the local magnetization, the inverse participation ratio (IPR) or its generalization, and the inverse ergodicity ratio (IER) showed clear regimes, where the transport rate and coherence time could be controlled by the dissipation in a consistent manner. In addition, when considering various excitations, we characterized the interplay between collisions and system interactions, identifying regimes in which transport was counterintuitively enhanced when increasing the collision rate, even in the case of initially separated excitations. These results constitute an example of an essential building block for the understanding of quantum transport in structured noisy and warm-disordered environments.

On natural attunement: Shared rhythms between the brain and the environment

Rhythms exist both in the embodied brain and the built environment. Becoming attuned to the rhythms of the environment, such as repetitive columns, can greatly affect perception. Here, we explore how the built environment affects human cognition and behavior through the concept of natural attunement, often resulting from the coordination of a person's sensory and motor systems with the rhythmic elements of the environment. We argue that the built environment should not be reduced to mere states, representations, and single variables but instead be considered a bundle of highly related continuous signals with which we can resonate. Resonance and entrainment are dynamic processes observed when intrinsic frequencies of the oscillatory brain are influenced by the oscillations of an external signal. This allows visual rhythmic stimulations of the environment to affect the brain and body through neural entrainment, cross-frequency coupling, and phase resetting. We review how real-world architectural settings can affect neural dynamics, cognitive processes, and behavior in people, suggesting the crucial role of everyday rhythms in the brain-body-environment relationship.

Predictive coding and stochastic resonance as fundamental principles of auditory phantom perception

Mechanistic insight is achieved only when experiments are employed to test formal or computational models. Furthermore, in analogy to lesion studies, phantom perception may serve as a vehicle to understand the fundamental processing principles underlying healthy auditory perception. With a special focus on tinnitus-as the prime example of auditory phantom perception-we review recent work at the intersection of artificial intelligence, psychology and neuroscience. In particular, we discuss why everyone with tinnitus suffers from (at least hidden) hearing loss, but not everyone with hearing loss suffers from tinnitus. We argue that intrinsic neural noise is generated and amplified along the auditory pathway as a compensatory mechanism to restore normal hearing based on adaptive stochastic resonance. The neural noise increase can then be misinterpreted as auditory input and perceived as tinnitus. This mechanism can be formalized in the Bayesian brain framework, where the percept (posterior) assimilates a prior prediction (brain's expectations) and likelihood (bottom-up neural signal). A higher mean and lower variance (i.e. enhanced precision) of the likelihood shifts the posterior, evincing a misinterpretation of sensory evidence, which may be further confounded by plastic changes in the brain that underwrite prior predictions. Hence, two fundamental processing principles provide the most explanatory power for the emergence of auditory phantom perceptions: predictive coding as a top-down and adaptive stochastic resonance as a complementary bottom-up mechanism. We conclude that both principles also play a crucial role in healthy auditory perception. Finally, in the context of neuroscience-inspired artificial intelligence, both processing principles may serve to improve contemporary machine learning techniques.

Endogenous noise of neocortical neurons correlates with atypical sensory response variability in the Fmr1-/y mouse model of autism

Excessive neural variability of sensory responses is a hallmark of atypical sensory processing in autistic individuals with cascading effects on other core autism symptoms but unknown neurobiological substrate. Here, by recording neocortical single neuron activity in a well-established mouse model of Fragile X syndrome and autism, we characterized atypical sensory processing and probed the role of endogenous noise sources in exaggerated response variability in males. The analysis of sensory stimulus evoked activity and spontaneous dynamics, as well as neuronal features, reveals a complex cellular and network phenotype. Neocortical sensory information processing is more variable and temporally imprecise. Increased trial-by-trial and inter-neuronal response variability is strongly related to key endogenous noise features, and may give rise to behavioural sensory responsiveness variability in autism. We provide a novel preclinical framework for understanding the sources of endogenous noise and its contribution to core autism symptoms, and for testing the functional consequences for mechanism-based manipulation of noise.

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.

Control of stochastic and inverse stochastic resonances in a liquid-crystal electroconvection system using amplitude and phase noises

Stochastic and inverse stochastic resonances are counterintuitive phenomena, where noise plays a pivotal role in the dynamics of various biological and engineering systems. Even though these resonances have been identified in various systems, a transition between them has never been observed before. The present study demonstrates the presence of both resonances in a liquid crystal electroconvection system using combined amplitude and phase noises, which correspond to colored noises with appropriate cutoff frequencies (i.e., finite correlation times). We established the emergence of both resonances and their transition through systematic control of the electroconvection threshold voltage using these two noise sources. Our numerical simulations were experimentally confirmed and revealed how the output performance of the system could be controlled by combining the intensity and cutoff frequency of the two noises. Furthermore, we suggested the crucial contribution of a usually overlooked additional phase noise to the advancements in various noise-related fields.

Estimation of cumulative amplitude distributions of miniature postsynaptic currents allows characterising their multimodality, quantal size and variability

A miniature postsynaptic current (mPSC) is a small, rare, and highly variable spontaneous synaptic event that is generally caused by the spontaneous release of single vesicles. The amplitude and variability of mPSCs are key measures of the postsynaptic processes and are taken as the main characteristics of an elementary unit (quantal size) in traditional quantal analysis of synaptic transmission. Due to different sources of biological and measurement noise, recordings of mPSCs exhibit high trial-to-trial heterogeneity, and experimental measurements of mPSCs are usually noisy and scarce, making their analysis demanding. Here, we present a sequential procedure for precise analysis of mPSC amplitude distributions for the range of small currents. To illustrate the developed approach, we chose previously obtained experimental data on the effect of the extracellular matrix on synaptic plasticity. The proposed statistical technique allowed us to identify previously unnoticed additional modality in the mPSC amplitude distributions, indicating the formation of new immature synapses upon ECM attenuation. We show that our approach can reliably detect multimodality in the distributions of mPSC amplitude, allowing for accurate determination of the size and variability of the quantal synaptic response. Thus, the proposed method can significantly expand the informativeness of both existing and newly obtained experimental data. We also demonstrated that mPSC amplitudes around the threshold of microcurrent excitation follow the Gumbel distribution rather than the binomial statistics traditionally used for a wide range of currents, either for a single synapse or when taking into consideration small influences of the adjacent synapses. Such behaviour is argued to originate from the theory of extreme processes. Specifically, recorded mPSCs represent instant random current fluctuations, among which there are relatively larger spikes (extreme events). They required more level of coherence that can be provided by different mechanisms of network or system level activation including neuron circuit signalling and extrasynaptic processes.

Cellular shortening and calcium dynamics are improved by noisy stimulus in a model of cardiomyopathy

Noise is present in cell biology. The capability of cells to respond to noisy environment have become essential. This study aimed to investigate whether noise can enhance the contractile response and Ca2+ handling in cardiomyocytes from a cardiomyopathy model. Experiments were conducted in an experimental setup with Gaussian white noise, frequency, and amplitude control to stimulate myocytes. Cell shortening, maximal shortening velocity, time to peak shortening, and time to half relaxation variables were recorded to cell shortening. Ca2+ transient amplitude and raise rate variables were registered to measure Ca2+ transients. Our results for cell shortening, Ca2+ transient amplitude, and raise rate suggest that cell response improve when myocytes are noise stimulated. Also, cell shortening, maximal shortening velocity, Ca2+ transient amplitude, and raise improves in control cells. Altogether, these findings suggest novel characteristics in how cells improve their response in a noisy environment.

Clinical Efficiency and Acceptability of EMDR and MOSAIC Therapy for PTSD

Eye movement desensitization and reprocessing (EMDR) therapy is one of the therapies recommended by the World Health Organization (2013) to treat posttraumatic stress disorder (PTSD). Although efficient, repeated exposure to the traumatic memory may reduce its acceptability to patients. The therapy "eye movement and alternate stimulation for brain integration" (MOSAIC in French) was developed to improve acceptability and reduce pain by drawing on the patient's internal resources. MOSAIC therapy focuses on the body sensations that the patient wants to experience and avoids having to relive the traumatic memories. This observational study aimed to compare the clinical efficiency of EMDR and MOSAIC therapy for PTSD and to measure the well-being generated by both therapies. Twenty-six PTSD patients (17 females and 9 males, mean age 37.01 years, SD = 13.06) received treatment by psychiatrists and/or psychologists trained with EMDR or MOSAIC therapy. Both patient groups achieved a significant decrease in PTSD symptoms as measured with the PCL-5. However, fewer sessions were required with the MOSAIC therapy than with EMDR therapy. As expected, the level of well-being experienced by the patient during the therapy, assessed using the Lickert scale, was higher with MOSAIC than with EMDR therapy from the first session. These findings provide the first evidence of the efficacy of MOSAIC therapy treatment, which now needs to be corroborated in a larger randomized clinical trial.

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.

Human neuronal excitation/inhibition balance explains and predicts neurostimulation induced learning benefits

Previous research has highlighted the role of the excitation/inhibition (E/I) ratio for typical and atypical development, mental health, cognition, and learning. Other research has highlighted the benefits of high-frequency transcranial random noise stimulation (tRNS)-an excitatory form of neurostimulation-on learning. We examined the E/I as a potential mechanism and studied whether tRNS effect on learning depends on E/I as measured by the aperiodic exponent as its putative marker. In addition to manipulating E/I using tRNS, we also manipulated the level of learning (learning/overlearning) that has been shown to influence E/I. Participants (n = 102) received either sham stimulation or 20-minute tRNS over the dorsolateral prefrontal cortex (DLPFC) during a mathematical learning task. We showed that tRNS increased E/I, as reflected by the aperiodic exponent, and that lower E/I predicted greater benefit from tRNS specifically for the learning task. In contrast to previous magnetic resonance spectroscopy (MRS)-based E/I studies, we found no effect of the level of learning on E/I. A further analysis using a different data set suggest that both measures of E/I (EEG versus MRS) may reflect, at least partly, different biological mechanisms. Our results highlight the role of E/I as a marker for neurostimulation efficacy and learning. This mechanistic understanding provides better opportunities for augmented learning and personalized interventions.

Heart Rate Variability Code: Does It Exist and Can We Hack It?

A code is generally defined as a system of signals or symbols for communication. Experimental evidence is synthesized for the presence and utility of such communication in heart rate variability (HRV) with particular attention to fetal HRV: HRV contains signatures of information flow between the organs and of response to physiological or pathophysiological stimuli as signatures of states (or syndromes). HRV exhibits features of time structure, phase space structure, specificity with respect to (organ) target and pathophysiological syndromes, and universality with respect to species independence. Together, these features form a spatiotemporal structure, a phase space, that can be conceived of as a manifold of a yet-to-be-fully understood dynamic complexity. The objective of this article is to synthesize physiological evidence supporting the existence of HRV code: hereby, the process-specific subsets of HRV measures indirectly map the phase space traversal reflecting the specific information contained in the code required for the body to regulate the physiological responses to those processes. The following physiological examples of HRV code are reviewed, which are reflected in specific changes to HRV properties across the signal-analytical domains and across physiological states and conditions: the fetal systemic inflammatory response, organ-specific inflammatory responses (brain and gut), chronic hypoxia and intrinsic (heart) HRV (iHRV), allostatic load (physiological stress due to surgery), and vagotomy (bilateral cervical denervation). Future studies are proposed to test these observations in more depth, and the author refers the interested reader to the referenced publications for a detailed study of the HRV measures involved. While being exemplified mostly in the studies of fetal HRV, the presented framework promises more specific fetal, postnatal, and adult HRV biomarkers of health and disease, which can be obtained non-invasively and continuously.

Vestibular prosthesis: from basic research to clinics

Balance disorders are highly prevalent worldwide, causing substantial disability with high personal and socioeconomic impact. The prognosis in many of these patients is poor, and rehabilitation programs provide little help in many cases. This medical problem can be addressed using microelectronics by combining the highly successful cochlear implant experience to produce a vestibular prosthesis, using the technical advances in micro gyroscopes and micro accelerometers, which are the electronic equivalents of the semicircular canals (SCC) and the otolithic organs. Reaching this technological milestone fostered the possibility of using these electronic devices to substitute the vestibular function, mainly for visual stability and posture, in case of damage to the vestibular endorgans. The development of implantable and non-implantable devices showed diverse outcomes when considering the integrity of the vestibular pathways, the device parameters (current intensity, impedance, and waveform), and the targeted physiological function (balance and gaze). In this review, we will examine the development and testing of various prototypes of the vestibular implant (VI). The insight raised by examining the state-of-the-art vestibular prosthesis will facilitate the development of new device-development strategies and discuss the feasibility of complex combinations of implantable devices for disorders that directly affect balance and motor performance.

Characterizing the information transmission of inverse stochastic resonance and noise-induced activity amplification in neuronal systems

Purkinje cells exhibit a reduction of the mean firing rate at intermediate-noise intensities, which is somewhat reminiscent of the response enhancement known as "stochastic resonance" (SR). Although the comparison with the stochastic resonance ends here, the current phenomenon has been given the name "inverse stochastic resonance" (ISR). Recent research has demonstrated that the ISR effect, like its close relative "nonstandard SR" [or, more correctly, noise-induced activity amplification (NIAA)], has been shown to stem from the weak-noise quenching of the initial distribution, in bistable regimes where the metastable state has a larger attraction basin than the global minimum. To understand the underlying mechanism of the ISR and NIAA phenomena, we study the probability distribution function of a one-dimensional system subjected to a bistable potential that has the property of symmetry, i.e., if we change the sign of one of its parameters, we can obtain both phenomena with the same properties in the depth of the wells and the width of their basins of attraction subjected to Gaussian white noise with variable intensity. Previous work has shown that one can theoretically determine the probability distribution function using the convex sum between the behavior at small and high noise intensities. To determine the probability distribution function more precisely, we resort to the "weighted ensemble Brownian dynamics simulation" model, which provides an accurate estimate of the probability distribution function for both low and high noise intensities and, most importantly, for the transition of both behaviors. In this way, on the one hand, we show that both phenomena emerge from a metastable system where, in the case of ISR, the global minimum of the system is in a state of lower activity, while in the case of NIAA, the global minimum is in a state of increased activity, the importance of which does not depend on the width of the basins of attraction. On the other hand, we see that quantifiers such as Fisher information, statistical complexity, and especially Shannon entropy fail to distinguish them, but they show the existence of the mentioned phenomena. Thus, noise management may well be a mechanism by which Purkinje cells find an efficient way to transmit information in the cerebral cortex.

Noise and vestibular perception of passive self-motion

Noise defined as random disturbances is ubiquitous in both the external environment and the nervous system. Depending on the context, noise can degrade or improve information processing and performance. In all cases, it contributes to neural systems dynamics. We review some effects of various sources of noise on the neural processing of self-motion signals at different stages of the vestibular pathways and the resulting perceptual responses. Hair cells in the inner ear reduce the impact of noise by means of mechanical and neural filtering. Hair cells synapse on regular and irregular afferents. Variability of discharge (noise) is low in regular afferents and high in irregular units. The high variability of irregular units provides information about the envelope of naturalistic head motion stimuli. A subset of neurons in the vestibular nuclei and thalamus are optimally tuned to noisy motion stimuli that reproduce the statistics of naturalistic head movements. In the thalamus, variability of neural discharge increases with increasing motion amplitude but saturates at high amplitudes, accounting for behavioral violation of Weber's law. In general, the precision of individual vestibular neurons in encoding head motion is worse than the perceptual precision measured behaviorally. However, the global precision predicted by neural population codes matches the high behavioral precision. The latter is estimated by means of psychometric functions for detection or discrimination of whole-body displacements. Vestibular motion thresholds (inverse of precision) reflect the contribution of intrinsic and extrinsic noise to perception. Vestibular motion thresholds tend to deteriorate progressively after the age of 40 years, possibly due to oxidative stress resulting from high discharge rates and metabolic loads of vestibular afferents. In the elderly, vestibular thresholds correlate with postural stability: the higher the threshold, the greater is the postural imbalance and risk of falling. Experimental application of optimal levels of either galvanic noise or whole-body oscillations can ameliorate vestibular function with a mechanism reminiscent of stochastic resonance. Assessment of vestibular thresholds is diagnostic in several types of vestibulopathies, and vestibular stimulation might be useful in vestibular rehabilitation.

How synaptic strength, short-term plasticity, and input synchrony contribute to neuronal spike output

Neurons integrate from thousands of synapses whose strengths span an order of magnitude. Intriguingly, in mouse neocortex, the few 'strong' synapses are formed between similarly tuned cells, suggesting they determine spiking output. This raises the question of how other computational primitives, including 'background' activity from the many 'weak' synapses, short-term plasticity, and temporal factors contribute to spiking. We used paired recordings and extracellular stimulation experiments to map excitatory postsynaptic potential (EPSP) amplitudes and paired-pulse ratios of synaptic connections formed between pyramidal neurons in layer 2/3 (L2/3) of barrel cortex. While net short-term plasticity was weak, strong synaptic connections were exclusively depressing. Importantly, we found no evidence for clustering of synaptic properties on individual neurons. Instead, EPSPs and paired-pulse ratios of connections converging onto the same cells spanned the full range observed across L2/3, which critically constrains theoretical models of cortical filtering. To investigate how different computational primitives of synaptic information processing interact to shape spiking, we developed a computational model of a pyramidal neuron in the excitatory L2/3 circuitry, which was constrained by our experiments and published in vivo data. We found that strong synapses were substantially depressed during ongoing activation and their ability to evoke correlated spiking primarily depended on their high temporal synchrony and high firing rates observed in vivo. However, despite this depression, their larger EPSP amplitudes strongly amplified information transfer and responsiveness. Thus, our results contribute to a nuanced framework of how cortical neurons exploit synergies between temporal coding, synaptic properties, and noise to transform synaptic inputs into spikes.

Enhancement of Vestibular Motion Discrimination by Small Stochastic Whole-body Perturbations in Young Healthy Humans

Noisy galvanic vestibular stimulation has been shown to improve vestibular perception in healthy subjects. Here, we sought to obtain similar results using more natural stimuli consisting of small-amplitude motion perturbations of the whole body. Thirty participants were asked to report the perceived direction of antero-posterior sinusoidal motion on a MOOG platform. We compared the baseline perceptual thresholds with those obtained by applying small, stochastic perturbations at different power levels along the antero-posterior axis, symmetrically distributed around a zero-mean. At the population level, we found that the thresholds for all but the highest level of noise were significantly lower than the baseline threshold. At the individual level, the threshold was lower with at least one noise level than the threshold without noise in 87% of participants. Thus, small, stochastic oscillations of the whole body can increase the probability of recognizing the direction of motion from low, normally subthreshold vestibular signals, possibly due to stochastic resonance mechanisms. We suggest that, just as the external noise of the present experiments, also the spontaneous random oscillations of the head and body associated with standing posture are beneficial by enhancing vestibular thresholds with a mechanism similar to stochastic resonance.

Mitochondrial Dynamics during Development

Mitochondria are dynamic membrane-bound organelles in eukaryotic cells. These are important for the generation of chemical energy needed to power various cellular functions and also support metabolic, energetic, and epigenetic regulation in various cells. These organelles are also important for communication with the nucleus and other cellular structures, to maintain developmental sequences and somatic homeostasis, and for cellular adaptation to stress. Increasing information shows mitochondrial defects as an important cause of inherited disorders in different organ systems. In this article, we provide an extensive review of ontogeny, ultrastructural morphology, biogenesis, functional dynamics, important clinical manifestations of mitochondrial dysfunction, and possibilities for clinical intervention. We present information from our own clinical and laboratory research in conjunction with information collected from an extensive search in the databases PubMed, EMBASE, and Scopus.

Does stochastic resonance improve performance for individuals with higher autism-spectrum quotient?

While noise is generally believed to impair performance, the detection of weak stimuli can sometimes be enhanced by introducing optimum noise levels. This phenomenon is termed 'Stochastic Resonance' (SR). Past evidence suggests that autistic individuals exhibit higher neural noise than neurotypical individuals. It has been proposed that the enhanced performance in Autism Spectrum Disorder (ASD) on some tasks could be due to SR. Here we present a computational model, lab-based, and online visual identification experiments to find corroborating evidence for this hypothesis in individuals without a formal ASD diagnosis. Our modeling predicts that artificially increasing noise results in SR for individuals with low internal noise (e.g., neurotypical), however not for those with higher internal noise (e.g., autistic, or neurotypical individuals with higher autistic traits). It also predicts that at low stimulus noise, individuals with higher internal noise outperform those with lower internal noise. We tested these predictions using visual identification tasks among participants from the general population with autistic traits measured by the Autism-Spectrum Quotient (AQ). While all participants showed SR in the lab-based experiment, this did not support our model strongly. In the online experiment, significant SR was not found, however participants with higher AQ scores outperformed those with lower AQ scores at low stimulus noise levels, which is consistent with our modeling. In conclusion, our study is the first to investigate the link between SR and superior performance by those with ASD-related traits, and reports limited evidence to support the high neural noise/SR hypothesis.

Noise facilitates entrainment of a population of uncoupled limit cycle oscillators

Many biological oscillators share two properties: they are subject to stochastic fluctuations (noise) and they must reliably adjust their period to changing environmental conditions (entrainment). While noise seems to distort the ability of single oscillators to entrain, in populations of uncoupled oscillators noise allows population-level entrainment for a wider range of input amplitudes and periods. Here, we investigate how this effect depends on the noise intensity and the number of oscillators in the population. We have found that, if a population consists of a sufficient number of oscillators, increasing noise intensity leads to faster entrainment after a phase change of the input signal (jet lag) and increases sensitivity to low-amplitude input signals.

Heterogeneous stochastic bifurcations explain intrinsic oscillatory patterns in entorhinal cortical stellate cells

Stellate cells (SC) in the medial entorhinal cortex manifest intrinsic membrane potential oscillatory patterns. Although different theoretical frameworks have been proposed to explain these patterns, a robust unifying framework that jointly accounts for intrinsic heterogeneities and stochasticity is missing. Here, we first performed in vitro patch-clamp electrophysiological recordings from rat SCs and found pronounced cell-to-cell variability in their characteristic physiological properties, including peri-threshold oscillatory patterns. We demonstrate that noise introduced into two independent populations (endowed with deterministic or stochastic ion-channel gating kinetics) of heterogeneous biophysical models yielded activity patterns that were qualitatively similar to electrophysiological peri-threshold oscillatory activity in SCs. We developed spectrogram-based quantitative metrics for the identification of valid oscillations and confirmed that these metrics reliably captured the variable-amplitude and arhythmic oscillatory patterns observed in electrophysiological recordings. Using these quantitative metrics, we validated activity patterns from both heterogeneous populations of SC models, with each model assessed with multiple trials of different levels of noise at distinct membrane depolarizations. Our analyses unveiled the manifestation of stochastic resonance (detection of the highest number of valid oscillatory traces at an optimal level of noise) in both heterogeneous populations of SC models. Finally, we show that a generalized network motif comprised of a slow negative feedback loop amplified by a fast positive feedback loop manifested stochastic bifurcations and stochastic resonance in the emergence of oscillations. Together, through a unique convergence of the degeneracy and stochastic resonance frameworks, our unifying framework centered on heterogeneous stochastic bifurcations argues for state-dependent emergence of SC oscillations.

Active versus sham transcranial direct current stimulation (tDCS) as an adjunct to varenicline treatment for smoking cessation: Study protocol for a double-blind single dummy randomized controlled trial

BACKGROUND

Smoking is a chronic and relapsing disease, with up to 60% of quitters relapsing within the first year. Transcranial Direct Current Stimulation (tDCS), targets cortical circuits and acutely reduces craving and withdrawal symptoms among cigarette smokers. However, the efficacy of tDCS as an adjunct to standard smoking cessation treatments has not been studied. This study aims to investigate the effectiveness of tDCS in combination with varenicline for smoking cessation. We hypothesize that active tDCS combined with varenicline will improve cessation outcomes compared to sham tDCS combined with varenicline.

METHODS

This is a double-blind, sham-controlled randomized clinical trial where fifty healthy smokers will be recruited in Toronto, Canada. Participants will be randomized 1:1 to either active tDCS (20 minutes at 2 mA) or sham tDCS (30 seconds at 2 mA, 19 minutes at 0 mA) for 10 daily sessions (2 weeks) plus 5 follow up sessions, occurring every two weeks for 10 weeks. All participants will be given standard varenicline treatment concurrently for the 12-week treatment period. The primary outcome is 30 day continuous abstinence at end of treatment, confirmed with urinary cotinine. Measurements made at each study visit include expired carbon monoxide, self-reported craving and withdrawal. Three magnetic resonance imaging (MRI) scans will be conducted: two at baseline and one at end of treatment, to assess any functional or structural changes following treatment.

DISCUSSION

For every two smokers who quit, one life is saved from a tobacco-related mortality. Therefore, it is important to develop new and more effective treatment approaches that can improve and maintain long-term abstinence, in order to decrease the prevalence of tobacco-related deaths and disease. Furthermore, the addition of longitudinal neuroimaging can shed light on neural circuitry changes that might occur as a result of brain stimulation, furthering our understanding of tDCS in addiction treatment.

TRIAL REGISTRATION

This trial has been registered with Clinicaltrials.gov: NCT03841292 since February 15th 2019 (https://clinicaltrials.gov/ct2/show/NCT03841292)-retrospectively registered.

Blots and brains. A note on the centenary of Hermann Rorschach's death

This historical note is a commemorial of Rorschach, the person, and Rorschach the test. Hermann Rorschach died 100 years ago, not quite a year after the publication of his book containing the 10 inkblots. These have reached an iconic status, but the "Rorschach Test" as used in psychiatry, legal organizations and aptitude assessments is not quite what Hermann Rorschach designed it for in the first line. A first section of this article introduces Hermann Rorschach as a man with very broad interests and an inclination to ask cognitive science questions that are still challenging today. A second section provides a critical summary of the fate of the ten inkblots after Rorschach's death - how they conquered the whole world in a time with a pronouced "psychometric attitude", and also how they failed in some attempts to measure personality traits in special populations. A final section focuses on recent research on one particular aspect of a testee's associations to the inkblots: "movement responses", i.e. the perception of implied motion. Here, neural and behavioral correlates have been demonstrated by modern neuroimaging techniques. One study, which set out to validate both the Rorschach as a personality test and the view that the two cerebral hemispheres correspond to divergent "personalities" is also summarized. The viewpoint concludes by suggesting that future work with inkblots should consider Rorschach's original intention to use inkblots to uncover basic laws of perception. Modern applications of computer-generated pseudorandom stimuli (random dot arrays or stochastic noise) would have been embraced by Hermann Rorschach as he appreciated the impact of visual noise for the study of vision and visual cognition.

Multiplexing-based control of stochastic resonance

We show that multiplexing (Here, the term "multiplexing" means a special network topology where a one-layer network is connected to another one-layer networks through coupling between replica nodes. In the present paper, this term does not refer to the signal processing issues and telecommunications.) allows us to control noise-induced dynamics of multilayer networks in the regime of stochastic resonance. We illustrate this effect on an example of two- and multi-layer networks of bistable overdamped oscillators. In particular, we demonstrate that multiplexing suppresses the effect of stochastic resonance if the periodic forcing is present in only one layer. In contrast, multiplexing allows us to enhance the stochastic resonance if the periodic forcing and noise are present in all the interacting layers. In such a case, the impact of multiplexing has a resonant character: the most pronounced effect of stochastic resonance is achieved for an appropriate intermediate value of coupling strength between the layers. Moreover, multiplexing-induced enhancement of the stochastic resonance can become more pronounced for the increasing number of coupled layers. To visualize the revealed phenomena, we use the evolution of the dependence of the signal-to-noise ratio on the noise intensity for varying strength of coupling between the layers.

The Effects of White Noise on Attentional Performance and On-Task Behaviors in Preschoolers with ADHD

Several models have tentatively associated improving attention-deficit/hyperactivity disorder (ADHD) symptoms with arousal and external environmental stimulation. In order to further clarify the relationships between ADHD symptoms, arousal, and external stimulation, this study focused on exploring the "simultaneous" effects of white noise on intrinsic attentional performance and extrinsic on-task behaviors in preschoolers with and without ADHD. By using the computerized task (K-CPT 2), 104 preschoolers, including 52 ADHD children and 52 typically developing (TD) children, were tested and analyzed for their intrinsic attention (such as detectability, omission errors, commission errors, and reaction time). Simultaneously, these preschoolers' external on-task behaviors were recorded for analysis through systematic observation. This study showed that white noise could effectively improve attention performance, including enhancing the ability to differentiate non-targets from targets and decreasing omission errors. It could also reduce the extrinsic hyperactive behaviors of preschoolers with ADHD. The findings of this study highlighted that white noise stimulation is a beneficial non-pharmacological treatment for preschoolers with ADHD. In contrast, for TD preschoolers, the results of this study showed that the external white noise stimuli were not only unhelpful but also a burden.

Neural synchrony in cortical networks: mechanisms and implications for neural information processing and coding

Synchronization of neuronal discharges on the millisecond scale has long been recognized as a prevalent and functionally important attribute of neural activity. In this article, I review classical concepts and corresponding evidence of the mechanisms that govern the synchronization of distributed discharges in cortical networks and relate those mechanisms to their possible roles in coding and cognitive functions. To accommodate the need for a selective, directed synchronization of cells, I propose that synchronous firing of distributed neurons is a natural consequence of spike-timing-dependent plasticity (STDP) that associates cells repetitively receiving temporally coherent input: the “synchrony through synaptic plasticity” hypothesis. Neurons that are excited by a repeated sequence of synaptic inputs may learn to selectively respond to the onset of this sequence through synaptic plasticity. Multiple neurons receiving coherent input could thus actively synchronize their firing by learning to selectively respond at corresponding temporal positions. The hypothesis makes several predictions: first, the position of the cells in the network, as well as the source of their input signals, would be irrelevant as long as their input signals arrive simultaneously; second, repeating discharge patterns should get compressed until all or some part of the signals are synchronized; and third, this compression should be accompanied by a sparsening of signals. In this way, selective groups of cells could emerge that would respond to some recurring event with synchronous firing. Such a learned response pattern could further be modulated by synchronous network oscillations that provide a dynamic, flexible context for the synaptic integration of distributed signals. I conclude by suggesting experimental approaches to further test this new hypothesis.

Frequency dependent growth of bacteria in living materials

The fusion of living bacteria and man-made materials represents a new frontier in medical and biosynthetic technology. However, the principles of bacterial signal processing inside synthetic materials with three-dimensional and fluctuating environments remain elusive. Here, we study bacterial growth in a three-dimensional hydrogel. We find that bacteria expressing an antibiotic resistance module can take advantage of ambient kinetic disturbances to improve growth while encapsulated. We show that these changes in bacterial growth are specific to disturbance frequency and hydrogel density. This remarkable specificity demonstrates that periodic disturbance frequency is a new input that engineers may leverage to control bacterial growth in synthetic materials. This research provides a systematic framework for understanding and controlling bacterial information processing in three-dimensional living materials.

Disorder in Ca2+ release unit locations confers robustness but cuts flexibility of heart pacemaking

Excitation-contraction coupling kinetics is dictated by the action potential rate of sinoatrial-nodal cells. These cells generate local Ca releases (LCRs) that activate Na/Ca exchanger current, which accelerates diastolic depolarization and determines the pace. LCRs are generated by clusters of ryanodine receptors, Ca release units (CRUs), residing in the sarcoplasmic reticulum. While CRU distribution exhibits substantial heterogeneity, its functional importance remains unknown. Using numerical modeling, here we show that with a square lattice distribution of CRUs, Ca-induced-Ca-release propagation during diastolic depolarization is insufficient for pacemaking within a broad range of realistic ICaL densities. Allowing each CRU to deviate randomly from its lattice position allows sparks to propagate, as observed experimentally. As disorder increases, the CRU distribution exhibits larger empty spaces and simultaneously CRU clusters, as in Poisson clumping. Propagating within the clusters, Ca release becomes synchronized, increasing action potential rate and reviving pacemaker function of dormant/nonfiring cells. However, cells with fully disordered CRU positions could not reach low firing rates and their β-adrenergic-receptor stimulation effect was substantially decreased. Inclusion of Cav1.3, a low-voltage activation L-type Ca channel isoform into ICaL, strongly increases recruitment of CRUs to fire during diastolic depolarization, increasing robustness of pacemaking and complementing effects of CRU distribution. Thus, order/disorder in CRU locations along with Cav1.3 expression regulates pacemaker function via synchronization of CRU firing. Excessive CRU disorder and/or overexpression of Cav1.3 boosts pacemaker function in the basal state, but limits the rate range, which may contribute to heart rate range decline with age and disease.

Diversity-induced decoherence

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

Colloidal Stochastic Resonance in Confined Geometries

We investigate the dynamical properties of a colloidal particle in a double cavity. Without external driving, the particle hops between two free-energy minima with transition mean time depending on the system's entropic and energetic barriers. We then drive the particle with a periodic force. When the forcing period is set at twice the transition mean time, a statistical synchronization between particle motion and forcing phase marks the onset of a stochastic resonance mechanism. Comparisons between experimental results and predictions from the Fick-Jacobs theory and Brownian dynamics simulation reveal significant hydrodynamic effects, which change both resonant amplification and noise level. We further show that hydrodynamic effects can be incorporated into existing theory and simulation by using an experimentally measured particle diffusivity.

Dissociable effects of music and white noise on conflict-induced behavioral adjustments

Auditory stimuli, encompassing a continually expanding collection of musical genres and sonic hues, present a safe and easily administrable therapeutic option for alleviating cognitive deficits associated with neuropsychological disorders, but their effects on executive control are yet to be completely understood. To better understand how the processing of certain acoustic properties can influence conflict processing, we had a large of cohort of undergraduate students complete the Stroop colour and word test in three different background conditions: classical music, white noise, and silence. Because of pandemic guidelines and the necessity to run the experiment remotely, participants also completed the Wisconsin card sorting test (WCST), so that the reliability and consistency of acquired data could be assessed. We found that white noise, but not classical music increased the response time difference between congruent (low conflict) and incongruent (high conflict) trials (conflict cost), hence impairing performance. Results from the WCST indicated that home-based data collection was reliable, replicating a performance bias reported in our previous laboratory-based experiments. Both the auditory stimuli were played at a similar intensity, thus their dissociable effects may have resulted from differing emotional responses within participants, where white noise, but not music elicited a negative response. Integrated with previous literature, our findings indicate that outside of changes in tempo and valence, classical music does not affect cognitive functions associated with conflict processing, whilst white noise impairs these functions in a manner similar to other stressors, and hence requires further research before its implementation into neuropsychiatric care.

Assessing the Effect of Simultaneous Combining of Transcranial Direct Current Stimulation and Transcutaneous Auricular Vagus Nerve Stimulation on the Improvement of Working Memory Performance in Healthy Individuals

A previous study found that combining transcranial direct current stimulation (tDCS) and transcutaneous auricular vagus nerve stimulation (taVNS) could evoke significantly larger activation on a range of cortical and subcortical brain regions than the numerical summation of tDCS and taVNS effects. In this study, two within-subject experiments were employed to investigate its effects on working memory (WM). In experiment 1, the WM modulatory effects of tDCS over the left dorsolateral prefrontal cortex (DLPFC), taVNS, and simultaneous joint simulation of tDCS over the left DLPFC and taVNS (SJS-L) were compared among 60 healthy subjects. They received these three interventions between the baseline test and post-test in a random manner three times. In spatial 3-back task, there was a significant interaction between time and stimulations in the accuracy rate of matching trials (mACC, p=0.018). MACCs were significantly improved by SJS (p = 0.001) and taVNS (p = 0.045), but not by tDCS (p = 0.495). Moreover, 41 subjects in the SJS group showed improvement, which was significantly larger than that in the taVNS group (29 subjects) and tDCS group (26 subjects). To further investigate the generalization effects of SJS, 72 students were recruited in experiment 2. They received tDCS over the right DLPFC, taVNS, simultaneous joint simulation of tDCS over the right DLPFC and taVNS (SJS-R), and sham stimulation in a random manner four times. No significant results were found, but there was a tendency similar to experiment 1 in the spatial 3-back task. In conclusion, combining tDCS and taVNS might be a potential non-invasive neuromodulation technique which is worthy of study in future.

Stochastic Resonance Governs Memory Consolidation Accuracy in a Neural Network Model

The formation and recollection of memories is a multi-step neural process subject to errors. We propose a computational model of memory nodes receiving input from a colored tic-tac-toe board. We report memory errors during consolidation and reconsolidation when different noise levels are introduced into the model. The model is based on Hebbian plasticity and attempts to store the color and position of an X or O from the board. Memory nodes simulating neurons use an integrate-and-fire model to represent the correct or incorrect storage of the board information by scaling synaptic weights. We explored how baseline firing rate, which we considered analogous to noise in storing memory, impacted the creation of correct and incorrect memories. We found that a higher firing rate was associated with fewer accurate memories. Interestingly, the ideal amount of noise for correct memory storage was nonzero. This phenomenon is known as stochastic resonance, wherein random noise enhances processing. We also examined how many times our model could reactivate a memory before making an error. We found an exponentially decaying response, with a low firing rate yielding more stable memories. Even though our model incorporates only two memory nodes, it provides a basis for examining the consolidation and retrieval of memory storage based on the unique visual input of a tic-tac-toe board. Further work may incorporate different inputs, more nodes, and increased network complexity. Clinical Relevance- This model enables investigation of how the human cortex may utilize and exploit noise during information processing.

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.

Improving Decodability of Polar Codes by Adding Noise

This paper presents an online perturbed and directed neural-evolutionary (Online-PDNE) decoding algorithm for polar codes, in which the perturbation noise and online directed neuro-evolutionary noise sequences are sequentially added to the received sequence for re-decoding if the standard polar decoding fails. The new decoding algorithm converts uncorrectable received sequences into error-correcting regions of their decoding space for correct decoding by adding specific noises. To reduce the decoding complexity and delay, the PDNE decoding algorithm and sole neural-evolutionary (SNE) decoding algorithm for polar codes are further proposed, which provide a considerable tradeoff between the decoding performance and complexity by acquiring the neural-evolutionary noise in an offline manner. Numerical results suggest that our proposed decoding algorithms outperform the other conventional decoding algorithms. At high signal-to-noise ratio (SNR) region, the Online-PDNE decoding algorithm improves bit error rate (BER) performance by more than four orders of magnitude compared with the conventional simplified successive cancellation (SSC) decoding algorithm. Furthermore, in the mid-high SNR region, the average normalized complexity of the proposed algorithm is almost the same as that of the SSC decoding algorithm, while preserving the decoding performance gain.

Oscillations and variability in neuronal systems: interplay of autonomous transient dynamics and fast deterministic fluctuations

Neuronal systems are subject to rapid fluctuations both intrinsically and externally. These fluctuations can be disruptive or constructive. We investigate the dynamic mechanisms underlying the interactions between rapidly fluctuating signals and the intrinsic properties of the target cells to produce variable and/or coherent responses. We use linearized and non-linear conductance-based models and piecewise constant (PWC) inputs with short duration pieces. The amplitude distributions of the constant pieces consist of arbitrary permutations of a baseline PWC function. In each trial within a given protocol we use one of these permutations and each protocol consists of a subset of all possible permutations, which is the only source of uncertainty in the protocol. We show that sustained oscillatory behavior can be generated in response to various forms of PWC inputs independently of whether the stable equilibria of the corresponding unperturbed systems are foci or nodes. The oscillatory voltage responses are amplified by the model nonlinearities and attenuated for conductance-based PWC inputs as compared to current-based PWC inputs, consistent with previous theoretical and experimental work. In addition, the voltage responses to PWC inputs exhibited variability across trials, which is reminiscent of the variability generated by stochastic noise (e.g., Gaussian white noise). Our analysis demonstrates that both oscillations and variability are the result of the interaction between the PWC input and the target cell's autonomous transient dynamics with little to no contribution from the dynamics in vicinities of the steady-state, and do not require input stochasticity.

A Stochastic Resonance Electrocardiogram Enhancement Algorithm for Robust QRS Detection

This study presents a new QRS detection algorithm making use of the background noise that is inevitably present in electrocardiogram (ECG) recordings. The algorithm suppresses noise, enhances the QRS-waves, and applies a threshold for QRS detection. Noise suppression and QRS enhancement are performed by a band-pass filter stage followed by a nonlinear stage based on the interaction of a particle inside an underdamped monostable potential well. The nonlinear stage maximizes the output when there is a QRS-wave and minimizes the output otherwise. One of the instruments that the nonlinear stage uses to enhance the QRS-waves is stochastic resonance, where the output is maximized for a non-zero intensity background noise. In terms of QRS-wave detection F1 score, which ranges from 98.87% to 99.99% on four major benchmarking databases (MIT-BIH Arrhythmia, QT, European ST-T, and MIT-BIH Noise Stress Test), the algorithm outperforms all existing ECG processing algorithms. The study, for the first time, demonstrates QRS-enhancement by facilitating stochastic resonance while suppressing in-band noise of ECG signals. Detecting QRS-waves as the ECG data streams, having a complexity of O(n), and not requiring any training data make the algorithm convenient for real-time ECG monitoring applications with limited computational resources.