The benefits of noise in neural systems: bridging theory and experiment

Low-intensity vestibular noise stimulation improves postural symptoms in progressive supranuclear palsy

BACKGROUND

Postural imbalance and falls are an early disabling symptom in patients with progressive supranuclear palsy (PSP) of multifactorial origin that may involve abnormal vestibulospinal reflexes. Low-intensity noisy galvanic vestibular stimulation (nGVS) is a non-invasive treatment to normalize deficient vestibular function and attenuate imbalance in Parkinson's disease. The presumed therapeutic mode of nGVS is stochastic resonance (SR), a mechanism by which weak sensory noise stimulation can enhance sensory information processing.

OBJECTIVE

To examine potential treatment effects of nGVS on postural instability in 16 patients with PSP with a clinically probable and [18F]PI-2620 tau-PET-positive PSP.

METHODS

Effects of nGVS of varying intensity (0-0.7 mA) on body sway were examined, while patients were standing with eyes closed on a posturographic force plate. We assumed a bell-shaped response curve with maximal sway reductions at intermediate nGVS intensities to be indicative of SR. An established SR-curve model was fitted on individual patient outcomes and three experienced human raters had to judge whether responses to nGVS were consistent with the exhibition of SR.

RESULTS

We found nGVS-induced reductions of body sway compatible with SR in 9 patients (56%) with optimal improvements of 31 ± 10%. In eight patients (50%), nGVS-induced sway reductions exceeded the minimal clinically important difference (improvement: 34 ± 5%), indicative of strong SR.

CONCLUSION

nGVS yielded clinically relevant reductions in body sway compatible with the exhibition of SR in vestibular sensorimotor pathways in at least half of the assessed patients. Non-invasive vestibular noise stimulation may be thus a well-tolerated treatment strategy to ameliorate postural symptoms in PSP.

No evidence for effects of low-intensity vestibular noise stimulation on mild-to-moderate gait impairments in patients with Parkinson's disease

BACKGROUND

Gait impairment is a key feature in later stages of Parkinson's disease (PD), which often responds poorly to pharmacological therapies. Neuromodulatory treatment by low-intensity noisy galvanic vestibular stimulation (nGVS) has indicated positive effects on postural instability in PD, which may possibly be conveyed to improvement of dynamic gait dysfunction.

OBJECTIVE

To investigate the effects of individually tuned nGVS on normal and cognitively challenged walking in PD patients with mild-to-moderate gait dysfunction.

METHODS

Effects of nGVS of varying intensities (0-0.7 mA) on body sway were examined in 32 patients with PD (ON medication state, Hoehn and Yahr: 2.3 ± 0.5), who were standing with eyes closed on a posturographic force plate. Treatment response and optimal nGVS stimulation intensity were determined on an individual patient level. In a second step, the effects of optimal nGVS vs. sham treatment on walking with preferred speed and with a cognitive dual task were investigated by assessment of spatiotemporal gait parameters on a pressure-sensitive gait carpet.

RESULTS

Evaluation of individual balance responses yielded that 59% of patients displayed a beneficial balance response to nGVS treatment with an average optimal improvement of 23%. However, optimal nGVS had no effects on gait parameters neither for the normal nor the cognitively challenged walking condition compared to sham stimulation irrespective of the nGVS responder status.

CONCLUSIONS

Low-intensity nGVS seems to have differential treatment effects on static postural imbalance and continuous gait dysfunction in PD, which could be explained by a selective modulation of midbrain-thalamic circuits of balance control.

Simple model to incorporate statistical noise based on a modified hodgkin-huxley approach for external electrical field driven neural responses

Noise activity is known to affect neural networks, enhance the system response to weak external signals, and lead to stochastic resonance phenomenon that can effectively amplify signals in nonlinear systems. In most treatments, channel noise has been modeled based on multi-state Markov descriptions or the use stochastic differential equation models. Here we probe a computationally simple approach based on a minor modification of the traditional Hodgkin-Huxley approach to embed noise in neural response. Results obtained from numerous simulations with different excitation frequencies and noise amplitudes for the action potential firing show very good agreement with output obtained from well-established models. Furthermore, results from the Mann-Whitney U Test reveal a statistically insignificant difference. The distribution of the time interval between successive potential spikes obtained from this simple approach compared very well with the results of complicated Fox and Lu type methods at much reduced computational cost. This present method could also possibly be applied to the analysis of spatial variations and/or differences in characteristics of random incident electromagnetic signals.

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.

How we perceive the width of grasped objects: Insights into the central processes that govern proprioceptive judgements

Low-level proprioceptive judgements involve a single frame of reference, whereas high-level proprioceptive judgements are made across different frames of reference. The present study systematically compared low-level (grasp → $\rightarrow$ grasp) and high-level (vision → $\rightarrow$ grasp, grasp → $\rightarrow$ vision) proprioceptive tasks, and quantified the consistency of grasp → $\rightarrow$ vision and possible reciprocal nature of related high-level proprioceptive tasks. Experiment 1 (n = 30) compared performance across vision → $\rightarrow$ grasp, a grasp → $\rightarrow$ vision and a grasp → $\rightarrow$ grasp tasks. Experiment 2 (n = 30) compared performance on the grasp → $\rightarrow$ vision task between hands and over time. Participants were accurate (mean absolute error 0.27 cm [0.20 to 0.34]; mean [95% CI]) and precise (R 2 $R^2$ = 0.95 [0.93 to 0.96]) for grasp → $\rightarrow$ grasp judgements, with a strong correlation between outcomes (r = -0.85 [-0.93 to -0.70]). Accuracy and precision decreased in the two high-level tasks (R 2 $R^2$ = 0.86 and 0.89; mean absolute error = 1.34 and 1.41 cm), with most participants overestimating perceived width for the vision → $\rightarrow$ grasp task and underestimating it for grasp → $\rightarrow$ vision task. There was minimal correlation between accuracy and precision for these two tasks. Converging evidence indicated performance was largely reciprocal (inverse) between the vision → $\rightarrow$ grasp and grasp → $\rightarrow$ vision tasks. Performance on the grasp → $\rightarrow$ vision task was consistent between dominant and non-dominant hands, and across repeated sessions a day or week apart. Overall, there are fundamental differences between low- and high-level proprioceptive judgements that reflect fundamental differences in the cortical processes that underpin these perceptions. Moreover, the central transformations that govern high-level proprioceptive judgements of grasp are personalised, stable and reciprocal for reciprocal tasks. KEY POINTS: Low-level proprioceptive judgements involve a single frame of reference (e.g. indicating the width of a grasped object by selecting from a series of objects of different width), whereas high-level proprioceptive judgements are made across different frames of reference (e.g. indicating the width of a grasped object by selecting from a series of visible lines of different length). We highlight fundamental differences in the precision and accuracy of low- and high-level proprioceptive judgements. We provide converging evidence that the neural transformations between frames of reference that govern high-level proprioceptive judgements of grasp are personalised, stable and reciprocal for reciprocal tasks. This stability is likely key to precise judgements and accurate predictions in high-level proprioception.

Theta oscillons in behaving rats

Recently discovered constituents of the brain waves-the oscillons -provide high-resolution representation of the extracellular field dynamics. Here we study the most robust, highest-amplitude oscillons that manifest in actively behaving rats and generally correspond to the traditional θ -waves. We show that the resemblances between θ -oscillons and the conventional θ -waves apply to the ballpark characteristics-mean frequencies, amplitudes, and bandwidths. In addition, both hippocampal and cortical oscillons exhibit a number of intricate, behavior-attuned, transient properties that suggest a new vantage point for understanding the θ -rhythms' structure, origins and functions. We demonstrate that oscillons are frequency-modulated waves, with speed-controlled parameters, embedded into a noise background. We also use a basic model of neuronal synchronization to contextualize and to interpret the observed phenomena. In particular, we argue that the synchronicity level in physiological networks is fairly weak and modulated by the animal's locomotion.

Stochastic brain dynamics exhibits differential regional distribution and maturation-related changes

Functional magnetic resonance imaging (fMRI) is a powerful non-invasive method for studying brain function by analyzing blood oxygenation level-dependent (BOLD) signals. These signals arise from intricate interplays of deterministic and stochastic biological elements. Quantifying the stochastic part is challenging due to its reliance on assumptions about the deterministic segment. We present a methodological framework to estimate intrinsic stochastic brain dynamics in fMRI data without assuming deterministic dynamics. Our approach utilizes Approximate Entropy and its behavior in noisy series to identify and characterize dynamical noise in unobservable fMRI dynamics. Applied to extensive fMRI datasets (645 Cam-CAN, 1086 Human Connectome Project subjects), we explore lifelong maturation of intrinsic brain noise. Findings indicate 10% to 60% of fMRI signal power is due to intrinsic stochastic brain elements, varying by age. These components demonstrate a physiological role of neural noise which shows a distinct distributions across brain regions and increase linearly during maturation.

Controlling spatiotemporal dynamics of neural networks by Lévy noise

We explore numerically how additive Lévy noise influences the spatiotemporal dynamics of a neural network of nonlocally coupled FitzHugh-Nagumo oscillators. Without noise, the network can exhibit various partial or cluster synchronization patterns, such as chimera and solitary states, which can also coexist in the network for certain values of the control parameters. Our studies show that these structures demonstrate different responses to additive Lévy noise and, thus, the dynamics of the neural network can be effectively controlled by varying the scale parameter and the stability index of Lévy noise. Specifically, introducing Lévy noise in the multistability mode can increase the probability of observing chimera states while suppressing solitary states. Nonetheless, decreasing the stability parameter enables one to diminish the noise effect on chimera states and amplify it on solitary states.

Mechanisms underlying treatment effects of vestibular noise stimulation on postural instability in patients with bilateral vestibulopathy

BACKGROUND

Previous studies indicate that imbalance in patients with bilateral vestibulopathy (BVP) may be reduced by treatment with low-intensity noisy galvanic vestibular stimulation (nGVS).

OBJECTIVE

To elucidate the potential mechanisms underlying this therapeutic effect. In particular, we determined whether nGVS-induced balance improvements in patients are compatible with stochastic resonance (SR)-a mechanism by which weak noise stimulation can paradoxically enhance sensory signal processing.

METHODS

Effects of nGVS of varying intensities (0-0.7 mA) on body sway were examined in 19 patients with BVP standing with eye closed on a posturographic force plate. We assumed a bell-shaped response curve with maximal sway reductions at intermediate nGVS intensities to be indicative of SR. An established SR curve model was fitted on individual patient outcomes, and three experienced human raters had to judge whether responses to nGVS were consistent with the exhibition of SR.

RESULTS

nGVS-induced reductions of body sway compatible with SR were found in 12 patients (63%) with optimal improvements of 31 ± 21%. In 10 patients (53%), nGVS-induced sway reductions exceeded the minimally important clinical difference (optimal improvement: 35 ± 21%), indicative of strong SR. This beneficial effect was more likely in patients with severe vestibular loss (i.e. lower video head impulse test gain; R = 0.663; p = 0.002) and considerable postural imbalance (baseline body sway; R = 0.616; p = 0.005).

CONCLUSIONS

More than half of the assessed patients showed robust improvements in postural balance compatible with SR when treated with nGVS. In particular, patients with a higher burden of disease may benefit from the non-invasive and well-tolerated treatment with nGVS.

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.

Noisy galvanic vestibular stimulation influences head stability in young healthy adults while standing on a moving platform

BACKGROUND

The ability to stand with eyes closed on a sinusoidal translational moving platform may be affected by spatial orientation owing to vestibular input information. Moreover, changes in the frequency of the moving platform may affect the sensory reweighting through somatosensory and vestibular sensations. However, it is unclear whether noisy galvanic vestibular stimulation (nGVS), which activates vestibular-related brain regions, affects the stability of individuals standing on a platform moving at different frequencies.

RESEARCH QUESTION

Do vestibular stimulation by nGVS and changes in the frequency of translationally moving platforms affect the standing stability of individuals?

METHODS

Thirty-one healthy young adult participants were provided both sham and nGVS interventions while they maintained a static standing position, with their eyes closed, on an anterior-posterior sinusoidal translation platform. The nGVS was adapted to an optimal intensity below the perceptual threshold (frequency band: 100-640 Hz), and the sham stimulus was adapted to 0 µA. The participants were randomly assessed for postural stability at 0.2, 0.6, and 1.2 Hz moving platform frequencies for 80 s each under both stimulus conditions. Postural stability was calculated as the root mean square (RMS) sway from head accelerations in the anteroposterior (AP) and mediolateral (ML) directions for 50 s between 20 and 70 s during the 80 s period, measured using an inertial sensor placed on the external occipital ridge.

RESULTS

nGVS significantly reduced the RMS sway of head acceleration in the AP direction compared with sham stimulation. Furthermore, nGVS significantly reduced RMS sway in the ML direction compared with sham stimulation at a 1.2 Hz moving platform oscillation.

SIGNIFICANCE

These findings suggest that postural adjustment by the vestibular system influences head stability on a moving platform at specific sinusoidal translation frequencies, suggesting that nGVS may reduce head sway.

Changes in vestibular-related responses to combined noisy galvanic vestibular stimulation and cerebellar transcranial direct current stimulation

Vestibular nuclei and cerebellar function comprise vestibular neural networks that control vestibular-related responses. However, the vestibular-related responses to simultaneous stimulation of these regions are unclear. This study aimed to examine whether the combination of noisy galvanic vestibular stimulation (nGVS) and cerebellar transcranial direct current stimulation (ctDCS) using a complex transcranial electrical stimulation device alters vestibular-dominant standing stability and vestibulo-ocular reflex (VOR) function. The center of foot pressure (COP) sway and VOR of participants (28 healthy, young adults) were assessed under four conditions of transcranial electrical stimulation using nGVS and ctDCS. The COP was calculated with the participant standing on a soft-foam surface with eyes closed using a force plate to evaluate body sway. VOR measurements were collected via passive head movements and fixation on a target projected onto the front wall using a video head impulse test (vHIT). VOR gain was calculated in six directions using a semicircular canal structure based on the ratio of eye movement to head movement. The nGVS + ctDCS and nGVS + sham ctDCS conditions decreased COP sway compared to the sham nGVS + ctDCS and sham nGVS + sham ctDCS conditions. No significant differences were observed in the main effect of stimulation or the interaction of stimulation and direction on the vHIT parameters. The results of this study suggest that postural stability may be independently affected by nGVS. Our findings contribute to the basic neurological foundation for the clinical application of neurorehabilitation using transcranial electrical stimulation of the vestibular system.

Effects of transcranial electrical stimulation of the right posterior parietal cortex on physical control responses

The posterior parietal cortex plays an important role in postural stability by adapting to changes in input from the visual, vestibular, and proprioceptive systems. However, little is known regarding whether transcranial electrical stimulation of the posterior parietal cortex affects reactive postural responses. This study aimed to investigate changes in physical control responses to anodal and cathodal transcranial direct current stimulation and transcranial random noise stimulation of the right posterior parietal cortex using a simultaneous inertial measurement unit. The joint movements of the lower limb of 33 healthy volunteers were measured while standing on a soft-foam surface with eyes closed during various stimulation modalities. These modalities included anodal, cathodal transcranial direct current stimulation, and sham stimulation in Experiment 1, and transcranial random noise and sham stimulations in Experiment 2. The results showed that cathodal stimulation significantly decreased the joint angular velocity in the hip rotation, ankle inversion-eversion, and abduction-adduction directions compared to anodal or sham stimulation in Experiment 1. In contrast, there were no significant differences in physical control responses with transcranial random noise stimulation coeducation in Experiment 2. These findings suggest that transcranial electrical stimulation of the right posterior parietal cortex may modulate physical control responses; however, the effect depends on the stimulus modality.

Behavioral Motor Performance

The human sensorimotor control system has exceptional abilities to perform skillful actions. We easily switch between strenuous tasks that involve brute force, such as lifting a heavy sewing machine, and delicate movements such as threading a needle in the same machine. Using a structure with different control architectures, the motor system is capable of updating its ability to perform through our daily interaction with the fluctuating environment. However, there are issues that make this a difficult computational problem for the brain to solve. The brain needs to control a nonlinear, nonstationary neuromuscular system, with redundant and occasionally undesired degrees of freedom, in an uncertain environment using a body in which information transmission is subject to delays and noise. To gain insight into the mechanisms of motor control, here we survey movement laws and invariances that shape our everyday motion. We then examine the major solutions to each of these problems in the three parts of the sensorimotor control system, sensing, planning, and acting. We focus on how the sensory system, the control architectures, and the structure and operation of the muscles serve as complementary mechanisms to overcome deviations and disturbances to motor behavior and give rise to skillful motor performance. We conclude with possible future research directions based on suggested links between the operation of the sensorimotor system across the movement stages. © 2024 American Physiological Society. Compr Physiol 14:5179-5224, 2024.

Characterising modulatory effects of high-intensity high frequency transcranial random noise stimulation using the perceptual template model

Neural noise is an inherent property of all nervous systems. However, our understanding of the mechanisms by which noise influences perception is still limited. To elucidate this relationship, we require techniques that can safely modulate noise in humans. Transcranial random noise stimulation (tRNS) has been proposed to induce noise into cortical processing areas according to the principles of stochastic resonance (SR). Specifically, it has been demonstrated that small to moderate intensities of noise improve performance. To date, however, high intensity tRNS effects on neural noise levels have not been directly quantified, nor have the detrimental effects proposed by SR been demonstrated in early visual function. Here, we applied 3 mA high-frequency tRNS to primary visual cortex during an orientation-discrimination task across increasing external noise levels and used the Perceptual Template Model to quantify the mechanisms by which noise changes perceptual performance in healthy observers. Results show that, at a group level, high-intensity tRNS worsened perceptual performance. Our computational analysis reveals that this change in performance was underpinned by an increased amount of additive noise and a reduced ability to filter external noise compared to sham stimulation. Interestingly, while most observers experienced detrimental effects, a subset of participants demonstrated improved performance. Preliminary evidence suggests that differences in baseline internal noise levels might account for these individual differences. Together, these results refine our understanding of the mechanisms underlying the influence of neural noise on perception and have important implications for the application of tRNS as a research tool.

Super-resolved q-space learning of diffusion MRI

BACKGROUND

Diffusion magnetic resonance imaging (dMRI) provides a powerful tool to non-invasively investigate neural structures in the living human brain. Nevertheless, its reconstruction performance on neural structures relies on the number of diffusion gradients in the q-space. High-angular (HA) dMRI requires a long scan time, limiting its use in clinical practice, whereas directly reducing the number of diffusion gradients would lead to the underestimation of neural structures.

PURPOSE

We propose a deep compressive sensing-based q-space learning (DCS-qL) approach to estimate HA dMRI from low-angular dMRI.

METHODS

In DCS-qL, we design the deep network architecture by unfolding the proximal gradient descent procedure that addresses the compressive sense problem. In addition, we exploit a lifting scheme to design a network structure with reversible transform properties. For implementation, we apply a self-supervised regression to enhance the signal-to-noise ratio of diffusion data. Then, we utilize a semantic information-guided patch-based mapping strategy for feature extraction, which introduces multiple network branches to handle patches with different tissue labels.

RESULTS

Experimental results show that the proposed approach can yield a promising performance on the tasks of reconstructed HA dMRI images, microstructural indices of neurite orientation dispersion and density imaging, fiber orientation distribution, and fiber bundle estimation.

CONCLUSIONS

The proposed method achieves more accurate neural structures than competing approaches.

Spatiotemporal dedifferentiation of the global brain signal topography along the adult lifespan

Age-related variations in many regions and/or networks of the human brain have been uncovered using resting-state functional magnetic resonance imaging. However, these findings did not account for the dynamical effect the brain's global activity (global signal [GS]) causes on local characteristics, which is measured by GS topography. To address this gap, we tested GS topography including its correlation with age using a large-scale cross-sectional adult lifespan dataset (n = 492). Both GS topography and its variation with age showed frequency-specific patterns, reflecting the spatiotemporal characteristics of the dynamic change of GS topography with age. A general trend toward dedifferentiation of GS topography with age was observed in both spatial (i.e., less differences of GS between different regions) and temporal (i.e., less differences of GS between different frequencies) dimensions. Further, methodological control analyses suggested that although most age-related dedifferentiation effects remained across different preprocessing strategies, some were triggered by neuro-vascular coupling and physiological noises. Together, these results provide the first evidence for age-related effects on global brain activity and its topographic-dynamic representation in terms of spatiotemporal dedifferentiation.

Role of asymmetry and external noise in the development and synchronization of oscillations in the analog Hopfield neural networks with time delay

The analog Hopfield neural network with time delay and random connections has been studied for its similarities in activity to human electroencephalogram and its usefulness in other areas of the applied sciences such as speech recognition, image analysis, and electrocardiogram modeling. Our goal here is to understand the mechanisms that affect the rhythmic activity in the neural network and how the addition of a Gaussian noise contributes to the network behavior. The neural network studied is composed of ten identical neurons. We investigated the excitatory and inhibitory networks with symmetric (square matrix) and asymmetric (triangular matrix) connections. The differential equations that model the network are solved numerically using the stochastic second-order Runge-Kutta method. Without noise, the neural networks with symmetric and asymmetric matrices possessed different synchronization properties: fully connected networks were synchronized both in time and in amplitude, while asymmetric networks were synchronized in time only. Saturation outputs of the excitatory neural networks do not depend on the time delay, whereas saturation oscillation amplitudes of inhibitory networks increase with the time delay until the steady state. The addition of the Gaussian noise is shown to significantly amplify small-amplitude oscillations, dramatically accelerates the rate of amplitude growth to saturation, and changes synchronization properties of the neural network outputs.

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.

Repetitive Low-Intensity Vestibular Noise Stimulation Partly Reverses Behavioral and Brain Activity Changes following Bilateral Vestibular Loss in Rats

Low-intensity noisy galvanic vestibular stimulation (nGVS) can improve static and dynamic postural deficits in patients with bilateral vestibular loss (BVL). In this study, we aimed to explore the neurophysiological and neuroanatomical substrates underlying nGVS treatment effects in a rat model of BVL. Regional brain activation patterns and behavioral responses to a repeated 30 min nGVS treatment in comparison to sham stimulation were investigated by serial whole-brain 18F-FDG-PET measurements and quantitative locomotor assessments before and at nine consecutive time points up to 60 days after the chemical bilateral labyrinthectomy (BL). The 18F-FDG-PET revealed a broad nGVS-induced modulation on regional brain activation patterns encompassing biologically plausible brain networks in the brainstem, cerebellum, multisensory cortex, and basal ganglia during the entire observation period post-BL. nGVS broadly reversed brain activity adaptions occurring in the natural course post-BL. The parallel behavioral locomotor assessment demonstrated a beneficial treatment effect of nGVS on sensory-ataxic gait alterations, particularly in the early stage of post-BL recovery. Stimulation-induced locomotor improvements were finally linked to nGVS brain activity responses in the brainstem, hemispheric motor, and limbic networks. In conclusion, combined 18F-FDG-PET and locomotor analysis discloses the potential neurophysiological and neuroanatomical substrates that mediate previously observed therapeutic nGVS effects on postural deficits in patients with BVL.

Exploiting noise as a resource for computation and learning in spiking neural networks

Networks of spiking neurons underpin the extraordinary information-processing capabilities of the brain and have become pillar models in neuromorphic artificial intelligence. Despite extensive research on spiking neural networks (SNNs), most studies are established on deterministic models, overlooking the inherent non-deterministic, noisy nature of neural computations. This study introduces the noisy SNN (NSNN) and the noise-driven learning (NDL) rule by incorporating noisy neuronal dynamics to exploit the computational advantages of noisy neural processing. The NSNN provides a theoretical framework that yields scalable, flexible, and reliable computation and learning. We demonstrate that this framework leads to spiking neural models with competitive performance, improved robustness against challenging perturbations compared with deterministic SNNs, and better reproducing probabilistic computation in neural coding. Generally, this study offers a powerful and easy-to-use tool for machine learning, neuromorphic intelligence practitioners, and computational neuroscience researchers.

Optimal Design of Galvanic Vestibular Stimulation for Patients with Vestibulopathy and Cerebellar Disorders

OBJECTIVES

Galvanic vestibular stimulation (GVS) has shown positive outcomes in various neurological and psychiatric disorders, such as enhancing postural balance and cognitive functions. In order to expedite the practical application of GVS in clinical settings, our objective was to determine the best GVS parameters for patients with vestibulopathy and cerebellar disorders using optimal design calculation.

METHODS

A total of 31 patients (26 males, mean age 57.03 ± 14.75 years, age range 22-82 years) with either unilateral or bilateral vestibulopathy (n = 18) or cerebellar ataxia (n = 13) were enrolled in the study. The GVS intervention included three parameters, waveform (sinusoidal, direct current [DC], and noisy), amplitude (0.4, 0.8, and 1.2 mA), and duration of stimulation (5 and 30 min), resulting in a total of 18 GVS intervention modes as input variables. To evaluate the effectiveness of GVS, clinical vertigo and gait assessments were conducted using the Dizziness Visual Analogue Scale (D-VAS), Activities-specific Balance Confidence Scale (ABC), and Scale for Assessment and Rating of Ataxia (SARA) as output variables. Optimal design and local sensitivity analysis were employed to determine the most optimal GVS modes.

RESULTS

Patients with unilateral vestibulopathy experienced the most favorable results with either noisy or sinusoidal GVS at 0.4 mA amplitude for 30 min, followed by DC GVS at 0.8 mA amplitude for 5 min. Noisy GVS at 0.8 or 0.4 mA amplitude for 30 min demonstrated the most beneficial effects in patients with bilateral vestibulopathy. For patients with cerebellar ataxia, the optimal choices were noisy GVS with 0.8 or 0.4 mA amplitude for 5 or 30 min.

CONCLUSIONS

This study is the first to utilize design optimization methods to identify the GVS stimulation parameters that are tailored to individual-specific characteristics of dizziness and imbalance. A sensitivity analysis was carried out along with the optimal design to offset the constraints of a limited sample size, resulting in the identification of the most efficient GVS modes for patients suffering from vestibular and cerebellar disorders.

Chimera resonance in networks of chaotic maps

We explore numerically the impact of additive Gaussian noise on the spatiotemporal dynamics of ring networks of nonlocally coupled chaotic maps. The local dynamics of network nodes is described by the logistic map, the Ricker map, and the Henon map. 2D distributions of the probability of observing chimera states are constructed in terms of the coupling strength and the noise intensity and for several choices of the local dynamics parameters. It is shown that the coupling strength range can be the widest at a certain optimum noise level at which chimera states are observed with a high probability for a large number of different realizations of randomly distributed initial conditions and noise sources. This phenomenon demonstrates a constructive role of noise in analogy with the effects of stochastic and coherence resonance and may be referred to as chimera resonance.

Modular architecture facilitates noise-driven control of synchrony in neuronal networks

High-level information processing in the mammalian cortex requires both segregated processing in specialized circuits and integration across multiple circuits. One possible way to implement these seemingly opposing demands is by flexibly switching between states with different levels of synchrony. However, the mechanisms behind the control of complex synchronization patterns in neuronal networks remain elusive. Here, we use precision neuroengineering to manipulate and stimulate networks of cortical neurons in vitro, in combination with an in silico model of spiking neurons and a mesoscopic model of stochastically coupled modules to show that (i) a modular architecture enhances the sensitivity of the network to noise delivered as external asynchronous stimulation and that (ii) the persistent depletion of synaptic resources in stimulated neurons is the underlying mechanism for this effect. Together, our results demonstrate that the inherent dynamical state in structured networks of excitable units is determined by both its modular architecture and the properties of the external inputs.

A Bio-Inspired Chaos Sensor Model Based on the Perceptron Neural Network: Machine Learning Concept and Application for Computational Neuro-Science

The study presents a bio-inspired chaos sensor model based on the perceptron neural network for the estimation of entropy of spike train in neurodynamic systems. After training, the sensor on perceptron, having 50 neurons in the hidden layer and 1 neuron at the output, approximates the fuzzy entropy of a short time series with high accuracy, with a determination coefficient of R2~0.9. The Hindmarsh-Rose spike model was used to generate time series of spike intervals, and datasets for training and testing the perceptron. The selection of the hyperparameters of the perceptron model and the estimation of the sensor accuracy were performed using the K-block cross-validation method. Even for a hidden layer with one neuron, the model approximates the fuzzy entropy with good results and the metric R2~0.5 ÷ 0.8. In a simplified model with one neuron and equal weights in the first layer, the principle of approximation is based on the linear transformation of the average value of the time series into the entropy value. An example of using the chaos sensor on spike train of action potential recordings from the L5 dorsal rootlet of rat is provided. The bio-inspired chaos sensor model based on an ensemble of neurons is able to dynamically track the chaotic behavior of a spike signal and transmit this information to other parts of the neurodynamic model for further processing. The study will be useful for specialists in the field of computational neuroscience, and also to create humanoid and animal robots, and bio-robots with limited resources.

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.

Subthreshold white noise vibration alters trembling sway in older adults

BACKGROUND

Somatosensory deficit is a significant contributor to falls in older adults. Stochastic resonance has shown promise in recent studies of somatosensation-based balance disorders, improving many measures of stability both inside and outside of the clinic. However, our understanding of this effect from a physiological perspective is poorly understood. Therefore, the primary goal of this study is to explore the influence of subthreshold vibratory stimulation on sway under the rambling-trembling framework.

METHODS

10 Healthy older adults (60-65 years) volunteered to participate in this study. Each participant underwent two randomized testing sessions on separate days, one experimental and one placebo. During each session, the participants' baseline sway was captured during one 90-s quiet standing trial. Their sensation threshold was then captured using a custom vibratory mat and 4-2-1 vibration perception threshold test. Finally, participants completed another 90-s quiet standing trial while the vibratory mat vibrated at 90% of their measured threshold (if experimental) or with the mat off (if placebo). While they completed these trials, an AMTI force plate collected force and moment data in the anteroposterior (AP) and mediolateral (ML), from which the center of pressure (COP), rambling (RM), and trembling (TR) time series were calculated. From each of these time series, range, variability (root-mean-square), and predictability (sample entropy) were extracted. One-tailed paired t-tests were used to compare baseline and during-vibration measures.

RESULTS

No significant differences were found during the placebo session. For the experimental session, significant increases were found in AP TR range, ML TR RMS, AP COP predictability, and AP & ML TR predictability. The TR time series was particularly sensitive to vibration, suggesting a strong influence on peripheral/spinal mechanisms of postural control.

SIGNIFICANCE

Though it is unclear whether observed effects are indicative of "improvements" or not, it does suggest that there was a measurable effect of subthreshold vibration on sway. This knowledge should be utilized in future studies of stochastic resonance, potentially acting as a mode of customization, tailoring vibration location, duration, magnitude, and frequency content to achieve the desired effect. One day, this work may aid in our ability to treat somatosensation-based balance deficits, ultimately reducing the incidence and severity of falls in older adults.

Stability, change, and reliable individual differences in electroencephalography measures: a lifespan perspective on progress and opportunities

Electroencephalographic (EEG) methods have great potential to serve both basic and clinical science approaches to understand individual differences in human neural function. Importantly, the psychometric properties of EEG data, such as internal consistency and test-retest reliability, constrain their ability to differentiate individuals successfully. Rapid and recent technological and computational advancements in EEG research make it timely to revisit the topic of psychometric reliability in the context of individual difference analyses. Moreover, pediatric and clinical samples provide some of the most salient and urgent opportunities to apply individual difference approaches, but the changes these populations experience over time also provide unique challenges from a psychometric perspective. Here we take a developmental neuroscience perspective to consider progress and new opportunities for parsing the reliability and stability of individual differences in EEG measurements across the lifespan. We first conceptually map the different profiles of measurement reliability expected for different types of individual difference analyses over the lifespan. Next, we summarize and evaluate the state of the field's empirical knowledge and need for testing measurement reliability, both internal consistency and test-retest reliability, across EEG measures of power, event-related potentials, nonlinearity, and functional connectivity across ages. Finally, we highlight how standardized pre-processing software for EEG denoising and empirical metrics of individual data quality may be used to further improve EEG-based individual differences research moving forward. We also include recommendations and resources throughout that individual researchers can implement to improve the utility and reproducibility of individual differences analyses with EEG across the lifespan.

Regularization Effect of Random Node Fault/Noise on Gradient Descent Learning Algorithm

For decades, adding fault/noise during training by gradient descent has been a technique for getting a neural network (NN) tolerant to persistent fault/noise or getting an NN with better generalization. In recent years, this technique has been readvocated in deep learning to avoid overfitting. Yet, the objective function of such fault/noise injection learning has been misinterpreted as the desired measure (i.e., the expected mean squared error (mse) of the training samples) of the NN with the same fault/noise. The aims of this article are: 1) to clarify the above misconception and 2) investigate the actual regularization effect of adding node fault/noise when training by gradient descent. Based on the previous works on adding fault/noise during training, we speculate the reason why the misconception appears. In the sequel, it is shown that the learning objective of adding random node fault during gradient descent learning (GDL) for a multilayer perceptron (MLP) is identical to the desired measure of the MLP with the same fault. If additive (resp. multiplicative) node noise is added during GDL for an MLP, the learning objective is not identical to the desired measure of the MLP with such noise. For radial basis function (RBF) networks, it is shown that the learning objective is identical to the corresponding desired measure for all three fault/noise conditions. Empirical evidence is presented to support the theoretical results and, hence, clarify the misconception that the objective function of a fault/noise injection learning might not be interpreted as the desired measure of the NN with the same fault/noise. Afterward, the regularization effect of adding node fault/noise during training is revealed for the case of RBF networks. Notably, it is shown that the regularization effect of adding additive or multiplicative node noise (MNN) during training an RBF is reducing network complexity. Applying dropout regularization in RBF networks, its effect is the same as adding MNN during training.

Noise improves the association between effects of local stimulation and structural degree of brain networks

Stimulation to local areas remarkably affects brain activity patterns, which can be exploited to investigate neural bases of cognitive function and modify pathological brain statuses. There has been growing interest in exploring the fundamental action mechanisms of local stimulation. Nevertheless, how noise amplitude, an essential element in neural dynamics, influences stimulation-induced brain states remains unknown. Here, we systematically examine the effects of local stimulation by using a large-scale biophysical model under different combinations of noise amplitudes and stimulation sites. We demonstrate that noise amplitude nonlinearly and heterogeneously tunes the stimulation effects from both regional and network perspectives. Furthermore, by incorporating the role of the anatomical network, we show that the peak frequencies of unstimulated areas at different stimulation sites averaged across noise amplitudes are highly positively related to structural connectivity. Crucially, the association between the overall changes in functional connectivity as well as the alterations in the constraints imposed by structural connectivity with the structural degree of stimulation sites is nonmonotonically influenced by the noise amplitude, with the association increasing in specific noise amplitude ranges. Moreover, the impacts of local stimulation of cognitive systems depend on the complex interplay between the noise amplitude and average structural degree. Overall, this work provides theoretical insights into how noise amplitude and network structure jointly modulate brain dynamics during stimulation and introduces possibilities for better predicting and controlling stimulation outcomes.

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.

Combined effects of spike-timing-dependent plasticity and homeostatic structural plasticity on coherence resonance

Efficient processing and transfer of information in neurons have been linked to noise-induced resonance phenomena such as coherence resonance (CR), and adaptive rules in neural networks have been mostly linked to two prevalent mechanisms: spike-timing-dependent plasticity (STDP) and homeostatic structural plasticity (HSP). Thus this paper investigates CR in small-world and random adaptive networks of Hodgkin-Huxley neurons driven by STDP and HSP. Our numerical study indicates that the degree of CR strongly depends, and in different ways, on the adjusting rate parameter P, which controls STDP, on the characteristic rewiring frequency parameter F, which controls HSP, and on the parameters of the network topology. In particular, we found two robust behaviors. (i) Decreasing P (which enhances the weakening effect of STDP on synaptic weights) and decreasing F (which slows down the swapping rate of synapses between neurons) always leads to higher degrees of CR in small-world and random networks, provided that the synaptic time delay parameter τ_{c} has some appropriate values. (ii) Increasing the synaptic time delay τ_{c} induces multiple CR (MCR)-the occurrence of multiple peaks in the degree of coherence as τ_{c} changes-in small-world and random networks, with MCR becoming more pronounced at smaller values of P and F. Our results imply that STDP and HSP can jointly play an essential role in enhancing the time precision of firing necessary for optimal information processing and transfer in neural systems and could thus have applications in designing networks of noisy artificial neural circuits engineered to use CR to optimize information processing and transfer.

Noise and Financial Stylized Facts: A Stick Balancing Approach

In this work, we address the beneficial role of noise in two different contexts, the human brain and financial markets. In particular, the similitude between the ability of financial markets to maintain in equilibrium asset prices is compared with the ability of the human nervous system to balance a stick on a fingertip. Numerical simulations of the human stick balancing phenomenon show that after the introduction of a small quantity of noise and a proper calibration of the main control parameters, intermittent changes in the angular velocity of the stick are able to reproduce the most basilar stylized facts involving price returns in financial markets. These results could also shed light on the relevance of the idea of the "planetary nervous system", already introduced elsewhere, in the financial context.

Noisy galvanic vestibular stimulation improves vestibular perception in bilateral vestibulopathy

BACKGROUND

Patients with bilateral vestibulopathy (BVP) suffer from impaired vestibular motion perception that is linked to deficits in spatial memory and navigation.

OBJECTIVE

To examine the potential therapeutic effect of imperceptible noisy galvanic vestibular stimulation (nGVS) on impaired vestibular perceptual performance in BVP.

METHODS

In 11 patients with BVP (mean age: 54.0 ± 8.3 years, 7 females), we initially determined the nGVS intensity that optimally stabilizes balance during a static posturographic assessment. Subsequently, effects of optimal nGVS vs. sham stimulation on vestibular motion perception were examined in randomized order. Vestibular perceptual performance was determined as direction recognition thresholds for head-centered roll tilt motion on a 6DOF motion platform in the absence of any visual or auditory motion cues.

RESULTS

For each patient, an nGVS intensity that optimally stabilized static balance compared to sham stimulation could be identified (mean 0.36 ± 0.16 mA). nGVS at optimal intensity resulted in lowered vestibular perceptual thresholds (0.94 ± 0.30 deg/s) compared to sham stimulation (1.67 ± 1.11 deg/s; p = 0.040). nGVS-induced improvements in vestibular perception were observed in 8 of 11 patients (73%) and were greater in patients with poorer perceptual performance during sham stimulation (R = - 0.791; p = 0.007).

CONCLUSIONS

nGVS is effective in improving impaired vestibular motion perception in patients with BVP, in particular in those patients with poor baseline perceptual performance. Imperceptible vestibular noise stimulation might thus offer a non-invasive approach to target BVP-related impairments in spatial memory, orientation, and navigation.

Influence of vibrotactile random noise on the smoothness of the grasp movement in patients with chemotherapy-induced peripheral neuropathy

Patients with chemotherapy-induced peripheral neuropathy (CIPN) often suffer from sensorimotor dysfunction of the distal portion of the extremities (e.g., loss of somatosensory sensation, numbness/tingling, difficulty typing on a keyboard, or difficulty buttoning/unbuttoning a shirt). The present study aimed to reveal the effects of subthreshold vibrotactile random noise stimulation on sensorimotor dysfunction in CIPN patients without exacerbating symptoms. Twenty-five patients with CIPN and 28 age-matched healthy adults participated in this study. To reveal the effects of subthreshold vibrotactile random noise stimulation on sensorimotor function, participants were asked to perform a tactile detection task and a grasp movement task during random noise stimulation delivered to the volar and dorsal wrist. We set three intensity conditions of the vibrotactile random noise: 0, 60, and 120% of the sensory threshold (Noise 0%, Noise 60%, and Noise 120% conditions). In the tactile detection task, a Semmes-Weinstein monofilament was applied to the volar surface of the tip of the index finger using standard testing measures. In the grasp movement task, the distance between the thumb and index finger was recorded while the participant attempted to grasp a target object, and the smoothness of the grasp movement was quantified by calculating normalized jerk in each experimental condition. The experimental data were compared using two-way repeated-measures analyses of variance with two factors: experimental condition (Noise 0, 60, 120%) × group (Healthy controls, CIPN patients). The tactile detection threshold and the smoothness of the grasp movement were only improved in the Noise 60% condition without exacerbating numbness/tingling in CIPN patients and healthy controls. The current study suggested that the development of treatment devices using stochastic resonance can improve sensorimotor function for CIPN patients.

How can artificial neural networks approximate the brain?

The article reviews the history development of artificial neural networks (ANNs), then compares the differences between ANNs and brain networks in their constituent unit, network architecture, and dynamic principle. The authors offer five points of suggestion for ANNs development and ten questions to be investigated further for the interdisciplinary field of brain simulation. Even though brain is a super-complex system with 1011 neurons, its intelligence does depend rather on the neuronal type and their energy supply mode than the number of neurons. It might be possible for ANN development to follow a new direction that is a combination of multiple modules with different architecture principle and multiple computation, rather than very large scale of neural networks with much more uniformed units and hidden layers.

Inter-day and intra-day variations in effective intensity of noisy galvanic vestibular stimulation to improve postural stability in bilateral vestibulopathy

BACKGROUND

The reproducibility of the effective intensity of noisy galvanic vestibular stimulation (nGVS) to improve postural stability is not well known.

OBJECTIVE

We aimed to investigate inter-day and intra-day variations in effective intensity in patients with bilateral vestibulopathy (BVP).

METHODS

Thirteen BVP patients were measured for center-of-pressure movements in the standing posture at five time points: morning of the first test day, morning and evening of the second test day, and morning and evening of the third test day. The mean velocity, the envelopment area, and the root mean square were measured in the eyes-closed condition for 30 s during nGVS application ranging from 0 to 1000μA. The effective intensity was defined as the intensity at which all the three parameters measured during the stimulation were simultaneously smaller than the values at baseline (0μA).

RESULTS

Seven of the 13 patients had a common effective intensity throughout the three test days. Six patients on the second test day and five patients on the third test day had no common effective intensity between morning and evening.

CONCLUSIONS

The effective intensity of nGVS changes depending on the time during the day as well as between the days.

Distinct Excitatory and Inhibitory Bump Wandering in a Stochastic Neural Field

Localized persistent cortical neural activity is a validated neural substrate of parametric working memory. Such activity "bumps" represent the continuous location of a cue over several seconds. Pyramidal (excitatory (E )) and interneuronal (inhibitory (I )) subpopulations exhibit tuned bumps of activity, linking neural dynamics to behavioral inaccuracies observed in memory recall. However, many bump attractor models collapse these subpopulations into a single joint E /I (lateral inhibitory) population and do not consider the role of interpopulation neural architecture and noise correlations. Both factors have a high potential to impinge upon the stochastic dynamics of these bumps, ultimately shaping behavioral response variance. In our study, we consider a neural field model with separate E /I populations and leverage asymptotic analysis to derive a nonlinear Langevin system describing E /I bump interactions. While the E bump attracts the I bump, the I bump stabilizes but can also repel the E bump, which can result in prolonged relaxation dynamics when both bumps are perturbed. Furthermore, the structure of noise correlations within and between subpopulations strongly shapes the variance in bump position. Surprisingly, higher interpopulation correlations reduce variance.

Resting-state BOLD signal variability is associated with individual differences in metacontrol

Numerous studies demonstrate that moment-to-moment neural variability is behaviorally relevant and beneficial for tasks and behaviors requiring cognitive flexibility. However, it remains unclear whether the positive effect of neural variability also holds for cognitive persistence. Moreover, different brain variability measures have been used in previous studies, yet comparisons between them are lacking. In the current study, we examined the association between resting-state BOLD signal variability and two metacontrol policies (i.e., persistence vs. flexibility). Brain variability was estimated from resting-state fMRI (rsfMRI) data using two different approaches (i.e., Standard Deviation (SD), and Mean Square Successive Difference (MSSD)) and metacontrol biases were assessed by three metacontrol-sensitive tasks. Results showed that brain variability measured by SD and MSSD was highly positively related. Critically, higher variability measured by MSSD in the attention network, parietal and frontal network, frontal and ACC network, parietal and motor network, and higher variability measured by SD in the parietal and motor network, parietal and frontal network were associated with reduced persistence (or greater flexibility) of metacontrol (i.e., larger Stroop effect or worse RAT performance). These results show that the beneficial effect of brain signal variability on cognitive control depends on the metacontrol states involved. Our study highlights the importance of temporal variability of rsfMRI activity in understanding the neural underpinnings of cognitive control.

Diverse coherence-resonance chimeras in coupled type-I excitable systems

Coherence-resonance chimera was discovered in [Phys. Rev. Lett. 117, 014102 (2016)10.1103/PhysRevLett.117.014102], which combines the effect of coherence-resonance and classical chimeras in the presence of noise in a network of type-II excitable systems. However, the same in a network of type-I excitable units has not been observed yet. In this paper we report the occurrence of coherence-resonance chimera in coupled type-I excitable systems. We consider a paradigmatic model of type-I excitability, namely, the saddle-node infinite period model, and show that the coherence-resonance chimera appears over an optimum range of noise intensity. Moreover, we discover a unique chimera pattern that is a mixture of classical chimera and the coherence-resonance chimera. We support our results using quantitative measures and map them in parameter space. This study reveals that the coherence-resonance chimera is a general chimera pattern and thus it deepens our understanding of role of noise in coupled excitable systems.

Optimal noise level for coding with tightly balanced networks of spiking neurons in the presence of transmission delays

Neural circuits consist of many noisy, slow components, with individual neurons subject to ion channel noise, axonal propagation delays, and unreliable and slow synaptic transmission. This raises a fundamental question: how can reliable computation emerge from such unreliable components? A classic strategy is to simply average over a population of N weakly-coupled neurons to achieve errors that scale as [Formula: see text]. But more interestingly, recent work has introduced networks of leaky integrate-and-fire (LIF) neurons that achieve coding errors that scale superclassically as 1/N by combining the principles of predictive coding and fast and tight inhibitory-excitatory balance. However, spike transmission delays preclude such fast inhibition, and computational studies have observed that such delays can cause pathological synchronization that in turn destroys superclassical coding performance. Intriguingly, it has also been observed in simulations that noise can actually improve coding performance, and that there exists some optimal level of noise that minimizes coding error. However, we lack a quantitative theory that describes this fascinating interplay between delays, noise and neural coding performance in spiking networks. In this work, we elucidate the mechanisms underpinning this beneficial role of noise by deriving analytical expressions for coding error as a function of spike propagation delay and noise levels in predictive coding tight-balance networks of LIF neurons. Furthermore, we compute the minimal coding error and the associated optimal noise level, finding that they grow as power-laws with the delay. Our analysis reveals quantitatively how optimal levels of noise can rescue neural coding performance in spiking neural networks with delays by preventing the build up of pathological synchrony without overwhelming the overall spiking dynamics. This analysis can serve as a foundation for the further study of precise computation in the presence of noise and delays in efficient spiking neural circuits.

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.

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.

Characteristic columnar connectivity caters to cortical computation: Replication, simulation, and evaluation of a microcircuit model

The neocortex, and with it the mammalian brain, achieves a level of computational efficiency like no other existing computational engine. A deeper understanding of its building blocks (cortical microcircuits), and their underlying computational principles is thus of paramount interest. To this end, we need reproducible computational models that can be analyzed, modified, extended and quantitatively compared. In this study, we further that aim by providing a replication of a seminal cortical column model. This model consists of noisy Hodgkin-Huxley neurons connected by dynamic synapses, whose connectivity scheme is based on empirical findings from intracellular recordings. Our analysis confirms the key original finding that the specific, data-based connectivity structure enhances the computational performance compared to a variety of alternatively structured control circuits. For this comparison, we use tasks based on spike patterns and rates that require the systems not only to have simple classification capabilities, but also to retain information over time and to be able to compute nonlinear functions. Going beyond the scope of the original study, we demonstrate that this finding is independent of the complexity of the neuron model, which further strengthens the argument that it is the connectivity which is crucial. Finally, a detailed analysis of the memory capabilities of the circuits reveals a stereotypical memory profile common across all circuit variants. Notably, the circuit with laminar structure does not retain stimulus any longer than any other circuit type. We therefore conclude that the model's computational advantage lies in a sharper representation of the stimuli.

Enhanced temporal complexity of EEG signals in older individuals with high cognitive functions

Recent studies suggest that the maintenance of cognitive function in the later life of older people is an essential factor contributing to mental wellbeing and physical health. Particularly, the risk of depression, sleep disorders, and Alzheimer's disease significantly increases in patients with mild cognitive impairment. To develop early treatment and prevention strategies for cognitive decline, it is necessary to individually identify the current state of cognitive function since the progression of cognitive decline varies among individuals. Therefore, the development of biomarkers that allow easier measurement of cognitive function in older individuals is relevant for hyperaged societies. One of the methods used to estimate cognitive function focuses on the temporal complexity of electroencephalography (EEG) signals. The characteristics of temporal complexity depend on the time scale, which reflects the range of neuron functional interactions. To capture the dynamics, composed of multiple time scales, multiscale entropy (MSE) analysis is effective for comprehensively assessing the neural activity underlying cognitive function in the brain. Thus, we hypothesized that EEG complexity analysis could serve to assess a wide range of cognitive functions in older adults. To validate our hypothesis, we divided older participants into two groups based on their cognitive function test scores: a high cognitive function group and a low cognitive function group, and applied MSE analysis to the measured EEG data of all participants. The results of the repeated-measures analysis of covariance using age and sex as a covariate in the MSE profile showed a significant difference between the high and low cognitive function groups (F = 10.18, p = 0.003) and the interaction of the group × electrodes (F = 3.93, p = 0.002). Subsequently, the results of the post-hoct-test showed high complexity on a slower time scale in the frontal, parietal, and temporal lobes in the high cognitive function group. This high complexity on a slow time scale reflects the activation of long-distance neural interactions among various brain regions to achieve high cognitive functions. This finding could facilitate the development of a tool for diagnosis of cognitive decline in older individuals.

Physiological noise facilitates multiplexed coding of vibrotactile-like signals in somatosensory cortex

Neurons can use different aspects of their spiking to simultaneously represent (multiplex) different features of a stimulus. For example, some pyramidal neurons in primary somatosensory cortex (S1) use the rate and timing of their spikes to, respectively, encode the intensity and frequency of vibrotactile stimuli. Doing so has several requirements. Because they fire at low rates, pyramidal neurons cannot entrain 1:1 with high-frequency (100 to 600 Hz) inputs and, instead, must skip (i.e., not respond to) some stimulus cycles. The proportion of skipped cycles must vary inversely with stimulus intensity for firing rate to encode stimulus intensity. Spikes must phase-lock to the stimulus for spike times (intervals) to encode stimulus frequency, but, in addition, skipping must occur irregularly to avoid aliasing. Using simulations and in vitro experiments in which mouse S1 pyramidal neurons were stimulated with inputs emulating those induced by vibrotactile stimuli, we show that fewer cycles are skipped as stimulus intensity increases, as required for rate coding, and that intrinsic or synaptic noise can induce irregular skipping without disrupting phase locking, as required for temporal coding. This occurs because noise can modulate the reliability without disrupting the precision of spikes evoked by small-amplitude, fast-onset signals. Specifically, in the fluctuation-driven regime associated with sparse spiking, rate and temporal coding are both paradoxically improved by the strong synaptic noise characteristic of the intact cortex. Our results demonstrate that multiplexed coding by S1 pyramidal neurons is not only feasible under in vivo conditions, but that background synaptic noise is actually beneficial.

Continuous learning of spiking networks trained with local rules

Artificial neural networks (ANNs) experience catastrophic forgetting (CF) during sequential learning. In contrast, the brain can learn continuously without any signs of catastrophic forgetting. Spiking neural networks (SNNs) are the next generation of ANNs with many features borrowed from biological neural networks. Thus, SNNs potentially promise better resilience to CF. In this paper, we study the susceptibility of SNNs to CF and test several biologically inspired methods for mitigating catastrophic forgetting. SNNs are trained with biologically plausible local training rules based on spike-timing-dependent plasticity (STDP). Local training prohibits the direct use of CF prevention methods based on gradients of a global loss function. We developed and tested the method to determine the importance of synapses (weights) based on stochastic Langevin dynamics without the need for the gradients. Several other methods of catastrophic forgetting prevention adapted from analog neural networks were tested as well. The experiments were performed on freely available datasets in the SpykeTorch environment.

How to discern external acoustic waves in a piezoelectric neuron under noise?

Biological neurons keep sensitive to external stimuli and appropriate firing modes can be triggered to give effective response to external chemical and physical signals. A piezoelectric neural circuit can perceive external voice and nonlinear vibration by generating equivalent piezoelectric voltage, which can generate an equivalent trans-membrane current for inducing a variety of firing modes in the neural activities. Biological neurons can receive external stimuli from more ion channels and synapse synchronously, but the further encoding and priority in mode selection are competitive. In particular, noisy disturbance and electromagnetic radiation make it more difficult in signals identification and mode selection in the firing patterns of neurons driven by multi-channel signals. In this paper, two different periodic signals accompanied by noise are used to excite the piezoelectric neural circuit, and the signal processing in the piezoelectric neuron driven by acoustic waves under noise is reproduced and explained. The physical energy of the piezoelectric neural circuit and Hamilton energy in the neuron driven by mixed signals are calculated to explain the biophysical mechanism of auditory neuron when external stimuli are applied. It is found that the neuron prefers to respond to the external stimulus with higher physical energy and the signal which can increase the Hamilton energy of the neuron. For example, stronger inputs used to inject higher energy and it is detected and responded more sensitively. The involvement of noise is helpful to detect the external signal under stochastic resonance, and the additive noise changes the excitability of neuron as the external stimulus. The results indicate that energy controls the firing patterns and mode selection in neurons, and it provides clues to control the neural activities by injecting appropriate energy into the neurons and network.

On the Spatial Distribution of Temporal Complexity in Resting State and Task Functional MRI

Measuring the temporal complexity of functional MRI (fMRI) time series is one approach to assess how brain activity changes over time. In fact, hemodynamic response of the brain is known to exhibit critical behaviour at the edge between order and disorder. In this study, we aimed to revisit the spatial distribution of temporal complexity in resting state and task fMRI of 100 unrelated subjects from the Human Connectome Project (HCP). First, we compared two common choices of complexity measures, i.e., Hurst exponent and multiscale entropy, and observed a high spatial similarity between them. Second, we considered four tasks in the HCP dataset (Language, Motor, Social, and Working Memory) and found high task-specific complexity, even when the task design was regressed out. For the significance thresholding of brain complexity maps, we used a statistical framework based on graph signal processing that incorporates the structural connectome to develop the null distributions of fMRI complexity. The results suggest that the frontoparietal, dorsal attention, visual, and default mode networks represent stronger complex behaviour than the rest of the brain, irrespective of the task engagement. In sum, the findings support the hypothesis of fMRI temporal complexity as a marker of cognition.