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This article deals with the stochastic resonance (SR) phenomenon experimentally observed in HfO2-based memristors. The SR impact on the binary spike time-dependent plasticity (STDP) protocol at the device level was investigated. We demonstrate that the two extreme conductance states of the device that represent the synaptic weights in neuromorphic systems can be better distinguished with the incorporation of Gaussian noise into the bias signal. This technique allows setting the memristor conductance which is directly related to the overlap between the pre- and postsynaptic pulses. The study is reproduced in the LTSPICE simulator using the dynamic memdiode model (DMM) for memristors., Postprint (published version)

This paper discusses a method to enhance fingertip tactile sensitivity by applying a vibrotactile noise at the wrist. This is an application of stochastic resonance to the field of haptics. We consider that the tactile sensitivity of the fingertip improves when a sufficiently large noise is propagated to it from the wrist. However, fingertip tactile sensitivity decreases when a large noise that humans can perceive is applied to the wrist. Therefore, in this paper, we cool the wrist skin to reduce the wrist's tactile sensitivity to noise. This allows us to apply noise that is large, but still imperceptible, at the wrist and thus to propagate it to the fingertip. On the basis of these procedures, we propose a method to enhance fingertip tactile sensitivity. Further, we carry out several experiments and confirm that the proposed method improves fingertip tactile sensitivity.

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.

The stochastic resonance (SR) effect is considered, which makes it possible to stand out a weak signal from an additive mixture with noise. The strongest effect is shown to occur at certain well-defined, optimal noise intensity. The term SR was introduced during studies of the oscillator bistable model, which was proposed to analyze the glacial periods repeatability on Earth. The model described the particle motion in a symmetric one-dimensional bistable potential under the action of periodic force under strong friction conditions. In subsequent studies, the effect of stochastic resonance was found in many systems and not only physical. The known results of the approximate solution of the SR equation are considered. This equation is solved by two methods: the method of linear response and the theory of two states. In these studies, analytical expressions for the gain and signal-to-noise ratio are obtained using a number of approximations: restrictions on the signal amplitude when the response is linear, and restrictions on the frequency of the signal. In addition, when comparing the two methods considered, their use to calculate the noise variance at which the SR effect occurs, is shown to lead to different results. This necessitates further research to develop an analytical apparatus and verify its reliability by numerical calculations. The results of numerical simulation of the stochastic resonator response on the influence of an additive mixture harmonic signal and white Gaussian noise are presented. The enrichment of the output signal with harmonics and effective noise suppression are shown. The signal-to-noise ratio at the output numerical calculation results dependence on the input noise variance are presented. As it seen the dependence is complex, where you can select a local maximum at a point that does not correspond to the known values of the input noise variance at different approximate solutions of the equation SR. It is shown that the stochastic resonat

Proxies of solar activity have revealed repeated Grand Minima that occur with a certain regularity associated with the well-known Gleissberg and Süss/deVries cycles. These and other prominent cycles in the spectrum of solar activity are also seen in the spectrum of the planetary torque exerted on the solar tachocline, which has revived the hypothesis of a planetary influence on solar activity. It is not clear, however, how the extremely weak planetary forcing could influence the solar magnetic activity. Here, we suggest that stochastic resonance could explain the necessary amplification of the forcing and provide numerical evidence from stochastic time-delayed dynamo models. If the intrinsic noise of the solar dynamo allows for a frequent switching between active and quiescent stable states, tiny periodic forcings can be greatly amplified, provided the dynamo is poised close to a critical point. Such a forcing could be caused by a tidal modulation of the minimal magnetic field required for flux-tube buoyancy.

NEUROSCI BIOBEHAV REV X (X) XXX-XXX 2021.- Transcranial random noise stimulation (tRNS) is a non-invasive electrical brain stimulation method that is increasingly employed in studies of human brain function and behavior, in health and disease. tRNS is effective in modulating perception acutely and can improve learning. By contrast, its effectiveness for modulating higher cognitive processes is variable. Prolonged stimulation with tRNS, either as one longer application, or multiple shorter applications, may engage plasticity mechanisms that can result in long-term benefits. Here we provide an overview of the current understanding of the effects of tRNS on the brain and behavior and provide some specific recommendations for future research.

Historically, the earth’s fluctuation between interglacial and glacial climates has been observedto have a period of 105 years [1]. However, simulations of the global average temperature didn’tmanage to reproduce this cycle period until 1982, when Benzi et al. [2] introduced the combinationof long-term variations in incoming solar radiation and stochastic noise in an energy balancemodel. Using an energy balance model means that the change in global average temperature isset as proportional to the difference in ingoing and outgoing energy. The result of the simulationsdemonstrated so-called stochastic resonance, where small stochastic perturbations amplified thepattern of the variation in insolation, causing a pattern of large changes in the global averagetemperature, i.e. changes in the climate. The stochastic perturbations model unpredictable shorttime scale phenomena like the weather. Our study aimed to reproduce the result of Benzi et al.[2] and to investigate the model and its parameters. The presence of a 105-year climatic cycle insimulated data was found. The combination of both noise and varying incoming solar radiationwas necessary to observe the 105-year cycle. The characteristics of the climate cycle pattern did,however, vary greatly depending on the values of constants in the model, illustrating how themodel and constants were imprecise. Therefore, no conclusions can be drawn from this studyabout the earth’s current or future climate. However, the study still confirms that stochasticnoise is an important part of modeling the climate, and manages to simulate the earth’s observed105-year climate cycle.

Biological signal sensing and processing has greatly improved our understanding about the body. With the increased number of high quality biosignals that can be sensed from the body, more efficient sensing and processing systems are detrimental to meet requirements of high bandwidth data measurement and processing in power and area limited settings. Power limitation is more and more stringent with the goal of making unobtrusive wearable/implantable devices, where the battery should be long-lasting (e.g., weeks) and as small as possible at the same time. A conventional wearable/implantable biological signal sensing system includes analog-front-end to measure a biosignal, analog-to-digital converter for conversion of the measured signal, and radio to transmit the digitized signal. The most power-hungry block among them is radio, where the power consumption increases with data bandwidth. To overcome the radio power domination, physiologically relevant information can be extracted on sensing system, which would significantly reduce the transferred data bandwidth. Notably, while achieving radio power savings, the accuracy of the on-chip processing should be high.To achieve ultra-low power and high accuracy on-chip processing in resource limiting settings, the dissertation presents two ways. The first path focuses on implementation of a high accuracy digital biological signal processing algorithm in the analog signal processing (ASP) domain. Presented ASP implementation of a high accuracy algorithm achieves high electrocardiogram (ECG) feature detection with the lowest power consumption reported. In the second path, a novel biosignal processing algorithm with physical roots is introduced for intracortical neural spike and ECG feature detection. Moreover, a physical implementation of the developed algorithm with physical computation elements is designed and validated against public and custom datasets. The algorithm with physical origins achieves better signal enhancement

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.

© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works., The beneficial role of noise in the performance of Hf-based memristors has been experimentally studied. The addition of an external gaussian noise to the bias circuitry positively impacts the memristors characteristics by increasing the OFF/ON resistances ratio. The known stochastic resonance effect has been observed, when changing the standard deviation of the noise. The influence of the additive noise on the memristor current-voltage characteristic and on the set and reset related parameters are also presented., This research was funded by Spanish MCIN/AEI/10.13039/501100011033, Project PID2019- 103869RB and TEC2017-90969-EXP., Peer Reviewed, Postprint (author's final draft)

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.

© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works., The beneficial role of noise in the binary spike time dependent plasticity (STDP) learning rule, when implemented with memristors, is experimentally analyzed. The two memristor conductance states, which emulate the neuron synapse in neuromorphic architectures, can be better distinguished if a gaussian noise is added to the bias. The addition of noise allows to reach memristor conductances which are proportional to the overlap between pre- and post-synaptic pulses., This research was funded by the Spanish MCIN/AEI/10.13039/501100011033, Projects PID2019- 103869RB and TEC2017-90969-EXP. The Spanish MicroNanoFab ICTS is acknowledged for sample fabrication., Peer Reviewed, Postprint (author's final draft)

© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works., Advances in emerging resistive random-access memory (ReRAM) technology show promise for its use in future computing systems, enabling neuromorphic and memory-centric computing architectures. However, one aspect that holds back the widespread practical use of ReRAM is the behavioral variability of resistive switching devices. In this context, a radically new path towards ReRAM-based electronics concerns the exploitation of noise and the Stochastic Resonance (SR) phenomenon as a mechanism to mitigate the impact of variability. While SR has been already demonstrated in ReRAM devices and its potential impact has been analyzed for memory applications, related works have only focused on voltage input signals. In this work we present preliminary results concerning the exploration of SR in current-driven ReRAM devices, commercially available by Knowm Inc. Our results indicate that additive noise of amplitude s = 0.125uA can stabilize the cycling performance of the devices, whereas higher noise amplitude improves the HRS-LRS resistance window, thus could affect positively the Bit Error Rate (BER) metric in ReRAM memory applications., Supported by the Chilean research grants FONDECYT INICIACION 11180706 and ANID-Basal FB0008, and by the Spanish MCIN grants PID2019-105658RB-I00, and MCIN/AEI/10.13039/501100011033 grant PID2019-103869RB-C33., Peer Reviewed, Postprint (author's final draft)

The Pleistocene glacial cycles are the most prominent climate variations over the past three million years. They are the climatic response to variations in the incoming solar radiation, insolation, due to the deviations of the Earth's orbital configuration from the perfect Keplerian orbit is caused by the influence of the other planets in the solar system. This climatic response to astronomical forcing is highly non-linear, which is most pronounced expressed in the changing duration of the glacial cycles through the Middle Pleistocene Transition around a million years ago, where the duration of glacial cycles changed from 40 kyr to approximately 100 kyr without any corresponding changes in the astronomical forcing. In the late Pleistocene glaciations, the Northern Hemisphere ice sheets have grown larger than before, causing an increased cooling through the ice-albedo feedback, which makes it harder with increased insolation to cause deglaciation. The cold climate with extended glaciations has also made the climate more unstable, where strong millennial-scale oscillations related to changes in the Atlantic Meridional Overturning Circulation are observed in the paleoclimatic records.

The cardiac cycle starts when an action potential is produced by pacemaking cells in the sinoatrial node. This cycle is repeated approximately 100 000 times in humans and 1 million times in mice per day, imposing a monumental metabolic demand on the heart, requiring efficient blood supply via the coronary vasculature to maintain cardiac function. Although the ventricular coronary circulation has been extensively studied, the relationship between vascularization and cellular pacemaking modalities in the sinoatrial node is poorly understood. Here, we tested the hypothesis that the organization of the sinoatrial node microvasculature varies regionally, reflecting local myocyte firing properties. We show that vessel densities are higher in the superior versus inferior sinoatrial node. Accordingly, sinoatrial node myocytes are closer to vessels in the superior versus inferior regions. Superior and inferior sinoatrial node myocytes produce stochastic subthreshold voltage fluctuations and action potentials. However, the intrinsic action potential firing rate of sinoatrial node myocytes is higher in the superior versus inferior node. Our data support a model in which the microvascular densities vary regionally within the sinoatrial node to match the electrical and Ca2+ dynamics of nearby myocytes, effectively determining the dominant pacemaking site within the node. In this model, the high vascular density in the superior sinoatrial node places myocytes with metabolically demanding, high-frequency action potentials near vessels. The lower vascularization and electrical activity of inferior sinoatrial node myocytes could limit these cells to function to support sinoatrial node periodicity with sporadic voltage fluctuations via a stochastic resonance mechanism.

У ряді застосувань радіотехніки і радіолокації необхідно вирішувати задачу виявлення сигналів в складних завадових обставинах. Основною і найбільш складною проблемою приймання сигналів є проблема завадостійкості, тобто завдання розробки найкращих способів приймання радіосигналів за наявності завад. Це пояснюється підвищенням вимог до якості відтворення сигналів. Наведено результати аналізу виділення радіосигналів за допомогою ефекту стохастичного резонансу. В якості об'єкту дослідження розглянуто лінійно-частотно-модульоване та фазо-кодо-маніпульоване коливання, які є сигнальною базою радіолокаційних і телекомунікаційних систем.Джерела шуму можуть не просто заважати роботі нелінійних пристроїв, але і, навпаки, істотно збільшувати чутливість систем до слабких зовнішніх впливів. В цьому полягає явище стохастичного резонансу, де наявність шуму надає можливість підсилювати сигнали з амплітудою, яка є істотно меншою інтенсивності шуму. Саме тому, стохастичний резонанс є міждисциплінарним поняттям та виявляється в різних галузях науки від соціології до медицини і біології. В роботі розраховано амплітудний спектр радіосигналу і процесу на виході стохастичного резонатора, показана можливість підсилення сигналу і зменшення шуму на виході стохастичного резонатора.Визначена залежність відношення сигнал/шум на виході стохастичного резонатора для моделі сигналу у вигляді адитивної суміші радіосигналу і білого гаусівського шуму при різних значеннях дисперсії вхідного шуму. Показано, що при будь-якому значенні дисперсії вхідного шуму відношення сигнал/шум на виході стохастичного резонатора вище, ніж на виході узгодженого фільтру.Показано, що ефект стохастичного резонансу забезпечує виділення радіосигналу на фоні білого гаусівського шуму.

Noise introduced in the human nervous system from cellular to systems levels can have a major impact on signal processing. Using transcranial stimulation, electrical noise can be added to cortical circuits to modulate neuronal activity and enhance function in the healthy brain and in neurological patients. Transcranial random noise stimulation (tRNS) is a promising technique that is less well understood than other non-invasive neuromodulatory methods. The aim of the present scoping review is to collate published evidence on the effects of electrical noise at the cellular, systems, and behavioural levels, and discuss how this emerging method might be harnessed to augment perceptual and motor functioning of the human nervous system. Online databases were used to identify papers published 2008–2021 using tRNS in humans, from which we identified 70 publications focusing on sensory and motor function. Additionally, we interpret the existing evidence by referring to articles investigating the effects of noise stimulation in animal and sub-cellular models. We review physiological and behavioural findings of tRNS induced offline aftereffects and acute online benefits which manifest immediately when tRNS is applied to sensory or motor cortices. We link these results to evidence showing that activity of voltagegated sodium ion channels might be an important cellular substrate for mediating these tRNS effects. We argue that tRNS might make neural signal transmission and processing within neuronal populations more efficient, which could contribute to both (i) offline after-effects in the form of a prolonged increase in cortical excitability and (ii) acute online noise benefits when computations rely on weak inputs.

Stochastic resonance (SR) has been proposed to play a major role in auditory perception, and to maintain optimal information transmission from the cochlea to the auditory system. By this, the auditory system could adapt to changes of the auditory input at second or even sub-second timescales. In case of reduced auditory input, somatosensory projections to the dorsal cochlear nucleus would be disinhibited in order to improve hearing thresholds by means of SR. As a side effect, the increased somatosensory input corresponding to the observed tinnitus-associated neuronal hyperactivity is then perceived as tinnitus. In addition, the model can also explain transient phantom tone perceptions occurring after ear plugging, or the Zwicker tone illusion. Vice versa, the model predicts that via stimulation with acoustic noise, SR would not be needed to optimize information transmission, and hence somatosensory noise would be tuned down, resulting in a transient vanishing of tinnitus, an effect referred to as residual inhibition.

Background: Reading disability (RD) is characterized by slow and inaccurate word reading development, commonly reflecting underlying phonological problems. We have has previously shown that exposure to white noise acutely improves cognitive performance in children with ADHD. The question addressed here is whether white noise exposure yields positive outcomes also for RD. There are theoretical reasons to expect such a possibility: a) RD and ADHD are two overlapping neurodevelopmental disorders and b) since prior research on white noise benefits has suggested that the central mechanism might be the phenomenon of stochastic resonance, then adding certain kinds of white noise might strengthen the signal-to-noise ratio during phonological processing and phoneme-grapheme mapping. Methods: The study was conducted with a group of 30 children with RD and phonological decoding difficulties and two comparison groups: one consisting of skilled readers (n = 22) and another of children with mild orthographic reading problems and age adequate phonological decoding (n = 30). White noise was presented experimentally in visual and auditory modalities, while the children performed tests of single-word reading, orthographic word recognition, nonword reading, and memory recall. Results: For the first time, we show that visual and auditory white noise exposure improves some reading and memory capacities “on the fly” in children with RD and phonological decoding difficulties. By contrast, the comparison groups displayed either no benefit or a gradual decrease in performance with increasing noise. In interviews, we also found that the white noise exposure was tolerable or even preferred by many children. Conclusion: These novel findings suggest that poor readers with phonological decoding difficulties may be immediately helped by white noise during reading. Future research is needed to determine the robustness, mechanisms, and long-term practical implications of the white noise benefits in

In the phenomenon of stochastic resonance, adding a certain level of nonzero noise to a nonlinear system reduces information loss. A previous study proposed a neural network consisting of thresholding functions that exploit stochastic resonance at run time and during training, with the aim of smooth mapping and backpropagation. Such a neural network can be rephrased as one that operates only when noise is added, i.e., one that is unable to smoothly map and train when noise is absent. Focusing on both explanations simultaneously, a neural network for which only a sub-network is activated selectively by adding noise locally on that sub-network is proposed in this paper. To this end, a new activation function is introduced. It exploits stochastic resonance and presents null output and derivative when no noise is added. Simple simulations confirm that the proposed neural network with the new activation function allows the sub-network to be functionalized selectively, and interpolations are investigated by imposing varying noise intensity on various regions of the network after sub-networks are trained separately.

Resistive Random Access Memory (ReRAM) is apromising novel memory technology for non-volatile storing, with low-power operation and ultra-high area density. However, ReRAM memories still face issues through commercialization, mainly owing to the fact that the high fabrication variations and the stochastic switching of the manufactured ReRAM devices cause high Bit Error Rate (BER). Given that ReRAM devices are nonlinear elements, the nonlinear phenomenon of Stochastic Resonance (SR), which defines that an input disturbance with specific characteristics can improve the total performance of the nonlinear system, is used to reduce the ReRAM cell’s BER. Thus, in this work, the BER of a single ReRAM cell is explored, using the Stanford PKU model, and is improved after the application of specific additive input noise. The power dissipation of the proposed approach is also evaluated and compared with the consideration of higher amplitude writing pulses in the lack of noise, showing that the proposed noiseinduced technique can decrease the BER without the excessive increase of the power dissipation. As a first step, towards the experimental verification of the proposed method, noise-induced measurements on a single fabricated ReRAM device are also performed. Overall, the presented results of the BER reduction with low power dissipation, reaching up to 3:26x less power consumption considering 100 ns writing pulses, are encouraging for ReRAM designers, delivering a circuit-level solution against the device-level problem., This work was supported in part by Spanish MICIU and MINECO under Projects TEC2016-75151-C3-R, TEC2017-90969-EXP and PID2019-103869RB-C3 and in part by FPI-UPC and partially supported by Greece-Russia bilateral joint research project MEM-Q (proj.no./MIS T4∆PΩ-00030/5021467) supported by GSRT, funded by National and European funds., Peer Reviewed, Postprint (author's final draft)

Stochastic resonance (SR) has been proposed to play a major role in auditory perception, and to maintain optimal information transmission from the cochlea to the auditory system. By this, the auditory system could adapt to changes of the auditory input at second or even sub-second timescales. In case of reduced auditory input, somatosensory projections to the dorsal cochlear nucleus would be disinhibited in order to improve hearing thresholds by means of SR. As a side effect, the increased somatosensory input corresponding to the observed tinnitus-associated neuronal hyperactivity is then perceived as tinnitus. In addition, the model can also explain transient phantom tone perceptions occurring after ear plugging, or the Zwicker tone illusion. Vice versa, the model predicts that via stimulation with acoustic noise, SR would not be needed to optimize information transmission, and hence somatosensory noise would be tuned down, resulting in a transient vanishing of tinnitus, an effect referred to as residual inhibition.

Coupled oscillator arrays can be used to model several natural systems and engineering systems including mechanical systems. In this dissertation work, the influence of noise on the dynamics of coupled mono-stable oscillators arrays is investigated by using numerical and experimental methods. This work is an extension of recent efforts, including those at the University of Maryland, on the use of noise to alter a nonlinear system's response. A chain of coupled oscillators is of interest for this work. This dissertation research is guided by the following questions: i) how can noise be used to create or quench spatial energy localization in a system of coupled, nonlinear oscillators? and ii) how can noise be used to move the energy localization from one oscillator to another? The coupled oscillator systems of interest were harmonically excited and found experimentally and numerically to have a multi-stability region (MR) in the respective frequency response curves. Relative to this region, it has been found that the influence of noise depends highly on the excitation frequency location in the MR. Near either end of the MR, the oscillator responses were found to be sensitive to noise addition in the input and it was observed that the change in system dynamics through movement amongst the stable branches in the deterministic system could be anticipated from the corresponding frequency response curves. The system response is found to be robust to the influence of noise as the excitation frequency is shifted toward the middle of the MR. Also, the effects of noise on different response modes of the coupled oscillators arrays were investigated. A method for predicting the behavior is based on so-called basins of attractions of high dimensional systems. Through the findings of this work, many unique noise influenced phenomena are found, including spatial movement of an energy localization to a neighboring oscillator, response movement gradually up the energy branches, and g

Stochastic stimulation has been shown to improve movement, balance, the sense of touch, and may also improve position sense. This stimulation can be non-invasive and may be a simple technology to enhance proprioception. In this study, we investigated whether sub-threshold stochastic tactile stimulation of mechanoreceptors reduces age-related errors in wrist position estimation. Fifteen young (24.5±1.5y) and 23 elderly (71.7±7.3y) unimpaired, right-handed adults completed a wrist position gauge-matching experiment. In each trial, the participant's concealed wrist was moved to a target position between 10 and 30° of wrist flexion or extension by a robotic manipulandum. The participant then estimated the wrist's position on a virtual gauge. During half of the trials, sub-threshold stochastic tactile stimulation was applied to the wrist muscle tendon areas. Stochastic stimulation did not significantly influence wrist position sense. In the elderly group, estimation errors decreased non-significantly when stimulation was applied compared to the trials without stimulation [mean constant error reduction Δμ(θofcon)=0.8° in flexion and Δμ(θoecon)=0.7° in extension direction, p = 0.95]. This effect was less pronounced in the young group [Δμ(θycon)=0.2° in flexion and in extension direction, p = 0.99]. These improvements did not yield a relevant effect size (Cohen's d < 0.1). Estimation errors increased with target angle magnitude in both movement directions. In young participants, estimation errors were non-symmetric, with estimations in flexion [μ(θyfcon)=1.8°, σ(θyfcon)=7.0°] being significantly more accurate than in extension [μ(θyecon)=8.3°, σ(θyecon)=9.3°, p < 0.01]. This asymmetry was not present in the elderly group, where estimations in flexion [μ(θofcon)=7.5°, σ(θofcon)=9.8°] were similar to extension [μ(θoecon)=7.7°, σ(θoecon)=9.3°]. Hence, young and elderly participants performed equally in extension direction, whereas wrist position sense in flexion direction dete

We analyze the fluctuation-driven escape of particles from a metastable state under the influence of a weak periodic force. We develop an asymptotic method to solve the appropriate Fokker-Planck equation with mixed natural and absorbing boundary conditions. The approach uses two boundary layers flanking an interior region; most of the probability is concentrated within the boundary layer near the metastable point of the potential and particles transit the interior region before exiting the domain through the other boundary layer, which is near the unstable maximal point of the potential. The dominant processes in each region are given by approximate time-dependent solutions matched to construct the approximate composite solution, which gives the rate of escape with weak periodic forcing. Using reflection we extend the method to a double well potential influenced by white noise and weak periodic forcing, and thereby derive a two-state stochastic model-the simplest treatment of stochastic resonance theory-in the nonadiabatic limit., QC 20200622

Can the Dual-Sequence-Frequency-Hopping (DSFH) as a military emergency communication mode work under strong color noise? And is there any detection improvement of the DSFH signal via stochastic resonance (SR) processing under color noise? To deal with this problem, we analyze the physical feature of the DSFH signal. Firstly, the signal models of transmission, reception and the intermediate frequency (IF) are constructed. And the scale transaction is used to adjust the IF signal to fit the SR. Secondly, the non-markovian Langevin Equation (LE) is transformed into a markovian one by expand the 1-D LE to~a 2-D one. Thirdly, the non-autonomous Fokker-Plank Equation (FPE) is transformed into an autonomous one by assuming that the SR transition of magnetic particles is instantaneous and introducing the decision time. Therefore, the analytical periodic steady solution of the probability density function (PDF) with the parameter of the correlation time of the color noise is obtained. Finally, the detection probability, false alarm probability and Receiver Operating Characteristics (ROC) curve are obtained, under the criterion of the maximum~a posterior probability (MAP). Theoretical and simulation results show as below: 1) whether the DSFH can work under strong color noise is decided by the correlation time of the color noise; 2) when the power intensity of the color noise is constant, the smaller the correlation time with the bigger local SNR, the greater PDF difference of the SR output under two hypothesis, leading to better detection performance.

In the case of poor channel environment, the detection and reception of digital signal often appear errors. In view of this situation, by reducing the error rate of coherent reception of 2D PSK signals, we propose a new method based on the detection efficiency and improved cascaded stochastic resonance theory.A cascaded bistable stochastic resonance model was established by using stochastic resonance theory. The nonlinear receiver was used to receive 2DPSK signal under small signal-to-noise ratio (SNR). The experimental results show that the spectrum peak of the output signal of cascade stochastic resonance system is 5.70 times that of the traditional model. The output error rate of cascaded nonlinear system model can be reduced by 92.31% compared to the traditional model when the input signal to noise ratio is -7dB. Consequently, the output signal of the system is more likely to be detected and the accuracy can be greatly improved.

Noisy galvanic vestibular stimulation (nGVS) can boost vestibular sensory thresholds via stochastic resonance and research on nGVS as an intervention for vestibulopathy has accelerated recently. Recent research has investigated the effects and associated mechanisms of nGVS on balance and gait. nGVS has potential as an intervention for balance and gait-related deficits in vestibulopathy. but further research into the mechanisms underlying these effects and consensus on stimulation protocols are required.

BACKGROUND: A method that promotes the retrieval of lost long-term memories has not been well established. Histamine in the central nervous system is implicated in learning and memory, and treatment with antihistamines impairs learning and memory. Because histamine H-3 receptor inverse agonists upregulate histamine release, the inverse agonists may enhance learning and memory. However, whether the inverse agonists promote the retrieval of forgotten long-term memory has not yet been determined. METHODS: Here, we employed multidisciplinary methods, including mouse behavior, calcium imaging, and chemogenetic manipulation, to examine whether and how the histamine H-3 receptor inverse agonists, thioperamide and betahistine, promote the retrieval of a forgotten long-term object memory in mice. In addition, we conducted a randomized double-blind, placebo-controlled crossover trial in healthy adult participants to investigate whether betahistine treatment promotes memory retrieval in humans. RESULTS: The treatment of H-3 receptor inverse agonists induced the recall of forgotten memories even 1 week and 1 month after training in mice. The memory recovery was mediated by the disinhibition of histamine release in the perirhinal cortex, which activated the histamine H-2 receptor. Histamine depolarized perirhinal cortex neurons, enhanced their spontaneous activity, and facilitated the reactivation of behaviorally activated neuronal ensembles. A human clinical trial revealed that treatment of H-3 receptor inverse agonists is specifically more effective for items that are more difficult to remember and subjects with poorer performance. CONCLUSIONS: These results highlight a novel interaction between the central histamine signaling and memory engrains.

BACKGROUND: A method that promotes the retrieval of lost long-term memories has not been well established. Histamine in the central nervous system is implicated in learning and memory, and treatment with antihistamines impairs learning and memory. Because histamine H-3 receptor inverse agonists upregulate histamine release, the inverse agonists may enhance learning and memory. However, whether the inverse agonists promote the retrieval of forgotten long-term memory has not yet been determined. METHODS: Here, we employed multidisciplinary methods, including mouse behavior, calcium imaging, and chemogenetic manipulation, to examine whether and how the histamine H-3 receptor inverse agonists, thioperamide and betahistine, promote the retrieval of a forgotten long-term object memory in mice. In addition, we conducted a randomized double-blind, placebo-controlled crossover trial in healthy adult participants to investigate whether betahistine treatment promotes memory retrieval in humans. RESULTS: The treatment of H-3 receptor inverse agonists induced the recall of forgotten memories even 1 week and 1 month after training in mice. The memory recovery was mediated by the disinhibition of histamine release in the perirhinal cortex, which activated the histamine H-2 receptor. Histamine depolarized perirhinal cortex neurons, enhanced their spontaneous activity, and facilitated the reactivation of behaviorally activated neuronal ensembles. A human clinical trial revealed that treatment of H-3 receptor inverse agonists is specifically more effective for items that are more difficult to remember and subjects with poorer performance. CONCLUSIONS: These results highlight a novel interaction between the central histamine signaling and memory engrains.

Can the Dual-Sequence-Frequency-Hopping (DSFH) as a military emergency communication mode work under strong color noise? And is there any detection improvement of the DSFH signal via stochastic resonance (SR) processing under color noise? To deal with this problem, we analyze the physical feature of the DSFH signal. Firstly, the signal models of transmission, reception and the intermediate frequency (IF) are constructed. And the scale transaction is used to adjust the IF signal to fit the SR. Secondly, the non-markovian Langevin Equation (LE) is transformed into a markovian one by expand the 1-D LE to~a 2-D one. Thirdly, the non-autonomous Fokker-Plank Equation (FPE) is transformed into an autonomous one by assuming that the SR transition of magnetic particles is instantaneous and introducing the decision time. Therefore, the analytical periodic steady solution of the probability density function (PDF) with the parameter of the correlation time of the color noise is obtained. Finally, the detection probability, false alarm probability and Receiver Operating Characteristics (ROC) curve are obtained, under the criterion of the maximum~a posterior probability (MAP). Theoretical and simulation results show as below: 1) whether the DSFH can work under strong color noise is decided by the correlation time of the color noise; 2) when the power intensity of the color noise is constant, the smaller the correlation time with the bigger local SNR, the greater PDF difference of the SR output under two hypothesis, leading to better detection performance.

Can the Dual-Sequence-Frequency-Hopping (DSFH) as a military emergency communication mode work under strong color noise? And is there any detection improvement of the DSFH signal via stochastic resonance (SR) processing under color noise? To deal with this problem, we analyze the physical feature of the DSFH signal. Firstly, the signal models of transmission, reception and the intermediate frequency (IF) are constructed. And the scale transaction is used to adjust the IF signal to fit the SR. Secondly, the non-markovian Langevin Equation (LE) is transformed into a markovian one by expand the 1-D LE to~a 2-D one. Thirdly, the non-autonomous Fokker-Plank Equation (FPE) is transformed into an autonomous one by assuming that the SR transition of magnetic particles is instantaneous and introducing the decision time. Therefore, the analytical periodic steady solution of the probability density function (PDF) with the parameter of the correlation time of the color noise is obtained. Finally, the detection probability, false alarm probability and Receiver Operating Characteristics (ROC) curve are obtained, under the criterion of the maximum~a posterior probability (MAP). Theoretical and simulation results show as below: 1) whether the DSFH can work under strong color noise is decided by the correlation time of the color noise; 2) when the power intensity of the color noise is constant, the smaller the correlation time with the bigger local SNR, the greater PDF difference of the SR output under two hypothesis, leading to better detection performance.

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works., Stochastic Resonance (SR) is a nonlinear system specific phenomenon, which was demonstrated to lead to system unexpected (counter-intuitive) performance improvements under certain noise conditions. Memristor, on the other hand, is a fundamentally nonlinear circuit element, thus susceptible to benefit from SR, which recently came in the spotlight of the emerging technologies potential candidates. However, at this time, the variability exhibited by manufactured memristor devices within the same array constitutes the main hurdle in the road towards the commercialisation of memristor-based memories and/or computing units. Thus, in this paper, memristor SR effects are explored, assuming various memristor models, and SR-based memristance range enhancement, tolerant to device-to-device variability, is demonstrated. Our experiments reveal that SR can induce significant R MAX /R MIN ratio increase under up to 60% variability, getting as high as 3.4× for 29 dBm noise power., Peer Reviewed, Postprint (author's final draft)

We investigate the flow excited by electromagnetic forcing in a unit aspect ratio Rayleigh-Bénard cylinder. Flow structure and velocities dependent on AC frequency and coil current amplitude have been analysed. The unstable impinging jet flow bears interesting features, and a possible stochastic resonance is still under investigation.

We investigate the flow excited by electromagnetic forcing in a unit aspect ratio Rayleigh-Bénard cylinder. Flow structure and velocities dependent on AC frequency and coil current amplitude have been analysed. The unstable impinging jet flow bears interesting features, and a possible stochastic resonance is still under investigation.

Memristor devices are two-terminal nanoscale circuit elements that exhibit nonvolatile information storing and can be manufactured in ultra-dense arrays with low-power operation. Although, theoretically, memristors are strong candidates for novel memory and computing applications, the fabricated devices show high variability, both device-to-device and cycle-to-cycle, such as varying switching behaviour and maximum (R MAX ) and minimum (R MIN ) resistance values. Those limitations in the device's R MAX /R MIN ratio suppress the wide use of memristors in memory or logic applications, thus, this work presents the enhancement of this ratio on actual memristor devices, namely Knowm memristors, due to the introduction of external noise as a beneficial disturbance, following the nonlinear system phenomenon known as Stochastic Resonance., Postprint (published version)

This paper is continuation of the researches of non-linear systems in case of different input signals. Earlier, the case of a harmonic input signal was considered. Expressions for the output harmonics were received. In present paper Gaussian random process passing through the non-linear filter having the effect of a stochastic resonance is researched. Volterra series is used in calculations. A substantial number of the systems encountered in communication problems can be represented as Volterra series. It is shown that the n-fold Fourier transform plays an important role in this analysis. When the Volterra transfer functions are known, items of interest regarding the output can be obtained by substituting them in general formulas derived from the Volterra series representation. These items include expressions for the output power spectrum and various moments. Volterra transfer function by means of which expressions for the second order initial moment and power spectral density of output process are calculated. The frequency dependences of power spectral density of the output signal of the non-linear stochastic filter and also amplitude characteristics are calculated and analyzed in case of different parameter values of the filter. The obtained results showed that power spectral density of the output signal of the considered non-linear stochastic filter decreases when frequency increases and increases when power spectral density of an input signal increases. Besides, the analysis of probability density function of an output signal showed that values of the non-linear stochastic filter output signal are described by Student's t-distribution. Numerical calculations of an output signal by Runge-Kutta method were carried out for assessment of accuracy and reliability of the obtained results. The comparative analysis of dependences of an output signal power spectrum densities are obtained by the numerical calculation and on the basis of Volterra series shows their similar

AIM To investigate the acute effects of sinusoidal and stochastic resonance partial-body vibration in sitting position, including muscle activity, heart rate variability, balance and flexibility. METHODS Fifty healthy participants were assigned randomly to two training conditions: A sinusoidal partial-body vibration (SIN, 8 Hz) or a stochastic resonance partial-body vibration (STOCH, 8 ± 2 Hz). For baseline assessment participants sat on the vibration platform without vibration. Both training conditions consisted of five series of a one-minute vibration training and a one-minute break between them. In this experimental study surface electromyography (EMG) of the erector spinae (ES), one of the back muscles, and heart rate variability (HRV) was measured at baseline and during training. Balance and flexibility were assessed at baseline and immediately after training. Balance was measured with the modified star excursion balance test (mSEBT) and flexibility was assessed through the modified fingertip-to-floor method (mFTF). RESULTS Paired sample t-test showed a significant increase in balance that was restricted to STOCH (t = -2.22, P = 0.018; SIN: t = -0.09, P = 0.466). An increase in flexibility was also restricted to STOCH (t = 2.65, P = 0.007; SIN: t = 1.41, P = 0.086). There was no significant change of muscle activity in the ES-EMG in STOCH or SIN conditions. In both training conditions, HRV decreased significantly, but remained in a low-load range (STOCH: t = 2.89, P = 0.004; SIN: t = 2.55, P = 0.009). CONCLUSION In sitting position, stochastic resonance partial-body vibration can improve balance and flexibility while cardiovascular load is low. STOCH can be a valuable training option to people who are unable to stand (e.g., people, who are temporarily wheelchair-bound)., + ID der Publikation: hslu_89416 + Art des Beitrages: Wissenschaftliche Medien + Jahrgang: 9 + Sprache: Englisch + Letzte Aktualisierung: 2022-04-21 11:48:22

Stochastic resonance (SR) is a phenomenon in which the presence of noise increases the performance of the system. The phenomenon has first been discovered in a climate change model and is later observed in neuronal systems. In artificial, electronic, systems, stochastic resonance is observed in systems based on Schmitt-Triggers and comparators. The common property of all these systems is the threshold, which, when reached, causes a large transition in the system. The presence of noise in small signals can cause the system to reach the threshold or can increase the number of state transitions, increasing the quality of the output signal. Oversampling and integration are applied to reconstruct the original signal. Biomedical signals are typically affected relative high noise levels. The observations of stochastic resonance in nature, such as biological neural systems, combined with the observations of stochastic resonance in comparator-based circuits formed the inspiration and fundamental of this thesis research. The goal of this project is to investigate the potentials for using stochastic resonance in biomedical signal acquisition. In this thesis, an explorative study on the behavior, the performance, and the design of an analog-to-digital (ADC) converter fully based on stochastic resonance in a 1-bit quantizer is presented. The design and application focus on processing ECG measurements. A comprehensive analysis of the behavior, and an analytical method to determine the performance of 1-bit stochastic resonance analog-to-digital conversion in a comparator based circuit is presented. A novel technique using a negative hysteresis is found, showing a potential increase in SNDR up to 6.4 dB. Based on this analysis, a system level design is presented which implements a closed-loop operation of the stochastic resonance ADC. This design comprises a feedback loop to control the noise level, realizing the maximum performance over an input amplitude range from 1-10 mV, indep, Electrical Engineering

Population spike signaling is widely observed both in intact brain and neuronal cultures. Experimental evidence suggests that a locally applied electrical stimulus can shape the network architecture and thus the neuronal response. However, there is no clue on how this process can be controlled. Here we study a realistic model of a culture of cortical-like neurons with spike timing dependent plasticity. We show that a stimulus applied at a corner of the culture can rebuild synaptic couplings. Then the network eventually switches from a turbulent-like asynchronous spiking to an ordered population spike signaling mode. The structural analysis shows that the stimulus potentiates centrifugal couplings, which promotes spiking waves traveling outwards the stimulus location. This phenomenon can be catalyzed by noise of an intermediate strength. We predict that matured cultures with high connectivity are more susceptible to reconfiguration and generation of a population spike response than young cultures with low connectivity. We also report on an intermittent synchronization causing switches between two quasi-stable states: generation of time-locked population spikes and turbulent spiking. In the turbulent mode the stimulus excites patches of spiking activity randomly traveling in the network. Such a regime can be implemented through a large scale looping of couplings backwards to the stimulus location. We anticipate that the robust mechanisms of shaping the network architecture discussed here can also be effective in more complex preparations and studies of the relationship between network structure and function., This work was supported by the Russian Science Foundation, Depto. de Análisis Matemático y Matemática Aplicada, Fac. de Ciencias Matemáticas, TRUE, pub

[EN] Cellular decision making is based on regulatory circuits that associate signal thresholds to specific physiological actions. This transmission of information is subjected to molecular noise what can decrease its fidelity. Here, we show instead how such intrinsic noise enhances information transfer in the presence of multiple circuit copies. The result is due to the contribution of noise to the generation of autonomous responses by each copy, which are altogether associated with a common decision. Moreover, factors that correlate the responses of the redundant units (extrinsic noise or regulatory cross-talk) contribute to reduce fidelity, while those that further uncouple them (heterogeneity within the copies) can lead to stronger information gain. Overall, our study emphasizes how the interplay of signal thresholding, redundancy, and noise influences the accuracy of cellular decision making. Understanding this interplay provides a basis to explain collective cell signaling mechanisms, and to engineer robust decisions with noisy genetic circuits.

確率共鳴(Stochastic Resonance: SR)は雑音の増加により信号対雑音比(SNR)が向上する非線形現象である.確率共鳴を利用することにより,従来の受信機では検出できないような微弱信号を検出できる可能性がある.本稿では確率共鳴を通信システムに応用することを考え,確率共鳴系としてSchmitt Triggerを用いた確率共鳴受信機を実装し,その性能評価を行う.実験および数値シミュレーションにより確率共鳴受信機の微弱信号に対する応答を評価し,確率共鳴を用いて検出可能な微弱信号について検討する. Stochastic Resonance(SR) is a nonlinear phenomena in which the Signal-to-Noise Ratio(SNR) is enhanced by an increase of the noise. By using SR, we can detect the weak signal which cannot detect by the conventional receiver. In this paper, we consider the application of SR to the communication systems. we implement the Stochastic Resonance Receiver using Schmitt Trigger as the Stochastic Resonance system. We evaluate the performance of the weak signal responce and we discuss about the weak signal detection.

Syftet med studien var att undersöka hur uppmärksamhet påverkades av manipulationerna: tyst, vitt brus, en dialog och två parallella dialoger. Deltagarna delades först, med hjälp av självskattning för uppmärksamhet, in i grupper om hög och låg uppmärksamhetsnivå. Genom ett e-Corsi Spatial Span- test, Go/No Go- test och igenkänningstest i olika manipulationer, erhölls uppmärksamhetspoäng för att vidare kunna kartlägga samband. Det framkom inga statistiskt signifikanta resultat som svarade på studiens syfte. Vid en sekundär analys av separata huvudtest fann författarna statistiskt signifikanta skillnader mellan specifika manipulationer i e-Corsi Spatial Span-testet och igenkänningstestet. Dessa skillnader måste tolkas med stor försiktighet på grund av bristande validitet och reliabilitet i metoden. Studiens resultat kan med hjälp av vidare forskning undersöka individuella förutsättningar och därmed tillämpas för att uppnå en optimal arbetsmiljö., The purpose of the study was to examine how attentiveness was affected by these conditions: quietness, white noise, one dialogue and two parallel dialogues. Participants were first divided into groups of high and low level of attentiveness, by a self-assessment questionnaire. Attention scores were given by an e-Corsi Spatial Span Test, a Go/No-Go Test and a Free Recall Test, in the various conditions, to identify how different variables were related. No statistical significant results were found to answer the purpose of the study. Statistical significant results between specific conditions were found trough a secondary analysis. The differences were found in e-Corsi Spatial Span Test and Free Recall Test. Results needs to be interpreted with great caution due to deficient validity and reliability in the method. This study can through further research examine individual circumstances to aim for an ideal work environment.

Scarce research has been conducted on the moderating role of attentiveness in the effect of tDCS on cognitive performance. On the other hand, research on the impact of auditory noise on attention has generally demonstrated the detrimental effect of task-irrelevant noise on performance of cognitive tasks, yet has also neglected to explore the role of attentiveness in this relationship. Accordingly, the current study examined whether attentiveness moderates the effect of white noise (WN) and transcranial direct current stimulation (tDCS) on cognitive performance. Twenty-four participants were split into a ‘low attentiveness’ group and a ‘high attentiveness’ group based on how they scored on the SNAP-IV attention subscale. Both groups completed the Cued Go/No-Go Task under three stimulation conditions: No-noise, WN, and tDCS. Reaction time and accuracy in the task were recorded. It was hypothesized that the ‘low attentiveness’ group would perform better in the WN and tDCS conditions, whereas the ‘high attentiveness’ group would perform worse in the WN and tDCS conditions, compared to the no-noise condition. These hypotheses were derived from the MBA model that suggests that external noise adds to the internal neural noise in the brain which increases the signal-to-noise ratio through stochastic resonance, and can either enhance or attenuate cognitive performance, depending on individuals’ attentiveness level. Both hypotheses were partially supported, with the reaction times decreasing in the WN and tDCS conditions for the ‘low attentiveness’ group but increasing for the ‘high attentiveness’ group, relative to the nonoise condition. However, no difference was found in accuracy.

A class of complex self-organizing systems subjected to fluctuations of environmental or intrinsic origin and to nonequilibrium constraints in the form of an external periodic forcing is analyzed from the standpoint of information theory. Conditions under which the response of information entropy and related quantities to the nonequilibrium constraint can be optimized via a stochastic resonance-type mechanism are identified, and the role of key parameters is assessed., SCOPUS: ar.j, info:eu-repo/semantics/published

A power allocation to active and reactive power in stochastic resonance is discussed for energy harvesting from noise. It is confirmed that active power can be increased at stochastic resonance, in the same way of the relationship between energy and phase at an appropriate setting in resonance.

Die vorliegende Arbeit befasst sich mit dem Einfluss von nicht-weißem oder nicht-Gauß’schem synaptischen Rauschen auf die Informationsübertragung in stochastischen Neuronenmodellen. Ziel ist es, zu verstehen, wie eine Nervenzelle ein Signal in ihrer Pulsaktivität kodiert. Synaptisches Rauschen beschreibt hier den Einfluss anderer Nervenzellen, die nicht das interessierende Signal tragen, aber seine Übertragung durch ihre synaptische Wirkung auf die betrachtete Zelle beeinflussen. In stochastischen Neuronenmodellen wird diese Hintergrundaktivität durch einen stochastischen Prozess mit geeigneter Statistik beschrieben. Ist die Rate, mit der präsynaptische Pulse auftreten, hoch und zeitlich konstant, die Wirkung einzelner Pulse aber verschwindend gering, so wird das synaptische Rauschen durch einen Gauß’schen Prozess beschrieben. Oft wird zudem angenommen, dass das Rauschen unkorreliert (weiß) ist. In dieser Arbeit wird neuronale Signalübertragung in dem Fall untersucht, dass eine solche Näherung nicht mehr gerechtfertigt ist, d.h. wenn der synaptische Hintergrund durch einen stochastischen Prozess beschrieben werden muss, der nicht weiß, nicht Gauß’sch, oder weder weiß noch Gauß’sch ist. Mittels Simulationen und analytischer Rechnungen werden drei Szenarien behandelt: Zunächst betrachten wir eine Zelle, die nicht ein, sondern zwei Signale empfängt, welche zusätzlich durch synaptische Kurzzeitplastizität gefiltert werden. In diesem Fall muss der Hintergrund durch ein farbiges Rauschen beschrieben werden. Im zweiten Szenario betrachten wir den Fall, dass der Effekt einzelner Pulse nicht mehr als schwach angenommen werden kann. Das Rauschen ist dann nicht mehr Gauß’sch, sondern ein Schrotrauschen. Schließlich untersuchen wir den Einfluss einer präsynaptischen Population, deren Feuerrate nicht zeitlich konstant ist, sondern zwischen Phasen hoher und niedriger Aktivität, sogenannten up und down states, springt. In diesem Fall ist das Rauschen weder weiß noch Gauß’sch., This thesis is concerned with the effect of non-white or non-Gaussian synaptic noise on the information transmission properties of single neurons. Synaptic noise subsumes the massive input that a cell receives from thousands of other neurons. In the framework of stochastic neuron models, this input is described by a stochastic process with suitably chosen statistics. If the overall arrival rate of presynaptic action potentials is high and constant in time and if each individual incoming spike has only a small effect on the dynamics of the cell, the massive synaptic input can be modeled as a Gaussian process. For mathematical tractability, one often assumes that furthermore, the input is devoid of temporal structure, i.e. that it is well described by a Gaussian white noise. This is the so-called diffusion approximation (DA). The present thesis explores neuronal signal transmission when the conditions that underlie the DA are no longer met, i.e. when one must describe the synaptic background activity by a stochastic process that is not white, not Gaussian, or neither. We explore three distinct scenarios by means of simulations and analytical calculations: First, we study a cell that receives not one but two signals, additionally filtered by synaptic short-term plasticity (STP), so that the background has to be described by a colored noise. The second scenario deals with synaptic weights that cannot be considered small; here, the effective noise is no longer Gaussian and the shot-noise nature of the input has to be taken into account. Finally, we study the effect of a presynaptic population that does not fire at a rate which is constant in time but instead undergoes transitions between states of high and low activity, so-called up and down states.