Basics of hearing theory and noise in cochlear implants

Stochastic behavior in spiking of the electrically stimulated bipolar afferent cochlear neuron of man and cat is analyzed with a compartment model that includes the first unmyelinated part, every node and internode in the peripheral and central axon, and the soma. Following a suggestion of Rubinstein, ion current fluctuations across the neural membrane are assumed to be proportional to the square root of the number of sodium channels in every compartment [Rubinstein JT. Biophys J 1995;68:779–85]. Intracellular conductance and capacitance effects disturb a direct relation between voltage and current fluctuations in a single compartment. Paradoxically, the unmyelinated human soma is the compartment with the highest number of sodium channels and the smallest voltage noise. Voltage fluctuates with maximum amplitudes in the peripheral axon, followed by the thicker central axon. The model demonstrates that a train of biphasic stimulating pulses can generate a pattern in the cochlear nerve with temporal structures which are fine enough to discriminate speech signals. However, several important differences between the electrically and acoustically generated spiking patterns demand for sophisticated strategies to obtain better speech understanding for cochlear implant users.     

Impact of substance P on the correlation of spike train evoked by electro acupuncture

Substance P (SP) participates in the neural signal transmission evoked by electro-acupuncture (EA). This paper investigates the impact of SP on the correlation of spike train in the median nerve evoked by EA at 'Neiguan' acupoint (PC6). It shows that the spiking rate and interspike interval (ISI) distribution change obviously after inhibiting SP. This variation of spiking activity indicates that SP affects the temporal structure of spike train through modulating the action potential on median nerve filaments. Furtherly, the correlation coefficient and scaling exponent are considered to measure the correlation of spike train. Scaled Windowed Variance (SWV) method is applied to calculate scaling exponent which quantifies the long-range correlation of the neural electrical signals. It is found that the correlation coefficients of ISI increase after inhibiting SP released. In addition, the scaling exponents of neuronal spike train have significant differences between before and after inhibiting SP. These findings demonstrate that SP has an influence on the long-range correlation of spike train. Our results indicate that SP may play an important role in EA-induced neural spiking and encoding.     

The influence of stochastic behavior on the human threshold of hearing

The inner hair cells in the cochlea perform the crucial task of transforming mechanical sound signals into electrical activity. The cochlear nerve fibers code this information and convey it to the brain for further processing. This study investigates the performance of the system inner hair cell – primary auditory afferent nerve fibers at the physical limit of the mechano-electrical transduction for the human auditory frequency range. The Brownian motion of the hair cell’s receptive organelle, the hair bundle, does not blunt the sensitivity, but in fact enlarges – especially in frequency regions which are most important for the perception of music and speech – via the mechanism of nonlinear stochastic resonance (SR) the dynamical range of the mechano-electrical transduction by at least one order of magnitude. The coding efficiency of small sinusoidal hair bundle deflections shows basic properties of the human hearing threshold curve for pure tones and corresponds to experimental results of noise-induced tuning curves in mechano-receptors in the rat foot. Furthermore, the model explains how altered coding efficiency contributes to pathological changes in the spiking pattern which arise from morphological changes in the hair bundle structure (e.g., in noise-induced sensorineural hearing loss of cochlear origin).     

Robust to noise and outliers estimator of correlation dimension

The estimation of correlation dimension of continuous and discreet deterministic chaotic processes corrupted by an additive noise and outliers observations is investigated. In this paper we propose a new estimator of correlation dimension based on similarity between the evolution of Gaussian kernel correlation sum (Gkcs) and that of modified Boltzmann sigmoidal function (mBsf), this estimator is given by the maximum value of the first derivative of logarithmic transform of Gkcs against logarithmic transform of bandwidth, so the proposed estimator is independent of the choice of regression region like other regression estimators of correlation dimension. Simulation study indicates the robustness of proposed estimator to the presence of different types of noise such us independent Gaussian noise, non independent Gaussian noise and uniform noise for high noise level, moreover, this estimator is also robust to presence of 60% of outliers observations. Application of this new estimator with determination of their confidence interval using the moving block bootstrap method to adjusted closed price of S&P500 index daily time series revels the stochastic behavior of such financial time series.     

Effect of noise on the pattern formation in an epidemic model

We present novel numerical evidence of complicated phenomenon controlled by noise in a spatial epidemic model. The number of the spot is decreased as the noise intensity being increased, which we show by performing a series of numerical simulations. Moreover, when the noise intensity and temporal correlation are both large enough, the model dynamics exhibits a noise controlled transition from spotted pattern to stripe growth. In addition to that, we show in details the number of the spotted and stripe pattern, with the identification of a wide range of noise intensity and temporal correlation. The obtained results show that noise plays an important role in the pattern formation of the epidemic model, which may provide guidance to prevent and control the spread of disease. © 2009 Wiley Periodicals, Inc. Numer Methods Partial Differential Eq 2010     

MODEL SELECTION METHOD BASED ON MAXIMAL INFORMATION COEFFICIENT OF RESIDUALS

The traditional model selection criterions try to make a balance between fitted error and model complexity. Assumptions on the distribution of the response or the noise, which may be misspecified, should be made before using the traditional ones. In this ar- ticle, we give a new model selection criterion, based on the assumption that noise term in the model is independent with explanatory variables, of minimizing the association strength between regression residuals and the response, with fewer assumptions. Maximal Information Coe~cient （MIC）, a recently proposed dependence measure, captures a wide range of associ- ations, and gives almost the same score to different type of relationships with equal noise, so MIC is used to measure the association strength. Furthermore, partial maximal information coefficient （PMIC） is introduced to capture the association between two variables removing a third controlling random variable. In addition, the definition of general partial relationship is given.     

The effect of Gaussian white noise on the fractality of fluctuations in the plasma of a symmetrical discharge

In this work we investigate the influence of white Gaussian noise on the fluctuations in the plasma of a symmetrical discharge using multifractal detrended fluctuation analysis. We observe that in the range of noise intensity used in our study, the multifractality strength is increased by the noise, at all values of the inter-anode voltage, both for original and filtered time-series. This is interpreted as a new positive influence of noise because this effect can be understood as an increasing in the predictability on the dynamics in a time-series. A constructive influence of noise can appear only for fluctuations with underlying chaotic dynamics. The shuffling analysis demonstrates that the multifractality is purely a consequence of the correlations of the fluctuations. The noise influence is also observed in the change of the position of the maximum in the singularity spectra. The multifractal detrended cross correlation between light intensity and current intensity demonstrates that the fluctuations in both parameters are generated by the same physical processes though they are very different in nature: one is a local parameter and the other is a global one.     

On noise modeling in a nerve fibre

We present a novel mathematical approach to model noise in dynamical systems. We do so by considering the dynamics of a chain of diffusively coupled Nagumo cells affected by noise. We show that the noise in a variable representing the transmembrane current can be effectively modeled as fluctuations in the model parameters corresponding to electric resistance and capacitance of the membrane. These fluctuations may account for the interactions between the membrane and the surrounding (physiological) solution as well as for the thermal effects. The proposed approach to model noise in a nerve fibre is an alternative to the standard technique based on the consideration of additive stochastic current perturbation (the Langevin type equations) and differs from it in important mathematical aspects, particularly, it points out to the non-Markov dynamics of transmembrane potential. Our scheme relates to a time scale which is shorter than the relaxation times of involved physiological processes.     

On Carr and Lee’s Correlation Immunization Strategy

In their seminal work Robust Replication of Volatility Derivatives, Carr and Lee show how to robustly price and replicate a variety of claims written on the quadratic variation of a risky asset under the assumption that the asset’s volatility process is independent of the Brownian motion that drives the asset’s price. Additionally, they propose a correlation immunization strategy that minimizes the pricing and hedging error that results when the correlation between the risky asset’s price and volatility is non-zero. In this paper, we show that the correlation immunization strategy is the only strategy among the class of strategies discussed in Carr and Lee's paper that results in real-valued hedging portfolios when the correlation between the asset’s price and volatility is non-zero. Additionally, we perform a number of Monte Carlo experiments to test the effectiveness of Carr and Lee’s immunization strategy. Our results indicate that the correlation immunization method is an effective means of reducing pricing and hedging errors that result from a non-zero correlation.     

Asymmetric non-Gaussian effects in a tumor growth model with immunization

The dynamical evolution of a tumor growth model, under immune surveillance and subject to asymmetric non-Gaussian α-stable Lévy noise, is explored. The lifetime of a tumor staying in the range between the tumor-free state and the stable tumor state, and the likelihood of noise-induced tumor extinction, are characterized by the mean residence time and the escape probability, respectively. For various initial densities of tumor cells, the mean residence time and the escape probability are computed with different noise parameters. It is observed that unlike the Gaussian noise or symmetric non-Gaussian noise, the asymmetric non-Gaussian noise plays a constructive role in the tumor evolution in this simple model. By adjusting the noise parameters, the mean residence time can be shortened and the escape probability can be increased, simultaneously. This suggests that a tumor may be mitigated with higher probability in a shorter time, under certain external environmental stimuli.     

Zum Stromrauschen von Halbleitern

Summary The wellknown trapping of electrons and holes seems to give an explanation of the current noise in semiconductors. It is pointed out how especially the magnitude and low frequency dependence of the noise power can be understood; the possibility of a strong correlation between low frequency noise and photoconductive properties of semiconductors is emphasized.     

Combined effects of correlated bounded noises and weak periodic signal input in the modified FitzHugh–Nagumo neural model

We study the dynamics of neurons via a bistable modified stochastic FitzHugh–Nagumo model having two stable fixed points separated by one unstable fixed point. Due to the ability of a neuron to detect and enhance weak information transmission, we show numerically that starting from the resting potential, we get firing activities (spiking) when operating slightly beyond the supercritical Hopf bifurcation. For real biological systems which are sometimes embedded in the complex environment, we observe that a gradual increase or decrease noise intensities did not result in a gradual change of the membrane potential distribution thanks to noise induced transition phenomena. We shown analytically that for zero correlation between two sine Wiener noises, additive noise has no effect on the transition between monostable and bistable phase on the neural model. We adapted a general expression of the signal-to-noise ratio for a general two-state theory extended in the asymmetric case and non-Gaussian noises in our model to study the influence of noise strength in stochastic resonance. Our investigation revealed that in the evolution of excitable system, neurons may use noises to their advantage by enhancing their sensitivity near a preferred phase to detect external stimuli or affect the efficiency and rate of information processing.     

The Stokesian Dynamics Method and the Settling behaviour of Particle-Fibre-Mixtures

Sedimentation of fibres and particles can be observed manifold in industrial application. In waste water treatment, the water is clarified from solid particles and fibres utilizing sedimentation. Some filtration processes use fibres as an aid to improve the filtration quality. Finally, in the paper recycling process, one tries to separate cellulose fibres from inorganic particles used as filling material or in printing colours. For all these applications, it is necessary to understand the hydrodynamic interactions between single particles as well as particle and fibre collectives. In the present paper, the sedimentation behaviour of fibres and particles is considered in detail. Mathematic modelling is used to investigate inter particle influences in detail. In particular, a method called Stokesian Dynamics is used to simulate the settling of fibres and particles. The main challenge of the modelling is the dependence of the direction of each fibre on its sedimentation velocity and the different sizes of the particles in a poly-modal particle size distribution. Additionally fibres, particles and the fluid are influencing each other in a significant manner and in a long range. Therefore, while calculating the force on a particle, one has to take into account the influences of many particles. (© 2010 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Estimation of communication-delays through adaptive synchronization of chaos

This paper deals with adaptive synchronization of chaos in the presence of time-varying communication-delays. We consider two bidirectionally coupled systems that seek to synchronize through a signal that each system sends to the other one and is transmitted with an unknown time-varying delay. We show that an appropriate adaptive strategy can be devised that is successful in dynamically identifying the time-varying delay and in synchronizing the two systems. The performance of our strategy with respect to the choice of the initial conditions and the presence of noise in the communication channels is tested by using numerical simulations. Another advantage of our approach is that in addition to estimating the communication-delay, the adaptive strategy could be used to simultaneously identify other parameters, such as, e.g., the unknown time-varying amplitude of the received signal.     

Consistency analysis of the spectrum and prosody within a syllable for Mandarin speech

This work presents a study of Mandarin speech focusing on consistency analysis of the spectrum and prosody within syllables. Identified as a result of inspection of the human pronunciation process, this consistency can be interpreted as a high correlation between the warping curves of the spectrum and the prosody intra a syllable. The consistency analysis consisted of three steps. First, the hidden Markov model algorithm was used to decode the hidden Markov model‐state sequences within a syllable, while at the same time dividing them into three segments. Second, based on a designated syllable, the vector quantization (VQ) with the Linde–Buzo–Gray algorithm was employed to train the VQ codebooks of the prosodic vector of each segment. Third, the prosodic vector of each segment was encoded as an index using the VQ codebooks, and then, to analyze the consistency, the probability of each possible path was evaluated as a prerequisite. Finally, two syllables were used as examples to verify the consistency property found in the experiments. It is demonstrated experimentally that there is definitely consistency in the case where the syllable is located in exactly the same word. These results offer a research direction in that the warping process between the spectrum and the prosody intra a syllable must be considered in text‐to‐speech systems to improve the synthesized speech quality. Copyright © 2013 John Wiley & Sons, Ltd.     

Stochastic encoding in sensory neurons: impulse patterns of mammalian cold receptors

Intrinsic oscillations at the level of the membrane potential are a widespread feature of nerve cells. Several evidences exist that, in particular, sensory neurons combine their oscillatory membrane potentials with intrinsic, membrane and/or synaptic noise to obtain sensitive encoding properties. An interesting example are mammalian cold receptors where stimulus transduction results from modulation of intrinsic receptor oscillations with essential contribution of noise thereby generating a rich spectrum of impulse patterns. To further explore the dynamics of these receptors we here investigate an HH-type model for oscillations and spike initiation in cold receptors. By use of a biophysically plausible temperature scaling and with addition of noise, the model successfully mimicks the principle temperature-dependence of stationary impulse patterns of real cold receptors. Our results suggest that interactions between stochastic and deterministic dynamics are of functional importance for the encoding charcteristics of cold receptors.     

Dialogue strategy for horizontal communication in MAS organization

A multi agent organization model defines the structure, roles, interaction ways and coordination styles of a multi agent system. Multi agent organizations may constrain the communication between included members. Organizations’ communication is the process of sending or receiving all the messages through a group of agents in order to achieve common goals. In a dialogue, agents follow some rules that define the permissive speech acts called dialogue protocol. Aiming for common goals, the dialogue strategy is the policy of agents to choose a particular speech act among the allowed ones by the protocol. In this paper a formal model for dialogue strategy for a group of agents in an organization is proposed in order to choose the most preferable speech acts. The argumentation theory is applied to the proposed method to define the values of plausible speech acts and to rank them. The algorithm finds the best option to utter and also it decreases the volume of exchanging messages. The proposed dialogue strategy is illustrated via a deliberation dialogue example in a group of agents.     

供应链网络道德风险演化与仿真研究

针对目前供应链网络存在的越来越严重的道德风险问题,利用演化博弈和系统动力学理论,从供应链网络结构入手,对其演化过程进行了研究。首先基于供应商之间合作过程中的诚信和败德行为,建立供应链网络同级企业间道德风险的演化博弈模型,并分析了其演化路径。其次通过对制造商和供应商之间博弈的动态性分析,建立供应链网络上下游企业间道德风险演化的SD模型,并对其演化过程进行了仿真分析。研究结果表明:供应链网络道德风险的演化结果取决于网络内败德企业获得的超额收益、收益调整程度、惩罚力度和惩罚机制。另外,罚款力度大小与供应链网络道德风险演化的动态性密切相关;动态惩罚机制在其演化的波动性控制方面,具有明显效果。     

Recovering unknown model parameters contained in a class of time-variant chaotic dynamical systems

This work deals mainly with the problem of recovering all unknown parameters for a class of time-variant chaotic dynamical systems from given time sequence. Based on synchronization between a chaotic sender system and an additional receiver system, a procedure, which combines a linear feedback technique with updated feedback gain and an adapted control strategy associated with the law of estimated parameters, is developed to dynamically determine the values of unknown parameters contained in the sender system. To promote widespread applications, the structure of the receiver system can be independent of that of the sender system. The effectiveness of this procedure is guaranteed by the periodic version of the classical LaSalle invariance principle of differential equations. Illustrations are presented for a harmonically excited Duffing oscillator and a four dimensional chaotic oscillator. The numerical results reveal the present procedure not only can precisely recover unknown model parameters, but also can rapidly response to sudden changes in unknown parameters. In addition, it has great robustness against the disturbance of noise.     

Analysis of Alternating-Priority Queueing Models with (Cross) Correlated Switchover Times

This paper analyzes a single server queueing system in which service is alternated between two queues and the server requires a (finite) switchover time to switch from one queue to the other. The distinction from classical results is that the sequence of switchover times from each of the queues need not be i.i.d. nor independent from each other; each sequence is merely required to form a stationary ergodic sequence. With the help of stochastic recursive equations explicit expressions are derived for a number of performance measures, most notably for the average delay of a customer and the average queue lengths under different service disciplines. With these expressions a comparison is made between the service disciplines and the influence of correlation is studied. Finally, through a number of examples it is shown that the correlation can significantly increase the mean delay and the average queue lengths indicating that the correlation between switchover times should not be ignored. This has important implications for communication systems in which a common communication channel is shared amongst various users and where the time between consecutive data transfers is correlated (for example in ad-hoc networks). In addition to this a number of notational mistakes in well-known existing literature are pointed out. AMS subject classification: 68M20, 60J85 A shorter version of this work has been published in the proceedings of IEEE Infocom 2005. This work was partly sponsored by the EURONGI network of excellence.