一类非线性神经网络系统的超阈值随机共振现象

超阈值随机共振可用来解释一些生物现象.本文对一类非线性多阈值神经网络模型的超阈值 随机共振现象进行了探讨. 首先推导出了系统输出互信息的表达式, 然后分析了系统参数及噪声对互信息量的影响.通过数值计算发现,在阈值系统的信息传递过程中, 根据乘性噪声和加性噪声对系统信息传递影响的不同, 对系统的阈值进行恰当选取是至关重要的. 此外, 还发现系统的阈值单元数目越多, 超阈值随机共振现象就越容易出现.     

Statistically robust evidence of stochastic resonance in human auditory perceptual system

In human perception, exogenous noise is known to yield a masking effect, i.e. an increase of the perceptual threshold relative to a stimulus acting on the same modality. However, somehow counter-intuitively, the opposite mechanism can occasionally occur: a decrease of the perceptual threshold for a non-vanishing, virtuous amount of noise. This mechanism, called stochastic resonance, is deemed to provide important information about the role of noise in the human brain. In this paper, we investigate stochastic resonance in a detection task in the auditory modality. Normal-hearing participants were asked to judge the presence of acoustic stimuli of different intensity and superimposed to different levels of white noise. The matrix-like outcomes of a behavioural experiment were fitted by a two-dimensional, noise-dependent psychometric function. The fit revealed a statistically significant stochastic resonance in 43% of the experimental runs. We conclude that, in the auditory modality, stochastic resonance is a tiny effect that, under conventional circumstances, is largely overrun by standard masking.     

基于随机共振进行弱信号探测的实验研究

非线性随机共振系统可利用噪声增强微弱信号检测的能力,为强噪声背景下微弱信号的检测开创了新方法.基于随机共振的基本原理设计了硬件电路系统,并将其应用于检测单频和多频微弱信号;通过输入模拟工程实际的带噪信号,采样所得的输出信号的频谱分析结果表明,利用随机共振技术可从强噪声背景下有效地提取出单频和多频弱信号.多频弱信号的有效提取拓展了基于随机共振原理的弱信号检测技术的应用领域,结合数字滤波处理技术有效地消除了低频噪声对信号识别的影响.基于随机共振的弱信号检测技术在信息识别与信息处理方面具有巨大的潜在的应用价值.     

Optimal information transmission in nonlinear arrays through suprathreshold stochastic resonance

We examine the optimal threshold distribution in populations of noisy threshold devices. When the noise on each threshold is independent, and sufficiently large, the optimal thresholds are realized by the suprathreshold stochastic resonance effect, in which case all threshold devices are identical. This result has relevance for neural population coding, as such noisy threshold devices model the key dynamics of nerve fibres. It is also relevant to quantization and lossy source coding theory, since the model provides a form of stochastic signal quantization. Furthermore, it is shown that a bifurcation pattern appears in the optimal threshold distribution as the noise intensity increases. Fisher information is used to demonstrate that the optimal threshold distribution remains in the suprathreshold stochastic resonance configuration as the population size approaches infinity.     

声学随机共振实验

在特定的非线性系统中,噪声的存在能够增强信号的响应, 这种现象为随机共振. 本文实验通过观察噪声对听觉阈限的影响,使学生了解声学中的随机共振现象.     

Stochastic Resonance in a Simple Threshold Sensor System with Alpha Stable Noise

We investigate the effect of alpha stable noise on stochastic resonance in a single-threshold sensor system by analytic deduction and stochastic simulation. It is shown that stochastic resonance occurs in the threshold system in alpha stable noise environment, but the resonant effect becomes weakened as the alpha stable index decreases or the skewness parameter of alpha stable distribution increases. In particular, for Cauchy noise a nonlinear relation among the optimal noise deviation parameter, the signal amplitude and the threshold is analytically obtained and illustrated by using the extreme value condition for the output signal-to-noise ratio. The results presented in this communication should have application in signal detection and image restoration in the non-Gaussian noisy environment.     

Noise-robust realization of Turing-complete cellular automata by using neural networks with pattern representation

A modularly-structured neural network model is considered. Each module, which we call a ‘cell’, consists of two parts: a Hopfield neural network model and a multilayered perceptron. An array of such cells is used to simulate the Rule 110 cellular automaton with high accuracy even when all the units of neural networks are replaced by stochastic binary ones. We also find that noise not only degrades but also facilitates computation if the outputs of multilayered perceptrons are below the threshold required to update the states of the cells, which is a stochastic resonance in computation.     

Stochastic resonance in Hopfield neural networks for transmitting binary signals

We investigate the stochastic resonance phenomenon in a discrete Hopfield neural network for transmitting binary amplitude modulated signals, wherein the binary information is represented by two stored patterns. Based on the potential energy function and the input binary signal amplitude, the observed stochastic resonance phenomena involve two general noise-improvement mechanisms. A suitable amount of added noise assists or accelerates the switch of the network state vectors to follow input binary signals more correctly, yielding a lower probability of error. Moreover, at a given added noise level, the probability of error can be further reduced by the increase of the number of neurons. When the binary signals are corrupted by external heavy-tailed noise, it is found that the Hopfield neural network with a large number of neurons can outperform the matched filter in the region of low input signal-to-noise ratios per bit.     

基于多域特征提取和深度学习的声源被动测距*

由于实际海洋环境中存在大量的非高斯噪声,一些基于高斯假设的传统去噪方法在实际海洋环境中性能下降甚至失效。针对非高斯噪声,如α稳定分布噪声、非平稳行船噪声下的脉冲信号的去噪与重构,该文提出一种基于深度学习的方法。去噪模型首先通过学习带噪信号短时傅里叶变换谱与残差谱之间的映射关系以去除环境噪声,之后对去噪信号的时频谱进行逆变换重构脉冲信号。仿真实验结果表明,深度学习模型在非高斯噪声环境下脉冲信号的去噪与重构任务中有着良好的表现,在实测样本上也表现出良好的泛化性,体现了一定的工程应用价值。     

Coherence and stochastic resonance in threshold crossing detectors with delayed feedback

We consider the dynamical behavior of threshold systems driven by external periodic and stochastic signals and internal delayed feedback. Specifically, the effect of positive delayed feedback on the sensitivity of a threshold crossing detector (TCD) to periodic forcing embedded in noise is investigated. The system has an intrinsic ability to oscillate in the presence of positive feedback. We first show conditions under which such reverberatory behavior is enhanced by noise, which is a form of coherence resonance (CR) for this system. Further, for input signals that are subthreshold in the absence of feedback, the open-loop stochastic resonance (SR) characteristic can be sharply enhanced by positive delayed feedback. This enhancement is shown to depend on the stimulus period, and is maximal when this period is matched to an integer multiple of the delay. Reverberatory oscillations, which are particularly prominent after the offset of periodic forcing, are shown to be eliminated by a summing network of such TCDs with local delayed feedback. Theoretical analysis of the crossing rate dynamics qualitatively accounts for the existence of CR and the resonant behavior of the SR effect as a function of delay and forcing frequency.     

Non-Gaussian,non-dynamical stochastic resonance

The classical model revealing stochastic resonance is a motion of an overdamped particle in a double-well fourth order potential when combined action of noise and external periodic driving results in amplifying of weak signals. Resonance behavior can also be observed in non-dynamical systems. The simplest example is a threshold triggered device. It consists of a periodic modulated input and noise. Every time an output crosses the threshold the signal is recorded. Such a digitally filtered signal is sensitive to the noise intensity. There exists the optimal value of the noise intensity resulting in the “most” periodic output. Here, we explore properties of the non-dynamical stochastic resonance in non-equilibrium situations, i.e. when the Gaussian noise is replaced by an α-stable noise. We demonstrate that non-equilibrium α-stable noises, depending on noise parameters, can either weaken or enhance the non-dynamical stochastic resonance.     

耦合小世界神经网络的随机共振

噪声广泛存在于生物神经系统中,对系统功能具有重要作用.采用神经元二维映射模型构建一个复杂神经网络,由多个小世界子网络构成,研究了Gaussian白噪声诱导的随机共振现象.研究发现,只有合适的噪声强度才能使神经网络对输入刺激信号的频率响应达到峰值.另外,网络结构对系统随机共振特性有重要影响.在固定的耦合强度下,存在一个最优的局部小世界子网络结构,使得整个系统的频率响应最佳.     

非对称双稳耦合网络系统的尺度随机共振研究

研究了不同周期信号调制下非对称双稳耦合网络系统的尺度随机共振问题. 针对该网络系统, 首先运用高斯近似和役使原理对其进行了降维, 推导了其简化模型. 在绝热近似条件下, 利用Fokker-Planck方程分别得到了余弦信号和矩形信号调制下信噪比的解析表达式. 在此基础上, 研究了系统的尺度随机共振行为, 并讨论了非对称性、噪声强度、周期信号的振幅和耦合系数对系统尺度随机共振的影响. 结果表明, 两种情形下信噪比均是系统尺度的非单调函数, 说明在此网络系统中产生了共振现象. 关键词： 尺度随机共振 非对称双稳耦合网络系统 余弦信号 矩形信号     

Stochastic resonance in a monostable system driven by square-wave signal and dichotomous noise

<正>This paper investigates the stochastic resonance in a monostable system driven by square-wave signal,asymmetric dichotomous noise as well as by multiplicative and additive white noise.By the use of the properties of the dichotomous noise,it obtains the expressions of the signal-to-noise ratio under the adiabatic approximation condition.It finds that the signal-to-noise ratio is a non-monotonic function of the asymmetry of the dichotomous noise,and which varies non-monotonously with the intensity of the multiplicative and additive noise as well as the system parameters.Moreover, the signal-to-noise ratio depends on the correlation rate and intensity of the dichotomous noise.     

单模激光系统随机共振的模拟研究

用数值模拟方法对具有调制信号的单模激光系统进行了研究, 分析了在阈值附近和远离阈值两种工作方式下的输出信噪比、输出信号功率、 背景噪声功率,并与线性化近似结果进行了对比. 研究发现,激光在阈值附近工作时,输出信噪比会随着量子 噪声的增大出现随机共振现象,这一现象不要求泵噪声与量子噪 声存在关联,但噪声之间的关联会进一步增大信噪比;在远离阈值时, 只有噪声间存在关联时才会发生随机共振现象.研究还发现噪声也 会影响输出信号的功率.     

Control coherence resonance by noise recycling

We study the effect of recycled noise, generated by the superposition of a primary Gaussian noise source with a second component of constant delay, in a parameter region below the threshold of supercritical Hopf bifurcation, by focussing on the performance of noise induced oscillations and coherence resonance. For fixed noise intensity, the amplitude and signal-to-noise ratio of the oscillation show periodic dependences on the delay time. The optimal noise intensity for the occurrence of coherence resonance also shows a periodic dependence on the delay. A theoretical analysis based on the stochastic normal form theory is presented, which qualitatively reproduces the simulation results with good agreement. This work presents a possible strategy for controlling noise induced oscillations and coherence resonance by deliberately adjusting the parameters of the recycled noise.     

Noise induced amplification of sub-threshold pulses in multi-thread excitable semiconductor ‘neurons’

We report on the transmission of electrical pulses through a semiconductor structure which emulates biological neurons. The ‘neuron’ emits bursts of electrical spikes whose coherence we study as a function of the amplitude and frequency of a sine wave stimulus and noise. Noise is found to enhance the transmission of pulses below the firing threshold of the neuron. We demonstrate stochastic resonance when the power of the output signal passes through a maximum at an optimum noise value. Under appropriate conditions, we observe coherence resonance and stochastic synchronization. Data are quantitatively explained by modelling the FitzHugh–Nagumo equations derived from the electrical equivalent circuit of the soma. We have therefore demonstrated a physically realistic neuron structure that provides first principles feedback on mathematical models and that is well suited to building arborescent networks of pulsing neurons.     

小世界神经元网络随机共振现象:混合突触和部分时滞的影响

实际神经元网络中,信息传递时电突触和化学突触同时存在,并且有些神经元间的时滞很小可以忽略.本文构建了带有不同类型突触耦合的小世界网络,研究部分时滞、混合突触及噪声对随机共振的影响.结果表明:兴奋性和抑制性突触的比例影响共振的产生;在抑制性突触为主的网络里,几乎不产生随机共振.系统最佳噪声强度和化学突触比例大致呈线性递增关系;特别是在以化学耦合为主的混合突触网络里,仅当兴奋性突触与抑制性突触比例约为4:1时,噪声才可诱导网络产生共振行为.在此比例下,引入部分时滞发现时滞可诱导网络产生随机多共振,且随网络中时滞边比例的增加,系统响应强度达到最优水平的时滞取值区间逐渐变窄;同时发现,网络中含有的化学突触越多,部分时滞诱导产生的多共振行为越强.此外,当时滞为系统固有周期的整数倍时,时滞越大共振所对应的噪声区域越广;并且网络中时滞边越多,越容易促使噪声和时滞诱导其产生明显的共振行为.     

Stochastic resonance in a model of opinion formation on small-world networks

We analyze the phenomenon of stochastic resonance in an Ising-like system on a small-world network. The system, which is subject to the combined action of noise and an external modulation, can be interpreted as a stylized model of opinion formation by imitation under the effects of a “fashion wave”. Both the amplitude threshold for the detection of the external modulation and the width of the stochastic-resonance peak show considerable variation as the randomness of the underlying small-world network is changed. Received 19 December 2001     

A critical experimental study of the classical tactile threshold theory

Background  

The tactile sense is being used in a variety of applications involving tactile human-machine interfaces. In a significant number of publications the classical threshold concept plays a central role in modelling and explaining psychophysical experimental results such as in stochastic resonance (SR) phenomena. In SR, noise enhances detection of sub-threshold stimuli and the phenomenon is explained stating that the required amplitude to exceed the sensory threshold barrier can be reached by adding noise to a sub-threshold stimulus. We designed an experiment to test the validity of the classical vibrotactile threshold. Using a second choice experiment, we show that individuals can order sensorial events below the level known as the classical threshold. If the observer's sensorial system is not activated by stimuli below the threshold, then a second choice could not be above the chance level. Nevertheless, our experimental results are above that chance level contradicting the definition of the classical tactile threshold.