Aperiodic stochastic resonance in neural information processing with Gaussian colored noise

Kang, Yanmei; Ruonan, Liu; Mao, Xuerong

doi:10.1007/s11571-020-09632-3

Cited by 31 publications

(13 citation statements)

References 43 publications

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“…The main difference between colored Gaussian noise and white Gaussian noise is expressed by the slope of the power spectral density, which indicates that colored Gaussian noise should be obtained to adjust the slope of Equation (6). Although the Ornstein–Uhlenbeck process is more commonly used to numerically generate Gaussian colored noise, [ 30 ] we modulated the power spectral density slope of white Gaussian noise with reference to a previous study to achieve a similar effect to real‐world applications. [ 31 ]…”

Section: Methodsmentioning

confidence: 99%

Quantum Analog Annealing of Gain‐Dissipative Ising Machine Driven by Colored Gaussian Noise

Liao

Sarker

et al. 2022

Advcd Theory and Sims

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Gain‐dissipative Ising machines (GIMs) are a type of quantum analog equipment that can rapidly determine the optimal solution for combinatorial optimization problems. When the noise intensity is significantly lower than the fixed point of the system, the performance of a GIM is not influenced by the fluctuation of the noise intensity. However, the noise in this study is limited to Gaussian white noise. The influence of prevalent colored noise on GIMs has not been researched. In this study, the influence of common‐colored noise on the performance of GIMs is numerically investigated. The results of a domain clustering dynamics analysis reveal that red noise can better suppress the generation of the noise‐induced irregular temporary domain. Furthermore, several prevalent MAXCUT problem topologies, including the Moebius ladder, random Moebius ladder, and 2D random lattice, are adopted as test benchmarks. The results reveal that GIMs influenced by white, blue, and violet noise perform better at low‐intensity noise condition. In contrast, pink and red noise‐injected GIMs demonstrate higher performances when applied to MAXCUT topologies with both ferromagnetic and antiferromagnetic connections under larger noise intensity conditions. This indicates that the noise dispersion can be used as an additional hyperparameter to optimize the performance of GIMs.

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Section: Methodsmentioning

confidence: 99%

Quantum Analog Annealing of Gain‐Dissipative Ising Machine Driven by Colored Gaussian Noise

Liao

Sarker

et al. 2022

Advcd Theory and Sims

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“…Extension to Levy jump processes should be direct. See Kang et al (2020) and Wallace (2016) for roughly analogous discussions.…”

Section: Applicationsmentioning

confidence: 98%

“…Original studies were confined to periodic systems like figure 2. More recent work has extended the concept to generalized 'aperiodic' circumstance (e.g., Kang et al 2020). As Kang et al put it, for aperiodic stochastic resonance, instead of emphasizing frequency matching, the focus should be on what they call 'shape matching'.…”

Section: Applicationsmentioning

confidence: 99%

Machine Cognition, Control and Embodiment on Landscapes of Fog, Friction and Selection

Wallace¹

2023

J. AI. Consci.

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Real-world cognitive structures — embodied biological, machine or composite entities — are inherently unstable by virtue of the “topological information” imposed upon them by external circumstance, adversarial intent, and other persistent “selection pressures”. Consequently, under the Data Rate Theorem (DRT), they must be constantly controlled by embedding regulators. For example, blood pressure and the stream of consciousness require persistent delicate regulation in higher organisms. Here, using the Rate Distortion Theorem of information theory, we derive a form of the DRT of control theory that characterizes such instability for adiabatically stationary nonergodic systems and uncover novel forms of cognitive dynamics under stochastic challenge. These range from aperiodic stochastic amplification to Yerkes–Dodson signal transduction and outright system collapse. The analysis, deliberately closely adapted from recent purely biological studies, leads toward new statistical tools for data analysis, uncovering groupoid symmetry-breaking phase transition analogs to Fisher Zeros in physical systems that may be important for studies of machine intelligence under real-world, hence embodied, interaction. The challenges facing construction, operation, and stabilization of high-order “workspace” or “multiple-workspace” machine cognition, perhaps backed by rapid pattern-matching “emotional” AI, whether explicitly recognized as conscious or not, will require parallel construction of new analytic machinery. This work provides one example, solidly based on the asymptotic limit theorems of information and control theories.

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“…There are many reasons for the difficulty of extracting weak features from weld X-ray flaw detection images, and the inevitable noise pollution is one of the reasons. The study of nonlinear stochastic resonance system shows that an appropriate amount of noise can amplify weak signals [24,25]. In recent years, the application research of stochastic resonance in fault diagnosis [26], image processing [27,28] and other fields has expanded the engineering value of nonlinear stochastic dynamics.…”

Section: Introductionmentioning

confidence: 99%

Automatic Extraction Method of Weld Weak Defect Features for Ultra-High Voltage Equipment

Zheng¹,

Luo²,

Shen³

et al. 2023

Energy Engineering

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To solve the problems of low precision of weak feature extraction, heavy reliance on labor and low efficiency of weak feature extraction in X-ray weld detection image of ultra-high voltage (UHV) equipment key parts, an automatic feature extraction algorithm is proposed. Firstly, the original weld image is denoised while retaining the characteristic information of weak defects by the proposed monostable stochastic resonance method. Then, binarization is achieved by combining Laplacian edge detection and Otsu threshold segmentation. Finally, the automatic identification of weld defect area is realized based on the sequential traversal of binary tree. Several characteristic analysis dimensions are established for weld defects of UHV key parts, including defect area, perimeter, slenderness ratio, duty cycle, etc. The experiment using the weld detection image of the actual production site shows that the proposed method can effectively extract the weak feature information of weld defects and further provide reference for decision-making.

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Aperiodic stochastic resonance in neural information processing with Gaussian colored noise

Cited by 31 publications

References 43 publications

Quantum Analog Annealing of Gain‐Dissipative Ising Machine Driven by Colored Gaussian Noise

Quantum Analog Annealing of Gain‐Dissipative Ising Machine Driven by Colored Gaussian Noise

Machine Cognition, Control and Embodiment on Landscapes of Fog, Friction and Selection

Automatic Extraction Method of Weld Weak Defect Features for Ultra-High Voltage Equipment

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