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Journal articles on the topic 'Neurodynamic optimization'

Author: Grafiati
Published: 25 May 2024
Last updated: 19 July 2025

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1

Ji, Zheng, Xu Cai, and Xuyang Lou. "A Quantum-Behaved Neurodynamic Approach for Nonconvex Optimization with Constraints." Algorithms 12, no. 7 (2019): 138. http://dx.doi.org/10.3390/a12070138.

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This paper presents a quantum-behaved neurodynamic swarm optimization approach to solve the nonconvex optimization problems with inequality constraints. Firstly, the general constrained optimization problem is addressed and a high-performance feedback neural network for solving convex nonlinear programming problems is introduced. The convergence of the proposed neural network is also proved. Then, combined with the quantum-behaved particle swarm method, a quantum-behaved neurodynamic swarm optimization (QNSO) approach is presented. Finally, the performance of the proposed QNSO algorithm is evaluated through two function tests and three applications including the hollow transmission shaft, heat exchangers and crank–rocker mechanism. Numerical simulations are also provided to verify the advantages of our method.
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2

Le, Xinyi, Sijie Chen, Fei Li, Zheng Yan, and Juntong Xi. "Distributed Neurodynamic Optimization for Energy Internet Management." IEEE Transactions on Systems, Man, and Cybernetics: Systems 49, no. 8 (2019): 1624–33. http://dx.doi.org/10.1109/tsmc.2019.2898551.

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3

Ahmadi-Asl, Salman, Valentin Leplat, Anh Huy Phan, and Andrzej Cichocki. "Nonnegative Tensor Decomposition via Collaborative Neurodynamic Optimization." SIAM Journal on Scientific Computing 47, no. 1 (2025): C100—C125. https://doi.org/10.1137/23m1627304.

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4

Li, Guocheng, and Zheng Yan. "Reconstruction of sparse signals via neurodynamic optimization." International Journal of Machine Learning and Cybernetics 10, no. 1 (2017): 15–26. http://dx.doi.org/10.1007/s13042-017-0694-4.

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5

Leung, Man-Fai, and Jun Wang. "A Collaborative Neurodynamic Approach to Multiobjective Optimization." IEEE Transactions on Neural Networks and Learning Systems 29, no. 11 (2018): 5738–48. http://dx.doi.org/10.1109/tnnls.2018.2806481.

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6

Ma, Litao, Jiqiang Chen, Sitian Qin, Lina Zhang, and Feng Zhang. "An Efficient Neurodynamic Approach to Fuzzy Chance-constrained Programming." International Journal on Artificial Intelligence Tools 30, no. 01 (2021): 2140001. http://dx.doi.org/10.1142/s0218213021400017.

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In both practical applications and theoretical analysis, there are many fuzzy chance-constrained optimization problems. Currently, there is short of real-time algorithms for solving such problems. Therefore, in this paper, a continuous-time neurodynamic approach is proposed for solving a class of fuzzy chance-constrained optimization problems. Firstly, an equivalent deterministic problem with inequality constraint is discussed, and then a continuous-time neurodynamic approach is proposed. Secondly, a sufficient and necessary optimality condition of the considered optimization problem is obtained. Thirdly, the boundedness, global existence and Lyapunov stability of the state solution to the proposed approach are proved. Moreover, the convergence to the optimal solution of considered problem is studied. Finally, several experiments are provided to show the performance of proposed approach.
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7

Yan, Zheng, Jun Wang, and Guocheng Li. "A collective neurodynamic optimization approach to bound-constrained nonconvex optimization." Neural Networks 55 (July 2014): 20–29. http://dx.doi.org/10.1016/j.neunet.2014.03.006.

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8

Wang, Tong, Hao Cui, Zhongyi Zhang, and Jian Wei. "A Neurodynamic Approach for SWIPT Power Splitting Optimization." Journal of Physics: Conference Series 2517, no. 1 (2023): 012010. http://dx.doi.org/10.1088/1742-6596/2517/1/012010.

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Abstract Simultaneous wireless information and power transfer (SWIPT) systems using energy from RF signals can effectively solve the energy shortage of wireless devices. However, the existing SWIPT optimization methods using numerical algorithms are difficult to solve the non-convex problem and to adapt to the dynamic communication circumstances. In this paper, a duplex neurodynamic optimization method is used to address the SWIPT system’s power partitioning issue. The information rate maximization problem of the SWIPT system is framed as a biconvex problem. A duplex recurrent neural network is used to concurrently execute local search and update the initial state of the neural network by a particle swarm optimization method to get the global optimum. The experimental results demonstrate that the duplex neurodynamic-based SWIPT system maximizes information rate while satisfying the minimal harvesting energy requirement in a variety of channel states.
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9

Liu, Bao, Xuehui Mei, Haijun Jiang, and Lijun Wu. "A Nonpenalty Neurodynamic Model for Complex-Variable Optimization." Discrete Dynamics in Nature and Society 2021 (February 16, 2021): 1–10. http://dx.doi.org/10.1155/2021/6632257.

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In this paper, a complex-variable neural network model is obtained for solving complex-variable optimization problems described by differential inclusion. Based on the nonpenalty idea, the constructed algorithm does not need to design penalty parameters, that is, it is easier to be designed in practical applications. And some theorems for the convergence of the proposed model are given under suitable conditions. Finally, two numerical examples are shown to illustrate the correctness and effectiveness of the proposed optimization model.
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10

Zhao, You, Xiaofeng Liao, and Xing He. "Novel projection neurodynamic approaches for constrained convex optimization." Neural Networks 150 (June 2022): 336–49. http://dx.doi.org/10.1016/j.neunet.2022.03.011.

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11

Yan, Zheng, Jianchao Fan, and Jun Wang. "A Collective Neurodynamic Approach to Constrained Global Optimization." IEEE Transactions on Neural Networks and Learning Systems 28, no. 5 (2017): 1206–15. http://dx.doi.org/10.1109/tnnls.2016.2524619.

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12

Liu, Qingshan, Shaofu Yang, and Jun Wang. "A Collective Neurodynamic Approach to Distributed Constrained Optimization." IEEE Transactions on Neural Networks and Learning Systems 28, no. 8 (2017): 1747–58. http://dx.doi.org/10.1109/tnnls.2016.2549566.

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13

Qin, Sitian, Xinyi Le, and Jun Wang. "A Neurodynamic Optimization Approach to Bilevel Quadratic Programming." IEEE Transactions on Neural Networks and Learning Systems 28, no. 11 (2017): 2580–91. http://dx.doi.org/10.1109/tnnls.2016.2595489.

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14

Yu, Xin, Qingzhou Huang, and Rixin Lin. "A reformulation neurodynamic algorithm for distributed nonconvex optimization." Neurocomputing 635 (June 2025): 130023. https://doi.org/10.1016/j.neucom.2025.130023.

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15

Wu, Dawen, and Abdel Lisser. "Solving Constrained Pseudoconvex Optimization Problems with deep learning-based neurodynamic optimization." Mathematics and Computers in Simulation 219 (May 2024): 424–34. http://dx.doi.org/10.1016/j.matcom.2023.12.032.

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16

Leung, Man-Fai, and Jun Wang. "Cardinality-constrained portfolio selection based on collaborative neurodynamic optimization." Neural Networks 145 (January 2022): 68–79. http://dx.doi.org/10.1016/j.neunet.2021.10.007.

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17

Xu, Chen, Yiyuan Chai, Sitian Qin, Zhenkun Wang, and Jiqiang Feng. "A neurodynamic approach to nonsmooth constrained pseudoconvex optimization problem." Neural Networks 124 (April 2020): 180–92. http://dx.doi.org/10.1016/j.neunet.2019.12.015.

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18

Liu, Shuxin, Haijun Jiang, Liwei Zhang, and Xuehui Mei. "A neurodynamic optimization approach for complex-variables programming problem." Neural Networks 129 (September 2020): 280–87. http://dx.doi.org/10.1016/j.neunet.2020.06.012.

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19

Che, Hangjun, and Jun Wang. "A collaborative neurodynamic approach to global and combinatorial optimization." Neural Networks 114 (June 2019): 15–27. http://dx.doi.org/10.1016/j.neunet.2019.02.002.

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20

Yan, Zheng, and Jun Wang. "Nonlinear Model Predictive Control Based on Collective Neurodynamic Optimization." IEEE Transactions on Neural Networks and Learning Systems 26, no. 4 (2015): 840–50. http://dx.doi.org/10.1109/tnnls.2014.2387862.

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21

Fan, Jianchao, and Jun Wang. "A Collective Neurodynamic Optimization Approach to Nonnegative Matrix Factorization." IEEE Transactions on Neural Networks and Learning Systems 28, no. 10 (2017): 2344–56. http://dx.doi.org/10.1109/tnnls.2016.2582381.

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22

Yang, Shaofu, Qingshan Liu, and Jun Wang. "A Collaborative Neurodynamic Approach to Multiple-Objective Distributed Optimization." IEEE Transactions on Neural Networks and Learning Systems 29, no. 4 (2018): 981–92. http://dx.doi.org/10.1109/tnnls.2017.2652478.

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23

Che, Hangjun, and Jun Wang. "A Two-Timescale Duplex Neurodynamic Approach to Biconvex Optimization." IEEE Transactions on Neural Networks and Learning Systems 30, no. 8 (2019): 2503–14. http://dx.doi.org/10.1109/tnnls.2018.2884788.

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24

Dai, Chengchen, Hangjun Che, and Man-Fai Leung. "A Neurodynamic Optimization Approach for L1 Minimization with Application to Compressed Image Reconstruction." International Journal on Artificial Intelligence Tools 30, no. 01 (2021): 2140007. http://dx.doi.org/10.1142/s0218213021400078.

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This paper presents a neurodynamic optimization approach for l1 minimization based on an augmented Lagrangian function. By using the threshold function in locally competitive algorithm (LCA), subgradient at a nondifferential point is equivalently replaced with the difference of the neuronal state and its mapping. The efficacy of the proposed approach is substantiated by reconstructing three compressed images.
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25

Jiang, Xinrui, Sitian Qin, Xiaoping Xue, and Xinzhi Liu. "A second-order accelerated neurodynamic approach for distributed convex optimization." Neural Networks 146 (February 2022): 161–73. http://dx.doi.org/10.1016/j.neunet.2021.11.013.

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26

Qin, Sitian, Yadong Liu, Xiaoping Xue, and Fuqiang Wang. "A neurodynamic approach to convex optimization problems with general constraint." Neural Networks 84 (December 2016): 113–24. http://dx.doi.org/10.1016/j.neunet.2016.08.014.

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27

Le, Xinyi, and Jun Wang. "A Two-Time-Scale Neurodynamic Approach to Constrained Minimax Optimization." IEEE Transactions on Neural Networks and Learning Systems 28, no. 3 (2017): 620–29. http://dx.doi.org/10.1109/tnnls.2016.2538288.

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28

Wang, Jiasen, Jun Wang, and Hangjun Che. "Task Assignment for Multivehicle Systems Based on Collaborative Neurodynamic Optimization." IEEE Transactions on Neural Networks and Learning Systems 31, no. 4 (2020): 1145–54. http://dx.doi.org/10.1109/tnnls.2019.2918984.

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29

Che, Hangjun, and Jun Wang. "A Two-Timescale Duplex Neurodynamic Approach to Mixed-Integer Optimization." IEEE Transactions on Neural Networks and Learning Systems 32, no. 1 (2021): 36–48. http://dx.doi.org/10.1109/tnnls.2020.2973760.

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30

Le, Xinyi, Sijie Chen, Zheng Yan, and Juntong Xi. "A Neurodynamic Approach to Distributed Optimization With Globally Coupled Constraints." IEEE Transactions on Cybernetics 48, no. 11 (2018): 3149–58. http://dx.doi.org/10.1109/tcyb.2017.2760908.

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31

Liu, Na, and Sitian Qin. "A Novel Neurodynamic Approach to Constrained Complex-Variable Pseudoconvex Optimization." IEEE Transactions on Cybernetics 49, no. 11 (2019): 3946–56. http://dx.doi.org/10.1109/tcyb.2018.2855724.

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32

Jia, Wenwen, Tingwen Huang, and Sitian Qin. "A collective neurodynamic penalty approach to nonconvex distributed constrained optimization." Neural Networks 171 (March 2024): 145–58. http://dx.doi.org/10.1016/j.neunet.2023.12.011.

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33

Drozdovski, A. K., A. A. Banayan, and L. G. Ulyaeva. "Psycho-physiological approach to the problem of giftedness and high-quality sports selection." Current Issues of Sports Psychology and Pedagogy 1, no. 1-2 (2021): 100–114. http://dx.doi.org/10.15826/spp.2021.1-2.11.

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The article notes that the problem of giftedness (talent) and highquality sports selection cannot be solved only by measuring anthropometric indicators, or only by tests-questionnaires, conversations, interviews, observations, which are dominate in the sports psychologists arsenal of nowadays. Meanwhile, the scientific developments of the national differential psychophysiology, that expand the possibilities for solving the problems indicated in the article, are ignored. The psychophysiological approach proposed by the authors is based on the method for assessing the natural predisposition of the subject to the definite sports specializations that presupposes the algorithm of actions: instrumental measurement of the nervous system’s properties (NSP, or otherwise, – neurodynamic characteristics) by E.P. Ilyin’s motor techniques; determination of the individual neurodynamic characteristics of the subject and their comparison with the known, experimentally identified “model” neurodynamic characteristics, which dominate, in terms of frequency of occurrence, among representatives of high-performance sports. The authors note a well-known scientific fact – the human’s NSP are rather conservative to changes in the growing-up process, which is essential to justification for the proposed psychophysiological approach to the problem of giftedness and selection in sports. The indications of scientific data are also significant, which are confirming the trend that with many possible combinations measuring by NSP, as a part of typological complexes (TC), the number of the latest is steeply reduced to several or even to one, dominating among athletes who have reached a high level of skill. The article states that the knowledge of the model neurodynamic characteristics dominating among representatives of different specializations in high-performance sports is the experimental basis, considering which it becomes possible early (6 years and older) identification of potentially gifted athletes, which is quite practicable if the neurodynamic characteristics of the subject for whom the choice of sports specialization is made are also known. The article notes that the optimization of training programs in the chosen sports specialization is impossible without knowing the severity of natural psychological abilities, peculiarities, and an example is given of such a forecast for athletes with different playing positions (forward, goaltender, defender), where the forecast is based on individual neurodynamic characteristics.
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34

Demin, D. B., L. V. Poskotinova, and Ye V. Krivonogova. "EEG CHARACTERISTICS AND THYROID PROFILE RATIO IN ADOLESCENTS OF SUBPOLAR AND POLAR EUROPEAN NORTH AREAS." Bulletin of Siberian Medicine 12, no. 1 (2013): 24–29. http://dx.doi.org/10.20538/1682-0363-2013-1-24-29.

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Features of brain bioelectric activity and thyroid system in adolescents living in Subpolar andPolar regionsof the North are considered. Hyperactivity of subcortical diencephalic brain structures in adolescents of the Polar region is revealed. Adolescents of Subpolar region have more intensive age optimization of neurodynamic processes. There are noted latitude distinctions of thyroid hormones role for age formation of brain bioelectric activity in adolescents.
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35

Wang, Yadi, Xiaoping Li, and Jun Wang. "A neurodynamic optimization approach to supervised feature selection via fractional programming." Neural Networks 136 (April 2021): 194–206. http://dx.doi.org/10.1016/j.neunet.2021.01.004.

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36

Chang, Xinyue, Yinliang Xu, and Hongbin Sun. "Online distributed neurodynamic optimization for energy management of renewable energy grids." International Journal of Electrical Power & Energy Systems 130 (September 2021): 106996. http://dx.doi.org/10.1016/j.ijepes.2021.106996.

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37

Fang, Xiaomeng, Dong Pang, Juntong Xi, and Xinyi Le. "Distributed optimization for the multi-robot system using a neurodynamic approach." Neurocomputing 367 (November 2019): 103–13. http://dx.doi.org/10.1016/j.neucom.2019.08.032.

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38

Jiang, Xinrui, Sitian Qin, and Xiaoping Xue. "A penalty-like neurodynamic approach to constrained nonsmooth distributed convex optimization." Neurocomputing 377 (February 2020): 225–33. http://dx.doi.org/10.1016/j.neucom.2019.10.050.

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39

He, Shengzhan, Junjian Huang, and Xing He. "Collective Neurodynamic Optimization for Image Segmentation by Binary Model with Constraints." Cognitive Computation 12, no. 6 (2020): 1265–75. http://dx.doi.org/10.1007/s12559-020-09762-0.

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40

Zeng, Zhigang, Andrzej Cichocki, Long Cheng, Youshen Xia, and Xiaolin Hu. "Guest Editorial Special Issue on Neurodynamic Systems for Optimization and Applications." IEEE Transactions on Neural Networks and Learning Systems 27, no. 2 (2016): 210–13. http://dx.doi.org/10.1109/tnnls.2016.2515458.

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41

Nazemi, Alireza. "Solving general convex nonlinear optimization problems by an efficient neurodynamic model." Engineering Applications of Artificial Intelligence 26, no. 2 (2013): 685–96. http://dx.doi.org/10.1016/j.engappai.2012.09.011.

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42

Lin, Meng, Zicong Xia, Shuting Sun, and Yang Liu. "Distributed neurodynamic optimization for optimal multicluster resource allocation with cardinality constraints." Information Sciences 712 (September 2025): 122138. https://doi.org/10.1016/j.ins.2025.122138.

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43

Yu, Dongmei, Shaowei Lin, Gehao Zhang, and Hongrui Yin. "Predefined-time with time-varying coefficients neurodynamic for composite optimization problems." Chaos, Solitons & Fractals 199 (October 2025): 116792. https://doi.org/10.1016/j.chaos.2025.116792.

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44

Huang, Banghua, Yang Liu, Yun-Liang Jiang, and Jun Wang. "Two-timescale projection neural networks in collaborative neurodynamic approaches to global optimization and distributed optimization." Neural Networks 169 (January 2024): 83–91. http://dx.doi.org/10.1016/j.neunet.2023.10.011.

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45

Demin, D. B. "THE ASSESSMENT OF REACTIONS OF POLYGRAPHIC PARAMETERS AT HRV-BIOFEEDBACK TRAINING IN ADOLESCENTS WITH DIFFERENT VARIANTS OF CARDIAC AUTONOMIC NERVOUS SYSTEM TONE." Annals of the Russian academy of medical sciences 67, no. 2 (2012): 11–15. http://dx.doi.org/10.15690/vramn.v67i2.117.

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There is examine a character of change of brain bioelectric activity and polygraphic indicators at sessions of biofeedback by heart rhythm variability parameters (HRV-biofeedback) in 15–17 years adolescents who have different variants of cardiac autonomic nervous system tone. It is taped, that adolescents with cardiac balanced tone have more intensive optimization of functional brain activity in comparison with adolescents who have cardiac sympathetic tone — increase on alpha-activity and theta-activity depression in electroencephalogram structure. There were optimization of neurodynamic processes and most expressed stabilization of the hemodynamics indicators in adolescents with cardiac sympathetic tone after HRVbiofeedback training.
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46

Berga, David, and Xavier Otazu. "A Neurodynamic Model of Saliency Prediction in V1." Neural Computation 34, no. 2 (2022): 378–414. http://dx.doi.org/10.1162/neco_a_01464.

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Abstract Lateral connections in the primary visual cortex (V1) have long been hypothesized to be responsible for several visual processing mechanisms such as brightness induction, chromatic induction, visual discomfort, and bottom-up visual attention (also named saliency). Many computational models have been developed to independently predict these and other visual processes, but no computational model has been able to reproduce all of them simultaneously. In this work, we show that a biologically plausible computational model of lateral interactions of V1 is able to simultaneously predict saliency and all the aforementioned visual processes. Our model's architecture (NSWAM) is based on Penacchio's neurodynamic model of lateral connections of V1. It is defined as a network of firing rate neurons, sensitive to visual features such as brightness, color, orientation, and scale. We tested NSWAM saliency predictions using images from several eye tracking data sets. We show that the accuracy of predictions obtained by our architecture, using shuffled metrics, is similar to other state-of-the-art computational methods, particularly with synthetic images (CAT2000-Pattern and SID4VAM) that mainly contain low-level features. Moreover, we outperform other biologically inspired saliency models that are specifically designed to exclusively reproduce saliency. We show that our biologically plausible model of lateral connections can simultaneously explain different visual processes present in V1 (without applying any type of training or optimization and keeping the same parameterization for all the visual processes). This can be useful for the definition of a unified architecture of the primary visual cortex.
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47

Li, Xinqi, Jun Wang, and Sam Kwong. "A Discrete-Time Neurodynamic Approach to Sparsity-Constrained Nonnegative Matrix Factorization." Neural Computation 32, no. 8 (2020): 1531–62. http://dx.doi.org/10.1162/neco_a_01294.

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Sparsity is a desirable property in many nonnegative matrix factorization (NMF) applications. Although some level of sparseness of NMF solutions can be achieved by using regularization, the resulting sparsity depends highly on the regularization parameter to be valued in an ad hoc way. In this letter we formulate sparse NMF as a mixed-integer optimization problem with sparsity as binary constraints. A discrete-time projection neural network is developed for solving the formulated problem. Sufficient conditions for its stability and convergence are analytically characterized by using Lyapunov's method. Experimental results on sparse feature extraction are discussed to substantiate the superiority of this approach to extracting highly sparse features.
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48

Peng, Zhouhua, Jun Wang, and Dan Wang. "Distributed Maneuvering of Autonomous Surface Vehicles Based on Neurodynamic Optimization and Fuzzy Approximation." IEEE Transactions on Control Systems Technology 26, no. 3 (2018): 1083–90. http://dx.doi.org/10.1109/tcst.2017.2699167.

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49

Le, Xinyi, Zheng Yan, and Juntong Xi. "A Collective Neurodynamic System for Distributed Optimization with Applications in Model Predictive Control." IEEE Transactions on Emerging Topics in Computational Intelligence 1, no. 4 (2017): 305–14. http://dx.doi.org/10.1109/tetci.2017.2716377.

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50

Luan, Linhua, Xingnan Wen, and Sitian Qin. "Distributed neurodynamic approaches to nonsmooth optimization problems with inequality and set constraints." Complex & Intelligent Systems, May 30, 2022. http://dx.doi.org/10.1007/s40747-022-00770-1.

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AbstractIn this paper, neurodynamic approaches are proposed for solving nonsmooth distributed optimization problems under inequality and set constraints, that is to find the solution that minimizes the sum of local cost functions. A continuous-time neurodynamic approach is designed and its state solution exists globally and converges to an optimal solution of the corresponding distributed optimization problem. Then, a neurodynamic approach with event-triggered mechanism is considered for the purpose of saving communication costs, and then, the convergence and its Zeno-free property are proved. Moreover, to realize the practical application of the neurodynamic approach, a discrete-time neurodynamic approach is proposed to solve nonsmooth distributed optimization problems under inequality and set constraints. It is rigorously proved that the iterative sequence generated by the discrete-time neurodynamic approach converges to the optimal solution set of the distributed optimization problem. Finally, numerical examples are solved to demonstrate the effectiveness of the proposed neurodynamic approaches, and the neurodynamic approach is further applied to solve the ill-conditioned Least Absolute Deviation problem and the load sharing optimization problem.
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