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Journal articles on the topic 'Pulse-Coupled Neural Networks'

Author: Grafiati

Published: 4 June 2021

Last updated: 13 February 2022

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1

Wang, Zhaobin, Yide Ma, Feiyan Cheng, and Lizhen Yang. "Review of pulse-coupled neural networks." Image and Vision Computing 28, no. 1 (January 2010): 5–13. http://dx.doi.org/10.1016/j.imavis.2009.06.007.

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2

Ota, Y., and B. M. Wilamowski. "Analog implementation of pulse-coupled neural networks." IEEE Transactions on Neural Networks 10, no. 3 (May 1999): 539–44. http://dx.doi.org/10.1109/72.761710.

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3

Ranganath, H. S., and G. Kuntimad. "Object detection using pulse coupled neural networks." IEEE Transactions on Neural Networks 10, no. 3 (May 1999): 615–20. http://dx.doi.org/10.1109/72.761720.

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4

Olmi, S., A. Politi, and A. Torcini. "Collective chaos in pulse-coupled neural networks." EPL (Europhysics Letters) 92, no. 6 (December 1, 2010): 60007. http://dx.doi.org/10.1209/0295-5075/92/60007.

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5

Monica Subashini, M., and Sarat Kumar Sahoo. "Pulse coupled neural networks and its applications." Expert Systems with Applications 41, no. 8 (June 2014): 3965–74. http://dx.doi.org/10.1016/j.eswa.2013.12.027.

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6

WANG, Xin, Yi-de MA, Zhi-jian XU, and Lian-feng LI. "Chaos control based on pulse-coupled neural networks." Journal of Computer Applications 29, no. 12 (March 1, 2010): 3277–79. http://dx.doi.org/10.3724/sp.j.1087.2009.03277.

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7

Kuntimad, G., and H. S. Ranganath. "Perfect image segmentation using pulse coupled neural networks." IEEE Transactions on Neural Networks 10, no. 3 (May 1999): 591–98. http://dx.doi.org/10.1109/72.761716.

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8

Kanamaru, Takashi, and Kazuyuki Aihara. "Rewiring-Induced Chaos in Pulse-Coupled Neural Networks." Neural Computation 24, no. 4 (April 2012): 1020–46. http://dx.doi.org/10.1162/neco_a_00252.

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The dependence of the dynamics of pulse-coupled neural networks on random rewiring of excitatory and inhibitory connections is examined. When both excitatory and inhibitory connections are rewired, periodic synchronization emerges with a Hopf-like bifurcation and a subsequent period-doubling bifurcation; chaotic synchronization is also observed. When only excitatory connections are rewired, periodic synchronization emerges with a saddle node–like bifurcation, and chaotic synchronization is also observed. This result suggests that randomness in the system does not necessarily contaminate the system, and sometimes it even introduces rich dynamics to the system such as chaos.

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9

Xu, Xinzheng, Guanying Wang, Shifei Ding, Yuhu Cheng, and Xuesong Wang. "Pulse-coupled neural networks and parameter optimization methods." Neural Computing and Applications 28, S1 (June 4, 2016): 671–81. http://dx.doi.org/10.1007/s00521-016-2397-2.

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10

He, Changtao, Fangnian Lang, and Hongliang Li. "Medical Image Registration using Cascaded Pulse Coupled Neural Networks." Information Technology Journal 10, no. 9 (August 15, 2011): 1733–39. http://dx.doi.org/10.3923/itj.2011.1733.1739.

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11

Yu, B., and L. Zhang. "Pulse-Coupled Neural Networks for Contour and Motion Matchings." IEEE Transactions on Neural Networks 15, no. 5 (September 2004): 1186–201. http://dx.doi.org/10.1109/tnn.2004.832830.

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12

Li, Yunxia, Zhang Yi, and Jian Cheng Lv. "Support vector set selection using pulse-coupled neural networks." Neural Computing and Applications 25, no. 2 (November 9, 2013): 401–10. http://dx.doi.org/10.1007/s00521-013-1506-8.

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13

Haken, H. "Pattern Recognition and Synchronization in Pulse-Coupled Neural Networks." Nonlinear Dynamics 44, no. 1-4 (June 2006): 269–76. http://dx.doi.org/10.1007/s11071-006-2000-y.

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14

Yao, Na, and Gang Wu. "Damaged Red Dates Detection Based PCNN and GVF Snake Model." Applied Mechanics and Materials 543-547 (March 2014): 1107–10. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.1107.

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According to the feature of moldy red dates, Pulse Coupled Neural Networks and Snake model are presented to detect damaged region of red dates so that the bad ones can be picked. Firstly, Pulse Coupled Neural Networks is used to segment red date images. Segmented images are binary images, in which wrinkled and decayed regions separate from other well regions. Then edge is detected using Pulse Coupled Neural Networks and this edge will be defined as initial contour of GVF Snake model. Finally, GVF Snake model is used to detect the decayed regions. Experiments show that this proposed method can extract decayed regions of red dates efficiently.

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15

Haken, Hermann. "Delay, noise and phase locking in pulse coupled neural networks." Biosystems 63, no. 1-3 (November 2001): 15–20. http://dx.doi.org/10.1016/s0303-2647(01)00143-5.

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16

Nisha, S. Shajun, and S. Kother Mohideen. "Noise Removal in Medical Images Using Pulse Coupled Neural Networks." Journal of Medical Imaging and Health Informatics 7, no. 1 (February 1, 2017): 101–5. http://dx.doi.org/10.1166/jmihi.2017.1990.

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17

Ly, Cheng, and G. Bard Ermentrout. "Analysis of Recurrent Networks of Pulse-Coupled Noisy Neural Oscillators." SIAM Journal on Applied Dynamical Systems 9, no. 1 (January 2010): 113–37. http://dx.doi.org/10.1137/090756065.

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18

Kinser, J. M., and T. Lindblad. "Implementation of pulse-coupled neural networks in a CNAPS environment." IEEE Transactions on Neural Networks 10, no. 3 (May 1999): 584–90. http://dx.doi.org/10.1109/72.761715.

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19

Zhan, Kun, Jinhui Shi, Haibo Wang, Yuange Xie, and Qiaoqiao Li. "Computational Mechanisms of Pulse-Coupled Neural Networks: A Comprehensive Review." Archives of Computational Methods in Engineering 24, no. 3 (July 19, 2016): 573–88. http://dx.doi.org/10.1007/s11831-016-9182-3.

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20

Gu, X., D. Yu, and L. Zhang. "Image Shadow Removal Using Pulse Coupled Neural Network." IEEE Transactions on Neural Networks 16, no. 3 (May 2005): 692–98. http://dx.doi.org/10.1109/tnn.2005.844902.

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21

Li, Yan-Long, Zhao-Yang Chen, Jun Ma, and Yu-Hong Chen. "Simulation study of stimulation parameters in desynchronisation based on the Hodgkin-Huxley small-world neural networks and its possible implications for vagus nerve stimulation." Acta Neuropsychiatrica 20, no. 1 (February 2008): 25–32. http://dx.doi.org/10.1111/j.1601-5215.2007.00254.x.

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Adopting small-world neural networks of the Hodgkin-Huxley (HH) model, the stimulation parameters in desynchronisation and its possible implications for vagus nerve stimulation (VNS) are numerically investigated. With the synchronisation status of networks to represent epilepsy, then, adding pulse to stimulations to 10% of neurons to simulate the VNS, we obtain the desynchronisation status of networks (representing antiepileptic effects). The simulations show that synchronisation evolves into desynchronisation in the HH neural networks when a part (10%) of neurons are stimulated with a pulse current signal. The network desynchronisation appears to be sensitive to the stimulation parameters. For the case of the same stimulation intensity, weakly coupled networks reach desynchronisation more easily than strongly coupled networks. The network desynchronisation reduced by short-stimulation interval is more distinct than that of induced by long stimulation interval. We find that there exist the optimal stimulation interval and optimal stimulation intensity when the other stimulation parameters remain certain.

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22

NAKANO, Hidehiro, Akihide UTANI, Arata MIYAUCHI, and Hisao YAMAMOTO. "Data Gathering Scheme Using Chaotic Pulse-Coupled Neural Networks for Wireless Sensor Networks." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E92-A, no. 2 (2009): 459–66. http://dx.doi.org/10.1587/transfun.e92.a.459.

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23

Sang, Yongsheng, Zhang Yi, and Jiliu Zhou. "Spatial Point-Data Reduction Using Pulse Coupled Neural Network." Neural Processing Letters 32, no. 1 (May 26, 2010): 11–29. http://dx.doi.org/10.1007/s11063-010-9140-2.

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24

Han, Zhen-zhong, Hou-jin Chen, Ju-peng Li, Chang Yao, and Lin Cheng. "Mass Detection in Mammogram Based on Marker-pulse Coupled Neural Networks." Journal of Electronics & Information Technology 35, no. 7 (February 24, 2014): 1664–70. http://dx.doi.org/10.3724/sp.j.1146.2012.01473.

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25

ZHENG Xin, 郑欣, and 彭真明 PENG Zhen-ming. "Image segmentation based on activity degree with pulse coupled neural networks." Optics and Precision Engineering 21, no. 3 (2013): 821–27. http://dx.doi.org/10.3788/ope.20132103.0821.

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26

Sang, Yongsheng, Jiancheng Lv, Hong Qu, and Zhang Yi. "Shortest path computation using pulse-coupled neural networks with restricted autowave." Knowledge-Based Systems 114 (December 2016): 1–11. http://dx.doi.org/10.1016/j.knosys.2016.08.027.

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27

Mureşan, Raul C. "Pattern recognition using pulse-coupled neural networks and discrete Fourier transforms." Neurocomputing 51 (April 2003): 487–93. http://dx.doi.org/10.1016/s0925-2312(02)00727-0.

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28

He, Kangjian, Ruxin Wang, Dapeng Tao, Jun Cheng, and Weifeng Liu. "Color Transfer Pulse-Coupled Neural Networks for Underwater Robotic Visual Systems." IEEE Access 6 (2018): 32850–60. http://dx.doi.org/10.1109/access.2018.2845855.

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29

Hong Qu, Zhang Yi, and S. X. Yang. "Efficient Shortest-Path-Tree Computation in Network Routing Based on Pulse-Coupled Neural Networks." IEEE Transactions on Cybernetics 43, no. 3 (June 2013): 995–1010. http://dx.doi.org/10.1109/tsmcb.2012.2221695.

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30

Cai, Xi, Han Guang, and Jin Kuan Wang. "Multiwavelet-Based Image Fusion Method Using Unit-Linking Pulse Coupled Neural Networks." Advanced Materials Research 905 (April 2014): 548–51. http://dx.doi.org/10.4028/www.scientific.net/amr.905.548.

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To simulate biological activities of human visual system to details and make full use of global features of source images, we propose a multiwavelet-based image fusion method using unit-linking pulse coupled neural networks (ULPCNNs) model. After motivated by external stimuli from images, ULPCNNs can produce series of binary pulses containing much global information. Then we employ the first firing time of each neuron as the salience measure. Experimental results demonstrate that, for multifocus images, remote sensing images, and infrared and visible images, our proposed method always generates satisfying fusion results.

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31

Wang Bo, 王博, 万磊 Wan Lei, 李晔 Li Ye, and 张铁栋 Zhang Tiedong. "Underwater Laser Image Segmentation Method based on Adaptive Pulse Coupled Neural Networks." Acta Optica Sinica 35, no. 4 (2015): 0410004. http://dx.doi.org/10.3788/aos201535.0410004.

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32

CHANG, Wei-Wei, Lei GUO, Zhao-Yang FU, and Kun LIU. "HYPERSPECTRAL MULTI-BAND IMAGE FUSION ALGORITHM BY USING PULSE COUPLED NEURAL NETWORKS." JOURNAL OF INFRARED AND MILLIMETER WAVES 29, no. 3 (July 5, 2010): 205–9. http://dx.doi.org/10.3724/sp.j.1010.2010.00205.

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33

Murugavel, Murali, and John M. Sullivan. "Automatic cropping of MRI rat brain volumes using pulse coupled neural networks." NeuroImage 45, no. 3 (April 2009): 845–54. http://dx.doi.org/10.1016/j.neuroimage.2008.12.021.

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34

Du, Songlin, Yi Huang, Jianlin Ma, and Yide Ma. "Mammalian visual characteristics inspired perceptual image quantization using pulse-coupled neural networks." Optik 126, no. 21 (November 2015): 3135–39. http://dx.doi.org/10.1016/j.ijleo.2015.07.072.

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35

Chandrasekaran, Lakshmi, Srisairam Achuthan, and Carmen C. Canavier. "Stability of two cluster solutions in pulse coupled networks of neural oscillators." Journal of Computational Neuroscience 30, no. 2 (August 20, 2010): 427–45. http://dx.doi.org/10.1007/s10827-010-0268-x.

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36

Sugiyama, Taiji, Noriyasu Homma, Kenichi Abe, and Masao Sakai. "Speech recognition using pulse-coupled neural networks with a radial basis function." Artificial Life and Robotics 7, no. 4 (April 2004): 156–59. http://dx.doi.org/10.1007/bf02471198.

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37

Wang, Ma Hua. "An Improved Algorithm for Medical Image Fusion Based on Pulse Coupled Neural Networks." Advanced Materials Research 340 (September 2011): 492–97. http://dx.doi.org/10.4028/www.scientific.net/amr.340.492.

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For the sake of overcoming the shortage of transitional region and marginal area information loss, especially lost texture information resulting from pixel-based pulse coupled neural network (PCNN) method, a region-based algorithm, which combined redundancy, shift-invariance of stationary wavelet transform (SWT) and regional firing intensity of PCNN, was present. This would provide more and exact information for clinical diagnosis, determination of Lesion distribution, Open-MRI-Guided surgery and so on by more effectively information drawn from sub-images. Finally, experimental results, that shown the proposed algorithm outperform other methods according to objective evaluation criteria, were given.

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38

LIU, Qing, Lu-Ping XU, Yi-De MA, and Yong WANG. "Image NMI Feature Extraction and Retrieval Method Based on Pulse Coupled Neural Networks." Acta Automatica Sinica 36, no. 7 (August 3, 2010): 931–38. http://dx.doi.org/10.3724/sp.j.1004.2010.00931.

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39

Pacifici, Fabio, and Fabio Del Frate. "Automatic Change Detection in Very High Resolution Images With Pulse-Coupled Neural Networks." IEEE Geoscience and Remote Sensing Letters 7, no. 1 (January 2010): 58–62. http://dx.doi.org/10.1109/lgrs.2009.2021780.

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40

Li, Yi, and Junli Zhao. "A Novel Medical Image Fusion Method Using Multi-Channel Pulse Coupled Neural Networks." IEEE Access 8 (2020): 157572–86. http://dx.doi.org/10.1109/access.2020.3019426.

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41

Kanamaru, Takashi, and Kazuyuki Aihara. "Roles of Inhibitory Neurons in Rewiring-Induced Synchronization in Pulse-Coupled Neural Networks." Neural Computation 22, no. 5 (May 2010): 1383–98. http://dx.doi.org/10.1162/neco.2010.04-09-997.

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The roles of inhibitory neurons in synchronous firing are examined in a network of excitatory and inhibitory neurons with Watts and Strogatz's rewiring. By examining the persistence of the synchronous firing that exists in the random network, it was found that there is a probability of rewiring at which a transition between the synchronous state and the asynchronous state takes place, and the dynamics of the inhibitory neurons play an important role in determining this probability.

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42

Stewart, R. D., I. Fermin, and M. Opper. "Region growing with pulse-coupled neural networks: an alternative to seeded region growing." IEEE Transactions on Neural Networks 13, no. 6 (November 2002): 1557–62. http://dx.doi.org/10.1109/tnn.2002.804229.

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43

Karvonen, J. A. "Baltic Sea ice SAR segmentation and classification using modified pulse-coupled neural networks." IEEE Transactions on Geoscience and Remote Sensing 42, no. 7 (July 2004): 1566–74. http://dx.doi.org/10.1109/tgrs.2004.828179.

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44

Chou, Nigel, Jiarong Wu, Jordan Bai Bingren, Anqi Qiu, and Kai-Hsiang Chuang. "Robust Automatic Rodent Brain Extraction Using 3-D Pulse-Coupled Neural Networks (PCNN)." IEEE Transactions on Image Processing 20, no. 9 (September 2011): 2554–64. http://dx.doi.org/10.1109/tip.2011.2126587.

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45

Johnson, J. L., M. L. Padgett, and O. Omidvar. "Guest Editorial Overview Of Pulse Coupled Neural Network (PCNN) Special Issue." IEEE Transactions on Neural Networks 10, no. 3 (May 1999): 461–63. http://dx.doi.org/10.1109/tnn.1999.761704.

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46

Ko, Eunhee, and Jungsoo Park. "Diesel Mean Value Engine Modeling Based on Thermodynamic Cycle Simulation Using Artificial Neural Network." Energies 12, no. 14 (July 22, 2019): 2823. http://dx.doi.org/10.3390/en12142823.

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This study aims to construct a reduced thermodynamic cycle model with high accuracy and high model execution speed based on artificial neural network training for real-time numerical analysis. This paper proposes a method of constructing a fast average-value model by combining a 1D plant model and exhaust gas recirculation (EGR) control logic. The combustion model of the detailed model uses a direct-injection diesel multi-pulse (DI-pulse) method similar to diesel combustion characteristics. The DI-pulse combustion method divides the volume of the cylinder into three zones, predicting combustion- and emission-related variables, and each combustion step comprises different correction variables. This detailed model is estimated to be within 5% of the reference engine test results. To reduce the analysis time while maintaining the accuracy of engine performance prediction, the cylinder volumetric efficiency and the exhaust gas temperature were predicted using an artificial neural network. Owing to the lack of input variables in the training of artificial neural networks, it was not possible to predict the 0.6–0.7 range for volumetric efficiency and the 1000–1200 K range for exhaust gas temperature. This is because the mean value model changes the fuel injection method from the common rail fuel injection mode to the single injection mode in the model reduction process and changes the in-cylinder combustion according to the injection timing of the fuel amount injected. In addition, the mean value model combined with EGR logic, i.e., the single-input single-output (SISO) coupled mean value model, verifies the accuracy and responsiveness of the EGR control logic model through a step-transient process. By comparing the engine performance results of the SISO coupled mean value model with those of the mean value model, it is observed that the SISO coupled mean value model achieves the desired target EGR rate within 10 s. The EGR rate is predicted to be similar to the response of volumetric efficiency. This process intuitively predicted the main performance parameters of the engine model through artificial neural networks.

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47

Dehyadegari, Louiza, and Somayeh Khajehasani. "Magnetic Resonance Imaging Image Segmentation and Brain Tumour Detection Using Pulse-Coupled Neural Networks." Journal of Engineering Science 17, no. 1 (May 31, 2021): 1–16. http://dx.doi.org/10.21315/jes2021.17.1.1.

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Image processing can be defined as a functional structure to correct and change the images viewed and their interpretation. One of the applications of digital image processing is using image processing techniques in the component and image segmentation. One of these techniques is magnetic resonance imaging (MRI) in the medical world. In this article, a brain tumour detection system and various anomalies and abnormalities are presented where image pre-processing and preparation include image enhancement, filtering and noise reduction. Then image segmentation is done by a pulse neural network. Next, the image features are extracted and finally, the tumour and abnormal area are separated from the normal area by the algorithms. In this research, the feature selection and integration method are used and the most important statistical features of brain MRI images are used to improve brain tumour detection. Along with the studies done and the implementation of tumour detection systems, the following suggestions can be provided for future researches and the tumour detection system will work more efficiently. The pulse-coupled neural network (PCNN) can be used for image segmentation in the pre-processing stage, especially in the image filtering.

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48

Gu, Xiaodong. "Feature Extraction using Unit-linking Pulse Coupled Neural Network and its Applications." Neural Processing Letters 27, no. 1 (November 10, 2007): 25–41. http://dx.doi.org/10.1007/s11063-007-9057-6.

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49

Gao, Chao, Dongguo Zhou, and Yongcai Guo. "An Iterative Thresholding Segmentation Model Using a Modified Pulse Coupled Neural Network." Neural Processing Letters 39, no. 1 (February 27, 2013): 81–95. http://dx.doi.org/10.1007/s11063-013-9291-z.

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50

Wang Mengjun, 王蒙军, 郭林 Guo Lin, 王霞 Wang Xia, and 郝宁 Hao Ning. "Color Image Segmentation Based on Improved Internal Activity Multi-Channel Pulse Coupled Neural Networks." Laser & Optoelectronics Progress 52, no. 12 (2015): 121001. http://dx.doi.org/10.3788/lop52.121001.

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