Designing pulse-coupled neural networks with spike-synchronization-dependent plasticity rule: image segmentation and memristor circuit application
Publication
, Journal Article
Xie, X; Wen, S; Yan, Z; Huang, T; Chen, Y
Published in: Neural Computing and Applications
September 1, 2020
Pulse-coupled neural network (PCNN) is a powerful unsupervised learning model with many parameters to be determined empirically. In particular, the weight matrix is invariable in the iterative process, which is inconsistent with the actual biological system. Based on the existing research foundation of biology and neural network, we propose a spike-synchronization-dependent plasticity (SSDP) rule. In this paper, the mathematical model and algorithm of SSDP are presented. Furthermore, a novel memristor-based circuit model of SSDP is designed. Finally, experimental results demonstrate that SSDP has greatly improved the image processing capabilities of PCNN.
Duke Scholars
Altmetric Attention Stats
Dimensions Citation Stats
Published In
Neural Computing and Applications
DOI
EISSN
1433-3058
ISSN
0941-0643
Publication Date
September 1, 2020
Volume
32
Issue
17
Start / End Page
13441 / 13452
Related Subject Headings
- Artificial Intelligence & Image Processing
- 4611 Machine learning
- 4603 Computer vision and multimedia computation
- 4602 Artificial intelligence
- 1702 Cognitive Sciences
- 0906 Electrical and Electronic Engineering
- 0801 Artificial Intelligence and Image Processing
Citation
APA
Chicago
ICMJE
MLA
NLM
Xie, X., Wen, S., Yan, Z., Huang, T., & Chen, Y. (2020). Designing pulse-coupled neural networks with spike-synchronization-dependent plasticity rule: image segmentation and memristor circuit application. Neural Computing and Applications, 32(17), 13441–13452. https://doi.org/10.1007/s00521-020-04752-7
Xie, X., S. Wen, Z. Yan, T. Huang, and Y. Chen. “Designing pulse-coupled neural networks with spike-synchronization-dependent plasticity rule: image segmentation and memristor circuit application.” Neural Computing and Applications 32, no. 17 (September 1, 2020): 13441–52. https://doi.org/10.1007/s00521-020-04752-7.
Xie X, Wen S, Yan Z, Huang T, Chen Y. Designing pulse-coupled neural networks with spike-synchronization-dependent plasticity rule: image segmentation and memristor circuit application. Neural Computing and Applications. 2020 Sep 1;32(17):13441–52.
Xie, X., et al. “Designing pulse-coupled neural networks with spike-synchronization-dependent plasticity rule: image segmentation and memristor circuit application.” Neural Computing and Applications, vol. 32, no. 17, Sept. 2020, pp. 13441–52. Scopus, doi:10.1007/s00521-020-04752-7.
Xie X, Wen S, Yan Z, Huang T, Chen Y. Designing pulse-coupled neural networks with spike-synchronization-dependent plasticity rule: image segmentation and memristor circuit application. Neural Computing and Applications. 2020 Sep 1;32(17):13441–13452.
Published In
Neural Computing and Applications
DOI
EISSN
1433-3058
ISSN
0941-0643
Publication Date
September 1, 2020
Volume
32
Issue
17
Start / End Page
13441 / 13452
Related Subject Headings
- Artificial Intelligence & Image Processing
- 4611 Machine learning
- 4603 Computer vision and multimedia computation
- 4602 Artificial intelligence
- 1702 Cognitive Sciences
- 0906 Electrical and Electronic Engineering
- 0801 Artificial Intelligence and Image Processing