Computational Mechanisms of Pulse-Coupled Neural Networks: A Comprehensive Review

Zhan, K; Shi, J; Wang, H; Xie, Y; Li, Q

Computational Mechanisms of Pulse-Coupled Neural Networks: A Comprehensive Review

Zhan, K

Shi, J Wang, H Xie, Y Li, Q

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Publication Type:: Journal Article
Citation:: Archives of Computational Methods in Engineering, 2017, 24 (3), pp. 573 - 588
Issue Date:: 2017-07-01

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Field	Value	Language
dc.contributor.author	Zhan, K https://orcid.org/0000-0002-8000-5682	en_US
dc.contributor.author	Shi, J	en_US
dc.contributor.author	Wang, H	en_US
dc.contributor.author	Xie, Y	en_US
dc.contributor.author	Li, Q	en_US
dc.date.issued	2017-07-01	en_US
dc.identifier.citation	Archives of Computational Methods in Engineering, 2017, 24 (3), pp. 573 - 588	en_US
dc.identifier.issn	1134-3060	en_US
dc.identifier.uri	http://hdl.handle.net/10453/125783
dc.description.abstract	© 2016, CIMNE, Barcelona, Spain. Pulse-coupled neural networks (PCNN) have an inherent ability to process the signals associated with the digital visual images because it is inspired from the neuronal activity in the primary visual area, V1, of the neocortex. This paper provides insight into the internal operations and behaviors of PCNN, and reveals the way how PCNN achieves good performance in digital image processing. The various properties of PCNN are categorized into a novel three-dimensional taxonomy for image processing mechanisms. The first dimension specifies the time matrix of PCNN, the second dimension captures the firing rate of PCNN, and the third dimension is the synchronization of PCNN. Many examples of processing mechanisms are provided to make it clear and concise.	en_US
dc.relation.ispartof	Archives of Computational Methods in Engineering	en_US
dc.relation.isbasedon	10.1007/s11831-016-9182-3	en_US
dc.subject.classification	Applied Mathematics	en_US
dc.title	Computational Mechanisms of Pulse-Coupled Neural Networks: A Comprehensive Review	en_US
dc.type	Journal Article
utslib.citation.volume	3	en_US
utslib.citation.volume	24	en_US
utslib.for	0103 Numerical and Computational Mathematics	en_US
utslib.for	0805 Distributed Computing	en_US
utslib.for	0915 Interdisciplinary Engineering	en_US
utslib.for	01 Mathematical Sciences	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access
pubs.issue	3	en_US
pubs.publication-status	Published	en_US
pubs.volume	24	en_US

Abstract:

© 2016, CIMNE, Barcelona, Spain. Pulse-coupled neural networks (PCNN) have an inherent ability to process the signals associated with the digital visual images because it is inspired from the neuronal activity in the primary visual area, V1, of the neocortex. This paper provides insight into the internal operations and behaviors of PCNN, and reveals the way how PCNN achieves good performance in digital image processing. The various properties of PCNN are categorized into a novel three-dimensional taxonomy for image processing mechanisms. The first dimension specifies the time matrix of PCNN, the second dimension captures the firing rate of PCNN, and the third dimension is the synchronization of PCNN. Many examples of processing mechanisms are provided to make it clear and concise.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/125783