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

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Abstract

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.

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Funding

This study is funded by the National Science Foundation of China under the Grant No. 61201422 and the Specialized Research Fund for the Doctoral Program of Higher Education under the Grant No. 20120211120013.

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Authors and Affiliations

  1. School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, Gansu, China

    Kun Zhan, Jinhui Shi, Haibo Wang, Yuange Xie & Qiaoqiao Li

Authors
  1. Kun Zhan
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  2. Jinhui Shi
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  3. Haibo Wang
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  4. Yuange Xie
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  5. Qiaoqiao Li
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Correspondence to Kun Zhan.

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Zhan, K., Shi, J., Wang, H. et al. Computational Mechanisms of Pulse-Coupled Neural Networks: A Comprehensive Review. Arch Computat Methods Eng 24, 573–588 (2017). https://doi.org/10.1007/s11831-016-9182-3

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