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A Winner-Take-All Neural Networks of N Linear Threshold Neurons without Self-Excitatory Connections

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Abstract

Multistable neural networks have attracted much interests in recent years, since the monostable networks are computationally restricted. This paper studies a N linear threshold neurons recurrent networks without Self-Excitatory connections. Our studies show that this network performs a Winner-Take-All (WTA) behavior, which has been recognized as a basic computational model done in brain. The contributions of this paper are: (1) It proves by mathematics that the proposed model is Non-Divergent. (2) An important implication (Winner-Take-All) of the proposed network model is studied. (3) Digital computer simulations are carried out to validate the performance of the theory findings.

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Authors and Affiliations

  1. School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 610054, People’s Republic of China

    Hong Qu, Zhang Yi & Xiaobin Wang

Authors
  1. Hong Qu
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  2. Zhang Yi
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  3. Xiaobin Wang
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Corresponding author

Correspondence to Hong Qu.

Additional information

This work was supported by Specialized Research Fund for the Doctoral Program of Higher Education of China under Grant 200806141049.

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Qu, H., Yi, Z. & Wang, X. A Winner-Take-All Neural Networks of N Linear Threshold Neurons without Self-Excitatory Connections. Neural Process Lett 29, 143–154 (2009). https://doi.org/10.1007/s11063-009-9100-x

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  • DOI: https://doi.org/10.1007/s11063-009-9100-x

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