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Memristor-based chaotic neural networks for associative memory

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Abstract

In chaotic neural networks, the rich dynamic behaviors are generated from the contributions of spatio-temporal summation, continuous output function, and refractoriness. However, a large number of spatio-temporal summations in turn make the physical implementation of a chaotic neural network impractical. This paper proposes and investigates a memristor-based chaotic neural network model, which adequately utilizes the memristor with unique memory ability to realize the spatio-temporal summations in a simple way. Furthermore, the associative memory capabilities of the proposed memristor-based chaotic neural network have been demonstrated by conventional methods, including separation of superimposed pattern, many-to-many associations, and successive learning. Thanks to the nanometer scale size and automatic memory ability of the memristors, the proposed scheme is expected to greatly simplify the structure of chaotic neural network and promote the hardware implementation of chaotic neural networks.

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Acknowledgments

The work was supported by Program for New Century Excellent Talents in University (Grant Nos.[2013]47), National Natural Science Foundation of China (Grant Nos. 61372139, 61101233, 60972155), “Spring Sunshine Plan” Research Project of Ministry of Education of China (Grant No. z2011148), Technology Foundation for Selected Overseas Chinese Scholars, Ministry of Personnel in China (Grant No. 2012-186), University Excellent Talents Supporting Foundations in of Chongqing (Grant No. 2011-65), University Key Teacher Supporting Foundations of Chongqing (Grant No. 2011-65), Fundamental Research Funds for the Central Universities (Grant Nos. XDJK2014A009, XDJK2013B011).

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Authors and Affiliations

  1. School of Electronics and Information Engineering, Southwest University, Chongqing, 400715, China

    Shukai Duan, Yi Zhang, Lidan Wang & Chuandong Li

  2. Department of Mechanical and Biomedical Engineering, City University of Hong Kong, 83 Tat Chee Avenue, Hong Kong, China

    Xiaofang Hu

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  1. Shukai Duan
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  2. Yi Zhang
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  3. Xiaofang Hu
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  4. Lidan Wang
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  5. Chuandong Li
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Corresponding authors

Correspondence to Shukai Duan or Xiaofang Hu.

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Duan, S., Zhang, Y., Hu, X. et al. Memristor-based chaotic neural networks for associative memory. Neural Comput & Applic 25, 1437–1445 (2014). https://doi.org/10.1007/s00521-014-1633-x

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  • DOI: https://doi.org/10.1007/s00521-014-1633-x

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