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A Weakly Connected Memristive Neural Network for Associative Memory

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Abstract

In this paper, we propose a star-like weakly connected memristive neural network which is organized in such a way that each cell only interacts with the central cells. By using the describing function method and Malkin’s theorem the phase deviation of this dynamical network is obtained. And then, under the Hebbian learning rule the phase deviation is designed as a desired model for associative memory. Moreover, we take the store and recall of digital images as an example to demonstrate the performance of associative memory. The main contribution of this paper is supply a useful mechanism which the new potential circuit element memristor can be used to realize the associative.

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Acknowledgments

This publication was made possible by NPRP grant # NPRP 4-1162-1-181 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. This work was also supported by Natural Science Foundation of China (grant no: 61374078).

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Authors and Affiliations

  1. College of Computer Science, Chongqing University, Chongqing, 400044, China

    Xin Wang & Chuandong Li

  2. Texas A&M University at Qatar, Doha, 23874, Qatar

    Tingwen Huang

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

    Shukai Duan

Authors
  1. Xin Wang
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  2. Chuandong Li
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  3. Tingwen Huang
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  4. Shukai Duan
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Correspondence to Chuandong Li.

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Wang, X., Li, C., Huang, T. et al. A Weakly Connected Memristive Neural Network for Associative Memory. Neural Process Lett 40, 275–288 (2014). https://doi.org/10.1007/s11063-013-9328-3

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