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An Improved Memristor-Based Associative Memory Circuit for Full-Function Pavlov Experiment

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Advances in Neural Networks – ISNN 2019 (ISNN 2019)

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Abstract

Associative memory networks have been extensively studied to imitate the biological associative learning. The control circuits of most associative memory circuits are more complicated. Using the memristor with forgetting effect as a synapse can significantly reduce the complexity of the circuit. In this paper, an associative memory circuit based on a forgetting memristor is proposed to implement full-function Pavlov associative memory. The learning process and forgetting process in the Pavlov experiment, including forgetting under ringing stimuli, forgetting under food stimuli, and forgetting over time, can be achieved by the proposed circuit. The PSPICE simulation results demonstrate the effectiveness of the proposed circuit.

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References

  1. Chua, L.: Memristor-the missing circuit element. IEEE Trans. Circ. Theory 18(5), 507–519 (1971)

    Article  Google Scholar 

  2. Strukov, D.B., Snider, G.S., Stewart, D.R., et al.: The missing memristor found. Nature 453(7191), 80–83 (2008)

    Article  Google Scholar 

  3. Biolek, Z., Biolek, D., Biolkova, V.: SPICE model of memristor with nonlinear dopant drift. Radioengineering 18(2), 210–214 (2009)

    MATH  Google Scholar 

  4. Zhang, Y., Wang, X., Li, Y., et al.: Memristive model for synaptic circuits. IEEE Trans. Circ. Syst. II: Express Briefs 64(7), 767–771 (2017)

    Google Scholar 

  5. Guckert, L., Swartzlander, E.E.: MAD gatesmemristor logic design using driver circuitry. IEEE Trans. Circ. Syst. II: Express Briefs 64(2), 171–175 (2017)

    Google Scholar 

  6. Azghadi, M.R., Linares-Barranco, B., Abbott, D., et al.: A hybrid CMOS-memristor neuromorphic synapse. IEEE Trans. Biomed. Circ. Syst. 11(2), 434–445 (2017)

    Article  Google Scholar 

  7. Wen, S., Xie, X., Yan, Z., et al.: General memristor with applications in multilayer neural networks. Neural Netw. 103, 142–149 (2018)

    Article  Google Scholar 

  8. Kvatinsky, S., Ramadan, M., Friedman, E., Kolodny, A.: VTEAM-A general model for voltage controlled memristors. IEEE Trans. Circ. Syst. II: Express Briefs 62(8), 786–790 (2015)

    Google Scholar 

  9. Chen, L., Li, C., Huang, T., et al.: A synapse memristor model with forgetting effect. Phys. Lett. A 377(45–48), 3260–3265 (2013)

    Article  Google Scholar 

  10. Zhou, E., Fang, L., Liu, R., et al.: An improved memristor model for brain-inspired computing. Chin. Phys. B 26(11), 118502 (2017)

    Article  Google Scholar 

  11. Pershin, Y.V., Di Ventra, M.: Experimental demonstration of associative memory with memristive neural networks. Neural Netw. 23(7), 881–886 (2010)

    Article  Google Scholar 

  12. Bichler, O., Zhao, W., Alibart, F., et al.: Pavlov’s dog associative learning demonstrated on synaptic-like organic transistors. Neural Comput. 25(2), 549–566 (2013)

    Article  Google Scholar 

  13. Chen, L., Li, C., Wang, X., et al.: Associate learning and correcting in a memristive neural network. Neural Comput. Appl. 22(6), 1071–1076 (2013)

    Article  Google Scholar 

  14. Wang, L., Li, H., Duan, S., et al.: Pavlov associative memory in a memristive neural network and its circuit implementation. Neurocomputing 171, 23–29 (2016)

    Article  Google Scholar 

  15. Ma, D., Wang, G., Han, C., et al.: A memristive neural network model with associative memory for modeling affections. IEEE Access 6, 61614–61622 (2018)

    Article  Google Scholar 

  16. Liu, X., Zeng, Z., Wen, S.: Implementation of memristive neural network with full-function pavlov associative memory. IEEE Trans. Circ. Syst. I: Regul. Pap. 63(9), 1454–1463 (2016)

    MathSciNet  Google Scholar 

  17. Yang, L., Zeng, Z., Wen, S.: A full-function Pavlov associative memory implementation with memristance changing circuit. Neurocomputing 272, 513–519 (2018)

    Article  Google Scholar 

  18. Wang, Z., Wang, X.: A novel memristor-based circuit implementation of full-function Pavlov associative memory accorded with biological feature. IEEE Trans. Circ. Syst. I: Regul. Pap. 65(7), 2210–2220 (2018)

    Google Scholar 

  19. Hu, X., Duan, S., Chen, G., et al.: Modeling affections with memristor-based associative memory neural networks. Neurocomputing 223, 129–137 (2017)

    Article  Google Scholar 

Download references

Acknowledgments

The work was supported by the National Natural Science Foundation of China under Grant 61571372, 61672436, and 61601376, the Fundamental Science and Advanced Technology Research Foundation of Chongqing cstc2017jcyjBX0050 and cstc2016jcyjA0547, the Fundamental Research Funds for the Central Universities under Grant XDJK2017A005 and XDJK2016A001.

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Correspondence to Lidan Wang .

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Zhou, M., Wang, L., Duan, S. (2019). An Improved Memristor-Based Associative Memory Circuit for Full-Function Pavlov Experiment. In: Lu, H., Tang, H., Wang, Z. (eds) Advances in Neural Networks – ISNN 2019. ISNN 2019. Lecture Notes in Computer Science(), vol 11555. Springer, Cham. https://doi.org/10.1007/978-3-030-22808-8_60

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  • DOI: https://doi.org/10.1007/978-3-030-22808-8_60

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-22807-1

  • Online ISBN: 978-3-030-22808-8

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