A Flexible Memristor-Based Neural Network

  • Junwei Sun
  • Gaoyong Han
  • Yanfeng WangEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 951)


Many memristor-based neural network arrays that have been proposed in recent years are simultaneously dealt with all of their signal inputs in signal reception status. Therefore, when a relatively small-scale neural network is implemented with this memristive array, some of the inputs which are not used may cause errors in the result due to the impact of an unexpected signal. In this paper, a flexible memristor-based neural network is proposed. Based on this network, the number of synapses used at work can be flexibly configured according to the required size, thereby improving system performance. The memristor-based neural network is simulated in Pspice to implement two different scales, which proves the feasibility and effectiveness of a flexible memristive neural network.


Memristor Neural network Circuit Flexible memristor array 



The work is supported by the State Key Program of National Natural Science of China (Grant No. 61632002), the National Key R&D Program of China for International S&T Cooperation Projects (No. 2017YFE010 3900), the National Natural Science of China (Grant Nos. 61603348, 61775198, 61603347, 61572446, 61472372), Science and Technology Innovation Talents Henan Province (Grant No. 174200510012), Research Program of Henan Province (Grant Nos. 172102210066, 17A120005, 182102210160), Youth Talent Lifting Project of Henan Province and the Science Foundation of for Doctorate Research of Zhengzhou University of Light Industry (Grant No. 2014BSJJ044).


  1. 1.
    Chua, L.O.: Memristor-the missing circuit element. IEEE Trans. Circ. Theory 18(5), 507–519 (1971)CrossRefGoogle Scholar
  2. 2.
    Chua, L.O., Kang, S.M.: Memristive devices and systems. Proc. IEEE 64(2), 209–223 (1976)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Strukov, D.B., Snider, G.S., Stewart, D.R., Williams, R.S.: The missing memristor found. Nature 453(7191), 80 (2008)CrossRefGoogle Scholar
  4. 4.
    Williams, R.S.: How we found the missing memristor. IEEE Spectrum 45(12), 28–35 (2008)CrossRefGoogle Scholar
  5. 5.
    Jo, S.H., Chang, T., Ebong, I., Bhadviya, B.B., Mazumder, P., Lu, W.: Nanoscale memristor device as synapse in neuromorphic systems. Nano Lett. 10(4), 1297–1301 (2010)CrossRefGoogle Scholar
  6. 6.
    Kim, H., Sah, M.P., Yang, C., Roska, T., Chua, L.O.: Neural synaptic weighting with a pulse-based memristor circuit. IEEE Trans. Circ. Syst. I: Regul. Pap. 59(1), 148–158 (2012)MathSciNetGoogle Scholar
  7. 7.
    Liu, B., Chen, Y., Wysocki, B., Huang, T.: Reconfigurable neuromorphic computing system with memristor-based synapse design. Neural Process. Lett. 41(2), 159–167 (2015)CrossRefGoogle Scholar
  8. 8.
    Indiveri, G., Linares, B.B., Legenstein, R., Deligeorgis, G., Prodromakis, T.: Integration of nanoscale memristor synapses in neuromorphic computing architectures. Nanotechnology 24(38), 384010 (2013)CrossRefGoogle Scholar
  9. 9.
    Kim, H., Sah, M.P., Yang, C., Roska, T., Chua, L.O.: Memristor bridge synapses. Proc. IEEE 100(6), 2061–2070 (2012)CrossRefGoogle Scholar
  10. 10.
    Sah, M.P., Yang, C., Kim, H., Chua, L.O.: A voltage mode memristor bridge synaptic circuit with memristor emulators. Sensors 12(3), 3587–3604 (2012)CrossRefGoogle Scholar
  11. 11.
    Azghadi, M.R., Linares, B.B., Abbott, D., Leong, P.H.: A hybrid cmos-memristor neuromorphic synapse. IEEE Trans. Biomed. Circ. Syst. 11(2), 434–445 (2017)CrossRefGoogle Scholar
  12. 12.
    Adhikari, S.P., Yang, C., Kim, H., Chua, L.O.: Memristor bridge synapse-based neural network and its learning. IEEE Trans. Neural Netw. Learn. Syst. 23(9), 1426–1435 (2012)CrossRefGoogle Scholar
  13. 13.
    Ebong, I.E., Mazumder, P.: CMOS and memristor-based neural network design for position detection. Proc. IEEE 100(6), 2050–2060 (2012)CrossRefGoogle Scholar
  14. 14.
    Duan, S., Hu, X., Dong, Z., Wang, L., Mazumder, P.: Memristor-based cellular nonlinear/neural network: design, analysis, and applications. IEEE Trans. Neural Netw. Learn. Syst. 26(6), 1202–1213 (2015)MathSciNetCrossRefGoogle Scholar
  15. 15.
    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
  16. 16.
    Sheridan, P.M., Cai, F., Du, C., Ma, W., Zhang, Z., Lu, W.D.: Sparse coding with memristor networks. Nat. Nanotechnol. 12(8), 784 (2017)CrossRefGoogle Scholar
  17. 17.
    Wen, S., Huang, T., Zeng, Z., Chen, Y., Li, P.: Circuit design and exponential stabilization of memristive neural networks. Neural Netw. 63, 48–56 (2015)CrossRefGoogle Scholar
  18. 18.
    Yang, J., Wang, L., Wang, Y., Guo, T.: A novel memristive Hopfield neural network with application in associative memory. Neurocomputing 227, 142–148 (2017)CrossRefGoogle Scholar
  19. 19.
    Adam, G.C., Hoskins, B.D., Prezioso, M., Merrikh, B.F., Chakrabarti, B., Strukov, D.B.: 3-D memristor crossbars for analog and neuromorphic computing applications. IEEE Trans. Electron Devices 64(1), 312–318 (2017)CrossRefGoogle Scholar
  20. 20.
    Prezioso, M., Merrikh, B.F., Hoskins, B.D., Adam, G.C., Likharev, K.K., Strukov, D.B.: Training and operation of an integrated neuromorphic network based on metal-oxide memristors. Nature 521(7550), 61 (2015)CrossRefGoogle Scholar
  21. 21.
    Hu, S.G., et al.: Associative memory realized by a reconfigurable memristive Hopfield neural network. Nature Commun. 6, 7522 (2015)CrossRefGoogle Scholar
  22. 22.
    Li, C., et al.: Efficient and self-adaptive in-situ learning in multilayer memristor neural networks. Nature Commun. 9(1), 2385 (2018)CrossRefGoogle Scholar
  23. 23.
    Wang, Z., et al.: Fully memristive neural networks for pattern classification with unsupervised learning. Nat. Electr. 1(2), 137 (2018)CrossRefGoogle Scholar
  24. 24.
    Wang, J.J., et al.: Predicting house price with a memristor-based artificial neural network. IEEE Access 6, 16523–16528 (2018)CrossRefGoogle Scholar
  25. 25.
    Yao, P., et al.: Face classification using electronic synapses. Nature Commun. 8, 15199 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Henan Key Lab of Information-Based Electrical AppliancesZhengzhou University of Light IndustryZhengzhouChina
  2. 2.School of Electrical and Information EngineeringZhengzhou University of Light IndustryZhengzhouChina

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