Skip to main content
Log in

Experimental verification of a memristive neural network

  • Original Paper
  • Published:
Nonlinear Dynamics Aims and scope Submit manuscript

Abstract

This paper presents an electronic circuit able to emulate the behavior of a neural network based on memristive synapses. The latter is built with two flux-controlled floating memristor emulator circuits operating at high frequency and two passive resistors. Synapses are connected in a way that a bridge circuit is obtained, and its dynamical behavioral model is derived from characterizing memristive synapses. Analysis of the memristor characteristics for obtaining a suitable synaptic response is also described. A neural network of one neuron and two inputs is connected using the proposed topology, where synaptic positive and negative weights can easily be reconfigured. The behavior of the proposed artificial neural network based on memristors is verified through MATLAB, HSPICE simulations and experimental results. Synaptic multiplication is performed with positive and negative weights, and its behavior is also demonstrated through experimental results getting 6% of error.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

Notes

  1. In this work, the minimum pulse width is 200 ns, which is governed by the st of the AD734 multiplier.

References

  1. Parberry, I.: Circuit Complexity and Neural Networks. MIT Press, Cambridge (1994). ISBN 0-262-16148-6

    MATH  Google Scholar 

  2. Adamatzky, A., Chua, L.O.: Memristor Networks. Springer, Switzerland (2014). ISBN 978-3-319-02629-9

    Book  MATH  Google Scholar 

  3. Hodgkin, A.L., Huxley, A.F.: A quantitative description of membrane current and its application to conduction and excitation in nerve. J. Phys. 117(4), 500–544 (1952)

    Google Scholar 

  4. Wang, Y., Ma, J., Xu, Y., Wu, F., Zhou, P.: The electrical activity of neurons subject to electromagnetic induction and gaussian white noise. Int. J. Bifurc. Chaos 27(02), 1750030 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  5. Wu, F., Wang, C., Jin, W., Ma, J.: Dynamical responses in a new neuron model subjected to electromagnetic induction and phase noise. Phys. A 469(1), 81–88 (2017)

    Article  MathSciNet  Google Scholar 

  6. Ma, J., Mi, L., Zhou, P., Xu, Y., Hayat, T.: Phase synchronization between two neurons induced by coupling of electromagnetic field. Appl. Math. Comput. 307, 321–328 (2017)

    MathSciNet  Google Scholar 

  7. Bostrom, N.: Superintelligence: Paths, Dangers, Strategies. Oxford University Press, Oxford (2014). ISBN 9780199678112

    Google Scholar 

  8. Wang, L., Li, L., Duan, D., Huang, T., Wang, H.: Pavlov associative memory in a memristive neural network and its circuit implementation. Neurocomputing 171(1), 23–29 (2016)

    Article  Google Scholar 

  9. Pershin, Y.V., Di Ventra, M.: Experimental demonstration of associative memory with memristive neural networks. Neural Netw. 23(7), 88–886 (2010)

    Article  Google Scholar 

  10. Yang, J., Wang, L., Wang, Y., Guo, T.: A novel memristive Hopfield neural network with application in associative memory. Neurocomputing 227, 142–148 (2017)

    Article  Google Scholar 

  11. 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)

    Article  Google Scholar 

  12. Pavlov, I.P.: Conditioned reflexes: an investigation of the physiological activity of the cerebral cortex. A. Neurosci. 17(3), 136–141 (2010)

    Google Scholar 

  13. Chechik, G., Meilijson, I., Ruppin, E.: Effective learning requires neuronal remodeling of Hebbian synapses. Proc. Adv. Neural Inf. Process. Syst. 12(1), 1–7 (1999)

    Google Scholar 

  14. Strukov, D.B., Sneider, G.S., Stewart, D.R., Williams, R.S.: The missing memristor found. Nature 453(1), 80–83 (2008)

    Article  Google Scholar 

  15. Zhang, Y., Wang, X., Li, Y., Friedman, E.G.: Memristive model for synaptic circuits. IEEE Trans. Circuits Syst. II: Express Briefs 64(7), 767–771 (2016)

    Article  Google Scholar 

  16. Luo, L., Hu, X., Duan, S., Dong, Z., Wang, L.: Multiple memristor series-parallel connections with use in synaptic circuit design. IET Circuits Dev. Syst. 11(2), 123–134 (2017)

    Article  Google Scholar 

  17. Adhikari, S.P., Yang, C., Kim, H., Chua, L.O.: Memristor bridge synapse-based neural network and its learning. IEEE Trans. Neural Net. Learn. Syst. 23(9), 1426–1435 (2012)

    Article  Google Scholar 

  18. Kim, H., Sah, M.P., Yang, C., Roska, T., Chua, L.O.: Neural synaptic weighting with a pulse-based memristor circuit. IEEE Trans. Circuits Syst. I: Reg. Pap. 59(1), 148–158 (2012)

    Article  MathSciNet  Google Scholar 

  19. Wang, L., Wang, X., Duan, S., Li, H.: A spintronic memristor bridge synapse circuit and the application in memrisitive cellular automata. NeuroComputing 167, 346–351 (2015)

    Article  Google Scholar 

  20. www.bioinspired.net

  21. Low Cost Analog Multiplier, AD633, REV. B, Analog Devices (1999)

  22. Sánchez-López, C., Mendoza-López, J., Carrasco-Aguilar, M.A., Muñiz-Montero, C.: A floating analog memristor emulator circuit. IEEE Trans. Circuits Syst. II: Express. Briefs 61(5), 309–313 (2014)

    Google Scholar 

  23. Sánchez-López, C., Carrasco-Aguilar, M.A., Muñiz-Montero, C.: A 16 Hz–160 kHz memristor emulator circuit. Int. J. Electron Commun. 69(9), 1208–1219 (2015)

    Article  Google Scholar 

  24. Sánchez-López, C., Aguila-Cuapio, L.E.: A 860 kHz grounded memristor emulator circuit. Int. J. Electron Commun. 73, 11 (2017)

    Article  Google Scholar 

  25. 10 MHz Four-Quadrant Multiplier/Divider: AD734, REV. E, Analog Devices (2011)

  26. Carro-Pérez, I., González-Hernández, H.G., Sánchez-López, C.: High-frequency memristive synapses. Proc. IEEE Int. Conf. Latin American Symp. Circuits Syst. 1(1), 1–4 (2017). https://doi.org/10.1109/LASCAS.2017.7948077

  27. Hasler, P., Diorio, C., Minch, B.A., Mead, C.: Single transistor learning synapse with long term storage. IEEE Int. Symp. Circ. Syst. 1–4 (1995)

  28. Data Sheet AD844AN: www.analog.com

  29. Very High Speed, High Output Current, Voltage Feedback Amplifier, LM7171, Texas Instruments (2014)

  30. Blackwell, G.R.: The Electronic Packaging Handbook. CRC Press, Boca Raton (1999). ISBN 9780849385919

    Book  Google Scholar 

Download references

Acknowledgements

This work was supported in part by the National Council for Science and Technology (CONACyT), Mexico, under Grant 222843; in part by the Universidad Autónoma de Tlaxcala (UATx), Tlaxcala de Xicohtencatl, TL, Mexico, under Grant CACyPI-UATx-2017; and in part by the Program to Strengthen Quality in Educational Institutions, under Grant C/PFCE-2016-29MSU0013Y-07-23.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to C. Sánchez-López.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Carro-Pérez, I., Sánchez-López, C. & González-Hernández, H.G. Experimental verification of a memristive neural network. Nonlinear Dyn 93, 1823–1840 (2018). https://doi.org/10.1007/s11071-018-4291-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11071-018-4291-1

Keywords

Navigation