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Nonlinear Dynamics

, Volume 93, Issue 4, pp 1823–1840 | Cite as

Experimental verification of a memristive neural network

  • I. Carro-Pérez
  • C. Sánchez-López
  • H. G. González-Hernández
Original Paper

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.

Keywords

Neural network Memristor Synapse Pinched hysteresis loop Current conveyor 

Notes

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.

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Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of MechatronicsTecnológico de MonterreyMonterreyMexico
  2. 2.Department of ElectronicsAutonomous University of TlaxcalaApizacoMexico

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