Experimental verification of a memristive neural network
- 119 Downloads
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.
KeywordsNeural network Memristor Synapse Pinched hysteresis loop Current conveyor
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.
- 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
- 7.Bostrom, N.: Superintelligence: Paths, Dangers, Strategies. Oxford University Press, Oxford (2014). ISBN 9780199678112Google 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
- 21.Low Cost Analog Multiplier, AD633, REV. B, Analog Devices (1999)Google Scholar
- 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
- 25.10 MHz Four-Quadrant Multiplier/Divider: AD734, REV. E, Analog Devices (2011)Google Scholar
- 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)Google Scholar
- 28.Data Sheet AD844AN: www.analog.com
- 29.Very High Speed, High Output Current, Voltage Feedback Amplifier, LM7171, Texas Instruments (2014)Google Scholar