An approximate backpropagation learning rule for memristor based neural networks using synaptic plasticity

We describe an approximation to backpropagation algorithm for training deep neural networks, which is designed to work with synapses implemented with memristors. The key idea is to represent the values of both the input signal and the backpropagated delta value with a series of pulses that trigger m...

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Bibliographic Details

Published in:	Neurocomputing (Amsterdam) Vol. 237; pp. 193 - 199
Main Authors:	Negrov, D., Karandashev, I., Shakirov, V., Matveyev, Yu, Dunin-Barkowski, W., Zenkevich, A.
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 10.05.2017
Subjects:	Backpropagation algorithm Deep learning Hardware design Memristor Neural networks Deep learning Memristor Backpropagation algorithm Neural networks Hardware design
ISSN:	0925-2312, 1872-8286
Online Access:	Get full text
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