An Approximate Backpropagation Learning Rule for Memristor Based Neural Networks Using Synaptic Plasticity

Autor:	Negrov, D. V., Karandashev, I. M., Shakirov, V. V., Matveyev, Yu. A., Dunin-Barkowski, W. L., Zenkevich, A. V.
Rok vydání:	2015
Předmět:	Computer Science - Neural and Evolutionary Computing Computer Science - Emerging Technologies
Druh dokumentu:	Working Paper
Popis:	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 multiple positive or negative updates of the synaptic weight, and to use the min operation instead of the product of the two signals. In computational simulations, we show that the proposed approximation to backpropagation is well converged and may be suitable for memristor implementations of multilayer neural networks. Comment: 21 pages, 6 figures, 1 table, title changed, manuscript thoroughly rewritten
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1511.07076 Zobrazit plný text záznamu View this record from Arxiv