Bottleneck of using a single memristive device as a synapse

被引：6
|
作者：
Bayat, Farnood Merrikh [1 ]
Shouraki, Saeed Bagheri [2 ]
Afrakoti, Iman Esmaili Paeen [2 ]
机构：
[1] Univ Calif Santa Barbara, Dept Elect & Comp Engn, Santa Barbara, CA 93106 USA
[2] Sharif Univ Technol, Dept Elect Engn, Tehran, Iran
来源：
NEUROCOMPUTING | 2013年 / 115卷
关键词：
Memristive device; Synapse Hebbian learning; Spike Timing-Dependent Plasticity; Neuromorphic systems;
D O I：
10.1016/j.neucom.2012.12.027
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this study we will show that the variation rate of the memristance of the memristive device depends completely on its current memristance which means that it can change significantly with time during the learning phase. This phenomenon can degrade the performance of learning methods like Spike Timing-Dependent Plasticity (STDP) and cause the corresponding neuromorphic systems to become unstable. (C) 2013 Elsevier B.V. All rights reserved.
引用
收藏
页码：166 / 168
页数：3
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