STDP Learning Rule Based on Memristor with STDP Property

被引:0
|
作者
Chen, Ling [1 ]
Li, Chuandong [1 ]
Huang, Tingwen [3 ]
He, Xing [1 ]
Li, Hai [2 ]
Chen, Yiran [2 ]
机构
[1] Chongqing Univ, Coll Comp, Chongqing 400044, Peoples R China
[2] Univ Pittsburgh, Elect & Comp Engn, Pittsburgh, PA 15261 USA
[3] Texas A&M Univ Qatar, Doha, Qatar
来源
PROCEEDINGS OF THE 2014 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) | 2014年
关键词
PLASTICITY; MODEL;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Spike-timing-dependent plasticity (STDP) learning ability has been observed in physical memristors, but whether the STDP is caused by the neuron or the memristor is unclear. In this paper, we proved the STDP property in the model for both symmetric and asymmetric memristor. We also employed the symmetric/asymmetric memristors with STDP property and the simplified neurons to perform the STDP learning ability. At last, the sequence learning experiment of the memritive neural network (MNN) with the symmetric memristor synapse further verifies the STDP learning ability of the memristor.
引用
收藏
页码:1 / 6
页数:6
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