Temporal Learning Using Second-Order Memristors

被引：26
|
作者：
Zidan, Mohammed A. [1 ]
Jeong, YeonJoo [1 ]
Lu, Wei D. [1 ]
机构：
[1] Univ Michigan, Dept Elect Engn & Comp Sci, Ann Arbor, MI 48109 USA
来源：
IEEE TRANSACTIONS ON NANOTECHNOLOGY | 2017年 / 16卷 / 04期
关键词：
Classification; feature; neural network; spike-timing dependent plasticity; SYNAPSE; NETWORK;
D O I：
10.1109/TNANO.2017.2710158
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
Utilizing internal dynamic processes in memristors may allow the devices to process temporal data natively. In this letter, we show the ability of second-order memristors to process information in the time domain, and discuss a memristive STDP network that can learn and classify temporal as well as classical data patterns.
引用
收藏
页码：721 / 723
页数：3
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