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	Information transfer characteristic in memristic neuromorphic network
	Ren, Quansheng ; Long, Qiufeng ; Zhang, Zhiqiang ; Zhao, Jianye
	2013
英文摘要	Memristive nanodevices can support exactly the same learning function as spike-timing-dependent plasticity in neuroscience, and thus the exploration for the evolution and self-organized computing of memristor-based neuromorphic networks becomes reality. We mainly study the STDP-driven refinement effect on memristor-based crossbar structure and its information transfer characteristic. The results show that self-organized refinement could enhance the information transfer of memristor crossbar, and the dependence of memristive device on current direction and the balance between potentiation and depression are of crucial importance. This gives an inspiration for resolving the power consumption issue and the so called sneak path problem. ? 2013 Springer-Verlag Berlin Heidelberg.; EI; 0
语种	英语
DOI标识	10.1007/978-3-642-39065-4-1
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/294567]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Ren, Quansheng,Long, Qiufeng,Zhang, Zhiqiang,et al. Information transfer characteristic in memristic neuromorphic network. 2013-01-01.

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