文章基本信息

标题：Enabling an Integrated Rate-temporal Learning Scheme on Memristor
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作者：Wei He ; Kejie Huang ; Ning Ning 等
期刊名称：Scientific Reports
电子版ISSN：2045-2322
出版年度：2015
卷号：4
期号：1
DOI：10.1038/srep04755
语种：English
出版社：Springer Nature
摘要：Learning scheme is the key to the utilization of spike-based computation and the emulation of neural/synaptic behaviors toward realization of cognition. The biological observations reveal an integrated spike time- and spike rate-dependent plasticity as a function of presynaptic firing frequency. However, this integrated rate-temporal learning scheme has not been realized on any nano devices. In this paper, such scheme is successfully demonstrated on a memristor. Great robustness against the spiking rate fluctuation is achieved by waveform engineering with the aid of good analog properties exhibited by the iron oxide-based memristor. The spike-time-dependence plasticity (STDP) occurs at moderate presynaptic firing frequencies and spike-rate-dependence plasticity (SRDP) dominates other regions. This demonstration provides a novel approach in neural coding implementation, which facilitates the development of bio-inspired computing systems.