记忆电阻器
跟踪(心理语言学)
计算机科学
神经形态工程学
人工智能
非线性系统
人工神经网络
尖峰神经网络
Spike(软件开发)
机器学习
电子工程
工程类
哲学
物理
软件工程
量子力学
语言学
作者
Deyu Wang,Jiawei Xu,Feng Li,Lianhao Zhang,Yuning Wang,Anders Lansner,Ahmed Hemani,Lirong Zheng,Zhuo Zou
标识
DOI:10.1109/aicas54282.2022.9870015
摘要
Recently, memristors have been widely used to implement Spiking Neural Networks (SNNs), which is promising in edge computing scenarios. However, most memristor-based SNN implementations adopt simplified spike-timing-dependent plasticity (STDP) for the online learning process. It is challenging for memristor-based implementations to support the trace-based STDP learning rules that have been widely used in neuromorphic applications. This paper proposed a versatile memristor-based architecture to implement the synaptic-level trace-based STDP learning rules. Especially, the similarity between synaptic trace dynamics and the memristor nonlinearity is explored and exploited to emulate the trace variables of trace-based STDP. As two typical trace-based STDP learning rules, the pairwise STDP and the triplet STDP, are simulated on two typical nonlinear bipolar memristor devices. The simulation results show that the behavior of physical memristor devices can be well estimated (below 6% in terms of the relative root-mean-square error), and the memristor-based in-circuit computation for trace-based STDP learning rules can achieve a high correlation coefficient over 98%.
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