记忆电阻器
神经形态工程学
计算机科学
突触重量
尖峰神经网络
突触
人工神经网络
记忆晶体管
Spike(软件开发)
人工智能
计算机体系结构
电子工程
电压
电阻随机存取存储器
神经科学
电气工程
工程类
生物
软件工程
作者
Miao Hu,Yiran Chen,J. Joshua Yang,Yu Wang,Hai Li
标识
DOI:10.1109/tcad.2016.2618866
摘要
Recent advances in memristor technology lead to the feasibility of large-scale neuromorphic systems by leveraging the similarity between memristor devices and synapses. For instance, memristor cross-point arrays can realize dense synapse network among hundreds of neuron circuits, which is not affordable for traditional implementations. However, little progress was made in synapse designs that support both static and dynamic synaptic properties. In addition, many neuron circuits require signals in specific pulse shape, limiting the scale of system implementation. Last but not least, a bottom-up study starting from realistic memristor devices is still missing in the current research of memristor-based neuromorphic systems. Here, we propose a memristor-based dynamic (MD) synapse design with experiment-calibrated memristor models. The structure obtains both static and dynamic synaptic properties by using one memristor for weight storage and the other as a selector. We overcame the device nonlinearities and demonstrated spike-timing-based recall, weight tunability, and spike-timing-based learning functions on MD synapse. Furthermore, a temporal pattern learning application was investigated to evaluate the use of MD synapses in spiking neural networks, under both spike-timing-dependent plasticity and remote supervised method learning rules.
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