Li Yi, Zhong Yingpeng, Zhang Jinjian, Xu Lei, Wang Qing, Sun Huajun, Tong Hao, Cheng Xiaoming, Miao Xiangshui
1] Wuhan National Laboratory for Optoelectronics (WNLO), Huazhong University of Science and Technology (HUST), Wuhan 430074, China [2] School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, China [3].
1] Wuhan National Laboratory for Optoelectronics (WNLO), Huazhong University of Science and Technology (HUST), Wuhan 430074, China [2] School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, China.
Sci Rep. 2014 May 9;4:4906. doi: 10.1038/srep04906.
Nanoscale inorganic electronic synapses or synaptic devices, which are capable of emulating the functions of biological synapses of brain neuronal systems, are regarded as the basic building blocks for beyond-Von Neumann computing architecture, combining information storage and processing. Here, we demonstrate a Ag/AgInSbTe/Ag structure for chalcogenide memristor-based electronic synapses. The memristive characteristics with reproducible gradual resistance tuning are utilised to mimic the activity-dependent synaptic plasticity that serves as the basis of memory and learning. Bidirectional long-term Hebbian plasticity modulation is implemented by the coactivity of pre- and postsynaptic spikes, and the sign and degree are affected by assorted factors including the temporal difference, spike rate and voltage. Moreover, synaptic saturation is observed to be an adjustment of Hebbian rules to stabilise the growth of synaptic weights. Our results may contribute to the development of highly functional plastic electronic synapses and the further construction of next-generation parallel neuromorphic computing architecture.
纳米级无机电子突触或突触器件能够模拟大脑神经元系统生物突触的功能,被视为超越冯·诺依曼计算架构的基本构建模块,可实现信息存储与处理。在此,我们展示了一种基于硫族化物忆阻器的电子突触的Ag/AgInSbTe/Ag结构。利用具有可重复的逐渐电阻调节的忆阻特性来模拟作为记忆和学习基础的活动依赖型突触可塑性。通过突触前和突触后尖峰的共同活动实现双向长期赫布可塑性调制,其符号和程度受包括时间差、尖峰率和电压等各种因素影响。此外,观察到突触饱和是对赫布规则的一种调整,以稳定突触权重的增长。我们的结果可能有助于开发高功能塑性电子突触以及进一步构建下一代并行神经形态计算架构。