R(t)-based Spike-Timing-Dependent Plasticity in Memristive Neural Networks

被引：0
|
作者：
Afrin, Farhana [1 ]
Cantley, Kurtis D. [1 ]
机构：
[1] Boise State Univ, Dept Elect & Comp Engn, Boise, ID 83725 USA
来源：
2023 IEEE WORKSHOP ON MICROELECTRONICS AND ELECTRON DEVICES, WMED | 2023年
基金：
美国国家科学基金会;
关键词：
Spike-Timing-Dependent Plasticity; R(t) element; memristor; Spiking Neural Network; spike triplet learning; TRIPLET-BASED STDP; MODEL; SYNAPSE; PAIR;
D O I：
10.1109/WMED58543.2023.10097441
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
Inspired by the human brain, neuromorphic computation should be extremely efficient at very large scales due to inherent parallelism, scalability, and fault and failure tolerance. Spike-Timing-Dependent Plasticity (STDP) is one of the most biologically plausible synaptic learning behaviors. The proposed generic model of time-varying resistance, or R(t) elements in this work can produce STDP in electronic spiking neural networks with memristive synapses that is very similar to that observed in biology. Both pair-based and triplet-based STDP is verified with the proposed generic R(t) model.
引用
收藏
页码：26 / 29
页数：4
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