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

页码：26 / 29

页数：4

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