Detecting Correlations Using Phase-Change Neurons and Synapses

被引：55

作者：

Tuma, Tomas ^{[1
]}

Le Gallo, Manuel ^{[1
,2
,3
]}

Sebastian, Abu ^{[1
]}

Eleftheriou, Evangelos ^{[1
]}

机构：

[1] IBM Res Zurich, CH-8803 Ruschlikon, Switzerland

[2] Univ Zurich, Inst Neuroinformat, CH-8092 Zurich, Switzerland

[3] ETH, CH-8092 Zurich, Switzerland

来源：

IEEE ELECTRON DEVICE LETTERS | 2016年 / 37卷 / 09期

关键词：

Neuromorphic computing; spiking neural networks; phase-change materials; correlation detection; CHANGE MEMORY; PLASTICITY; NETWORK;

D O I：

10.1109/LED.2016.2591181

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：

As the conventional von Neumann-based computational architectures reach their scalability and performance limits, alternative computational frameworks inspired by biological neuronal networks hold promise to revolutionize the way we process information. Here, we present a bioinspired computational primitive that utilizes an artificial spiking neuron equipped with plastic synapses to detect temporal correlations in data streams in an unsupervised manner. We demonstrate that the internal states of the neuron and of the synapses can be efficiently stored in nanoscale phase-change memory devices and show computations with collocated storage in an experimental setting.

引用

页码：1238 / 1241

页数：4

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