Digital Implementation of a Spiking Neural Network (SNN) Capable of Spike-Timing-Dependent Plasticity (STDP) Learning

被引：0

作者：

Ru, Di ^{[1
]}

Zhang, Xu ^{[2
]}

Xu, Ziye ^{[2
]}

Ferrari, Silvia ^{[2
]}

Mazumder, Pinaki ^{[1
]}

机构：

[1] Univ Michigan, Ann Arbor, MI 48109 USA

[2] Duke Univ, Durham, NC 27708 USA

来源：

2014 IEEE 14TH INTERNATIONAL CONFERENCE ON NANOTECHNOLOGY (IEEE-NANO) | 2014年

基金：

美国国家科学基金会;

关键词：

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

The neural network model of computation has been proven to be faster and more energy-efficient than Boolean CMOS computations in numerous real-world applications. As a result, neuromorphic circuits have been garnering growing interest as the integration complexity within chips has reached several billion transistors. This article presents a digital implementation of a re-scalable spiking neural network (SNN) to demonstrate how spike timing-dependent plasticity (STDP) learning can be employed to train a virtual insect to navigate through a terrain with obstacles by processing information from the environment.

引用

页码：873 / 876

页数：4

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