Silicon spiking neurons for hardware implementation of extreme learning machines

被引：58

作者：

Basu, Arindam ^{[1
]}

Shuo, Sun ^{[1
]}

Zhou, Hongming ^{[1
]}

Lim, Meng Hiot ^{[1
]}

Huang, Guang-Bin ^{[1
]}

机构：

[1] Nanyang Technol Univ, Sch Elect & Elect Engn, Nanyang Ave, Singapore 639798, Singapore

来源：

NEUROCOMPUTING | 2013年 / 102卷

关键词：

Spiking neural network; Extreme learning machine; Asynchronous communication; Silicon neuron; Neuromorphic; MODEL;

D O I：

10.1016/j.neucom.2012.01.042

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, we propose a silicon implementation of extreme learning machines (ELM) using spiking neural circuits. The major components of a silicon spiking neural network, neuron, synapse and 'Address Event Representation' (AER) for asynchronous spike based communication, are described. The benefits of using this hardware to implement an ELM as opposed to other single layer feedforward networks (SLFN) are explained. Several possible architectures for efficient implementation of ELM using these circuits are presented and their possible impact on ELM performance is discussed. (C) 2012 Elsevier B.V. All rights reserved.

引用

页码：125 / 134

页数：10

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