Fast Learning in Spiking Neural Networks by Learning Rate Adaptation<sup>*</sup>

方慧娟; 罗继亮; 王飞

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Fast Learning in Spiking Neural Networks by Learning Rate Adaptation^*

Updated：2026-01-06

- Fast Learning in Spiking Neural Networks by Learning Rate Adaptation^*
- Fast Learning in Spiking Neural Networks by Learning Rate Adaptation^*
- 中国化学工程学报（英文版） 2012年20卷第6期页码：1219-1224
- Affiliations：
  
  College of Information Science and Engineering, Huaqiao University,Xiamen,China,361021
- Author bio：
- Funds：
  
  Supported by the National Natural Science Foundation of China (60904018;61203040);the Natural Science Foundation of Fujian Province of China (2009J05147;2011J01352);the Foundation for Distinguished Young Scholars of Higher Education of Fujian Province of China (JA10004);the Science Research Foundation of Huaqiao University (09BS617)
- DOI：
  中图分类号：
- 纸质出版：2012
- Accepted：
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方慧娟, 罗继亮, 王飞. Fast Learning in Spiking Neural Networks by Learning Rate Adaptation^*[J]. 中国化学工程学报（英文版）, 2012,20(6):1219-1224.

[J]. Chinese Journal of Chemical Engineering, 2012, 20(6): 1219-1224.
方慧娟, 罗继亮, 王飞. Fast Learning in Spiking Neural Networks by Learning Rate Adaptation^*[J]. 中国化学工程学报（英文版）, 2012,20(6):1219-1224. DOI：

[J]. Chinese Journal of Chemical Engineering, 2012, 20(6): 1219-1224. DOI：

摘要

Abstract

For accelerating the supervised learning by the SpikeProp algorithm with the temporal coding paradigm in spiking neural networks (SNNs)

three learning rate adaptation methods (heuristic rule

delta-delta rule

and delta-bar-delta rule)

which are used to speed up training in artificial neural networks

are used to develop the training algorithms for feedforward SNN.The performance of these algorithms is investigated by four experiments: classical XOR (exclusive or) problem

Iris dataset

fault diagnosis in the Tennessee Eastman process

and Poisson trains of discrete spikes.The results demonstrate that all the three learning rate adaptation methods are able to speed up convergence of SNN compared with the original SpikeProp algorithm.Furthermore

if the adaptive learning rate is used in combination with the momentum term

the two modifications will balance each other in a beneficial way to accomplish rapid and steady convergence.In the three learning rate adaptation methods

delta-bar-delta rule performs the best.The delta-bar-delta method with momentum has the fastest convergence rate

the greatest stability of training process

and the maximum accuracy of network learning.The proposed algorithms in this paper are simple and efficient

and consequently valuable for practical applications of SNN.

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