计算机工程 ›› 2017, Vol. 43 ›› Issue (12): 197-202.doi: 10.3969/j.issn.1000-3428.2017.12.036

• 人工智能及识别技术 • 上一篇    下一篇

基于脉冲序列合成核的脉冲神经元在线监督学习算法

蔺想红,李丹,王向文,张宁   

  1. (西北师范大学 计算机科学与工程学院,兰州 730070)
  • 收稿日期:2016-09-18 出版日期:2017-12-15 发布日期:2017-12-15
  • 作者简介:国家自然科学基金(61165002);甘肃省自然科学基金(1506RJZA127);甘肃省高等学校科研项目(2015A-013)。
  • 基金项目:
    国家自然科学基金(61165002);甘肃省自然科学基金(1506RJZA127);甘肃省高等学校科研项目(2015A-013)。

Online Supervised Learning Algorithm for Spiking Neuron Based on Spiking Sequence Composite Kernel

LIN Xianghong,LI Dan,WANG Xiangwen,ZHANG Ning   

  1. (School of Computer Science and Engineering,Northwest Normal University,Lanzhou 730070,China)
  • Received:2016-09-18 Online:2017-12-15 Published:2017-12-15

摘要: 脉冲神经网络使用时间编码的方式进行数据处理,是进行复杂时空信息处理的有效工具。为此,将多脉冲序列合成核引入脉冲序列处理过程,提出一种在线监督学习算法,采用累加和累积合成核机制进行实验学习,并与基于单一核函数的在线PSD算法进行比较。实验结果表明,该算法具有较好的学习性能,特别在数据样本较大时优势更为突出。同时结果也表明,通过多个核函数的组合可以获得更稳定高效的脉冲序列合成核表示。

关键词: 脉冲神经元, 在线学习, 多脉冲序列核, 卷积, 监督学习

Abstract: Spiking neural network uses temporal coding for data processing,which is an effective tool for complex spatial and temporal information processing.In view of this,this paper applies multiple sequence composite kernel into the spiking sequence processing and proposes an online supervised learning algorithm.It uses accumulation and cumulative mechanisms for supervised learning and makes an experiment compared with online PSD algorithm based on single kernel.Experimental results show the better performance of the proposed algorithm.Especially in the performance of larger data sample,it can maintain this excellent performance more significantly.The results also show that the combination of multiple kernel functions can get more stable and efficient spiking sequence composite kernel representation.

Key words: spiking neuron, online learning, multiple spiking sequence kernel, convolution, supervised learning

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