一种脉冲神经元监督学习的直接计算方法

doi:10.12263/DZXB.20190350

电子学报 ›› 2021, Vol. 49 ›› Issue (2): 331-337.DOI: 10.12263/DZXB.20190350

一种脉冲神经元监督学习的直接计算方法

陈国军¹, 蔺想红², 王国恩¹, 王向文²

1. 武汉大学城市设计学院, 湖北武汉 430072;
2. 西北师范大学计算机科学与工程学院, 甘肃兰州 730070

收稿日期:2019-04-02 修回日期:2020-04-27 出版日期:2021-02-25 发布日期:2021-02-25
通讯作者: 王国恩
作者简介:陈国军 男,1991年7月出生,山东德州人.2016年毕业于西北师范大学计算机科学与工程学院,获软件工程硕士学位.现为武汉大学城市设计学院博士生.研究方向为神经网络、智慧城市与城市规划.E-mail:guojunchen@whu.edu.cn;蔺想红 男,1976年1月出生,甘肃天水人.2009年获哈尔滨工业大学计算机应用技术专业博士学位.现任西北师范大学计算机科学与工程学院教授,硕士生导师.研究方向为神经网络、深度学习、智能软件.E-mail:linxh@nwnu.edu.cn;王向文 男,1991年3月出生,甘肃天水人.2015年毕业于西北师范大学,获得硕士学位.现任西北师范大学计算机科学与工程学院教师.研究方向为神经网络、机器学习与生物信息学.E-mail:wangxiangwen2@163.com
基金资助:
国家自然科学基金（No.51878516）

A Direct Computation Method of Supervised Learning for Spiking Neurons

CHEN Guo-jun¹, LIN Xiang-hong², WANG Guo-en¹, WANG Xiang-wen²

1. School of Urban Design, Wuhan University, Wuhan, Hubei 430072, China;
2. Computer Science and Engineering, Northwest Normal University, Lanzhou, Gansu 730070, China

Received:2019-04-02 Revised:2020-04-27 Online:2021-02-25 Published:2021-02-25

摘要/Abstract

摘要： 精确脉冲定时作为一种神经元信息编码方式更具生物可解释性，使用精确脉冲定时编码的脉冲神经元具有更为强大的时空信号处理能力.脉冲神经元监督学习是神经计算的重要方面，目的是使神经元对给定输入脉冲在期望时刻发放脉冲.通过分析输入脉冲序列、期望输出脉冲序列与实际输出脉冲序列的关系，发现已有脉冲神经元监督学习算法的脉冲选择与计算较为复杂，致使不能达到理想学习效果.通过去除影响整体学习效果的多余脉冲计算，构建用于脉冲神经元突触权值调整的双脉冲单元，提出了一种适用于脉冲神经元监督学习的直接计算方法.该方法基于输入脉冲，使用期望输出脉冲与实际输出脉冲的时序关系，直接计算突触权值的调整量；每个输入脉冲在每次迭代中最多计算一次，有效减少了脉冲计算次数.实验结果表明，直接计算方法作为脉冲神经元监督学习的一般性脉冲计算优化策略，可以大幅提高已有算法的学习准确率.

关键词: 监督学习, 脉冲神经元, 脉冲神经网络, 脉冲序列, 直接计算

Abstract: The purpose of supervised learning for spiking neurons is to emit spikes at desired times.By analyzing the relationship between desired output spike train,actual output spike train,and input spike trains,we argue that the spike selection and calculation of existing algorithms are much complex.By removing the repetitive spike computations which have a negative effect on the overall learning performance,we construct a unit of pair-spike and propose a direct computation method of supervised learning algorithms for spiking neurons.Based on input spikes and the relationship between desired output spike train and actual output spike train,the proposed method utilities every useful input spike selected only once to directly adjust the synaptic weights,which truly reduces the computational cost.Experimental results show that the direct computation method,as a general optimization strategy of supervised learning for spiking neurons,can effectively improve the learning performance of existing algorithms.

Key words: supervised learning, spiking neuron, spiking neural networks, spike train, direct computation

中图分类号:

TP183

陈国军, 蔺想红, 王国恩, 王向文. 一种脉冲神经元监督学习的直接计算方法[J]. 电子学报, 2021, 49(2): 331-337.

CHEN Guo-jun, LIN Xiang-hong, WANG Guo-en, WANG Xiang-wen. A Direct Computation Method of Supervised Learning for Spiking Neurons[J]. Acta Electronica Sinica, 2021, 49(2): 331-337.

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一种脉冲神经元监督学习的直接计算方法

A Direct Computation Method of Supervised Learning for Spiking Neurons

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