基于深度学习网络的神经元自适应投影分类方法

doi:10.3969/j.issn.0372-2112.2020.07.010

电子学报 ›› 2020, Vol. 48 ›› Issue (7): 1321-1329.DOI: 10.3969/j.issn.0372-2112.2020.07.010

基于深度学习网络的神经元自适应投影分类方法

蔺想红, 郑鉴洋, 王向文, 马慧芳

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:2019-01-11 修回日期:2019-12-15 出版日期:2020-07-25 发布日期:2020-04-02
作者简介:蔺想红 男,1976年1月生于甘肃天水.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;郑鉴洋 男,1990年9月生于广西柳州.西北师范大学计算机科学与工程学院硕士研究生.研究方向为神经网络、机器学习.E-mail:148406243@qq.com
基金资助:
国家自然科学基金（No.61762080，No.61762078）；兰州市科学技术计划（No.2019-1-34）；西北师范大学青年教师科研能力提升计划创新团队（No.6008-01602）

A Neuronal Classification Approach with Adaptive Projection Using Deep Learning Networks

LIN Xiang-hong, ZHENG Jian-yang, WANG Xiang-wen, MA Hui-fang

School of Computer Science and Engineering, Northwest Normal University, Lanzhou, Gansu 730070, China

Received:2019-01-11 Revised:2019-12-15 Online:2020-07-25 Published:2020-04-02

摘要/Abstract

摘要： 传统的基于几何形态的神经元分类方法依赖于神经元空间结构特征的提取与选择，会损失大量有用的神经元分类信息.应用自适应投影算法将三维神经元进行转换，不需要提取神经元的几何特征，提出了一种基于深度学习网络的神经元几何形态分类方法.该方法将原始神经元数据进行三维体素重建，经过自适应投影过程构成二维神经元图像数据，并构建了基于双卷积门限循环神经网络的深度学习模型对神经元进行分类.将该方法应用于三种神经元分类数据集，通过与基于特征提取的神经元分类方法相比，实验结果表明该方法具有更高的分类准确率和良好的适应能力.

关键词: 神经元分类, 自适应投影, 卷积神经网络, 门限循环单元

Abstract: Traditional morphology-based neuronal classification approaches largely rely on the feature extraction and selection techniques of neuronal spatial structures,a lot of useful information for neuronal classification may be lost.Using the adaptive projection algorithm to convert the three-dimensional neuron data without feature extraction,this paper proposes a neuronal morphology classification approach based on deep learning networks.The three-dimensional voxel reconstruction is used for the original neuron data,and the two-dimensional neuron data is generated through adaptive projection process.Then,the deep learning model of double convolutional gated recurrent neural networks is established to classify neurons.The proposed approach is successfully applied to three neuronal classification datasets,the experiment results show that the proposed method has higher classification accuracy and flexibility than the neuronal classification methods based on feature extraction.

Key words: neuronal classification, adaptive projection, convolution neural network, gated recurrent unit

中图分类号:

TP183

蔺想红, 郑鉴洋, 王向文, 马慧芳. 基于深度学习网络的神经元自适应投影分类方法[J]. 电子学报, 2020, 48(7): 1321-1329.

LIN Xiang-hong, ZHENG Jian-yang, WANG Xiang-wen, MA Hui-fang. A Neuronal Classification Approach with Adaptive Projection Using Deep Learning Networks[J]. Acta Electronica Sinica, 2020, 48(7): 1321-1329.

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基于深度学习网络的神经元自适应投影分类方法

A Neuronal Classification Approach with Adaptive Projection Using Deep Learning Networks

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