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GNMR:基于图神经网络的三维神经元几何形态检索

成普

计算机与数字工程2024,Vol.52Issue(4):1131-1136,6.
计算机与数字工程2024,Vol.52Issue(4):1131-1136,6.DOI:10.3969/j.issn.1672-9722.2024.04.030

GNMR:基于图神经网络的三维神经元几何形态检索

GNMR:3D Neuron Morphology Retrieval Based on Graph Neural Network

成普1

作者信息

  • 1. 西安工程大学计算机科学学院 西安 710048
  • 折叠

摘要

Abstract

Neuron morphology and structure is an important task to analyze neuron activity and development function.How to effectively identify neurons of different shapes is a challenge.This paper proposes a new method of 3D neuron morphology retrieval based on graph convolutional neural network(GNMR for short).First,the child-parent node scheme is used to preprocess the three-dimensional neuron.According to the spatial geometric structure of the three-dimensional shape,a three-dimensional neuron is mapped to the three planes of X-Y,X-Z and Y-Z.Secondly,a GNMR is designed to retrieve Neuron shape,in order to avoid the problem of gradient explosion and gradient disappearance,three layers of ReLU function are added to the connection layer.Finally,the method is simulated on the NEU-1500 data set.The experimental results show that the method can effectively identify the shape of three-dimensional neurons,and has high retrieval accuracy,precision and recall.

关键词

神经元几何形态/图神经网络/神经元识别/ReLU函数

Key words

neuron geometry/graph neural network/neuron recognition/ReLU function

分类

信息技术与安全科学

引用本文复制引用

成普..GNMR:基于图神经网络的三维神经元几何形态检索[J].计算机与数字工程,2024,52(4):1131-1136,6.

计算机与数字工程

OACSTPCD

1672-9722

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