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A Neuronal Morphology Classification Approach Based on Deep Residual Neural Networks

  • Xianghong Lin
  • Jianyang Zheng
  • Xiangwen Wang
  • Huifang Ma
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11304)

Abstract

The neuron classification problem is significant for understanding structure-function relationships in computational neuroscience. Advances in recent years have accelerated the speed of data collection, resulting in a large amount of data on the geometric, morphological, physiological, and molecular characteristics of neurons. These data encourage researchers to strive for automated neuron classification through powerful machine learning techniques. This paper extracts a statistical dataset of 43 geometrical features obtained from 116 human neurons, and proposes a neuronal morphology classification approach based on deep residual neural networks with feature scaling. The approach is applied to classify 18 types of human neurons and compares the accuracy of different number of residual block. Then, we also compare the accuracy between the proposed approach and other mainstream machine learning approaches, the classification accuracy of our approach is 100% in the training set and the testing set accuracy is 76.96%. The experimental results show that the deep residual neural network model has better classification accuracy for human neurons.

Keywords

Neuron classification Geometric features Deep residual neural network Feature scaling 

Notes

Acknowledgment

The work is supported by the National Natural Science Foundation of China under Grant No. 61762080, and the Medium and Small Scale Enterprises Technology Innovation Foundation of Gansu Province under Grant No. 17CX2JA038.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Xianghong Lin
    • 1
  • Jianyang Zheng
    • 1
  • Xiangwen Wang
    • 1
  • Huifang Ma
    • 1
  1. 1.College of Computer Science and EngineeringNorthwest Normal UniversityLanzhouChina

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