Identification of Neuronal Polarity by Node-Based Machine Learning
Abstract
Identify the directions of signal flows in neural networks is one of the most important stages for understanding the intricate information dynamics of a living brain. Using a dataset of 213 projection neurons distributed in different regions of Drosophila brain, we develop a powerful machine learning algorithm: node-based polarity identifier of neurons (NPIN). The proposed model is trained by nodal information only and includes both Soma Features (which contain spatial information from a given node to a soma) and Local Features (which contain morphological information of a given node). After including the spatial correlations between nodal polarities, our NPIN provided extremely high accuracy (>96.0%) for the classification of neuronal polarity, even for complex neurons with more than two dendrite/axon clusters. Finally, we further apply NPIN to classify the neuronal polarity of the blowfly, which has much less neuronal data available. Our results demonstrate that NPIN is a powerful tool to identify the neuronal polarity of insects and to map out the signal flows in the brain's neural networks.
Keywords
Cite
@article{arxiv.2006.12148,
title = {Identification of Neuronal Polarity by Node-Based Machine Learning},
author = {Chen-Zhi Su and Kuan-Ting Chou and Hsuan-Pei Huang and Chung-Chuan Lo and Daw-Wei Wang},
journal= {arXiv preprint arXiv:2006.12148},
year = {2020}
}
Comments
Manuscript: 18 pages and 9 figures; Appendix: 14 pages, 5 figures, and 2 tables