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arXiv · 2006.12148

Identification of Neuronal Polarity by Node-Based Machine Learning

Chen-Zhi Su·Kuan-Ting Chou·Hsuan-Pei Huang·Chung-Chuan Lo·Daw-Wei Wang
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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.

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Chen-Zhi Su, Kuan-Ting Chou, Hsuan-Pei Huang, Chung-Chuan Lo, Daw-Wei Wang. 2020-06-22. Identification of Neuronal Polarity by Node-Based Machine Learning. https://doi.org/10.1101/2020.06.20.160564

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