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bioRxiv · 10.1101/2020.06.20.160564

Identification of Neuronal Polarity by Node-Based Machine Learning

Su, C.-Z.·Chou, K.-T.·Huang, H.-P.·Lo, C.-C.·Wang, D.-W.
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Abstract

Identifying 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 a 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 brains neural networks. Availability of data and materialThe FlyCircuit database (http://www.flycircuit.tw/) is provided by the National Center for High-Performance Computing. Code availabilityWe provide an online version of NPIN to be used or tested by other research groups at the following address: https://npin-for-drosophila.herokuapp.com/

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Su, C.-Z., Chou, K.-T., Huang, H.-P., Lo, C.-C., Wang, D.-W.. 2020-06-20. Identification of Neuronal Polarity by Node-Based Machine Learning. https://doi.org/10.1101/2020.06.20.160564

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