  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.
