Identification of Neuronal Polarity by Node-Based Machine Learning

TL;DR

This paper introduces NPIN, a machine learning model that accurately determines neuronal polarity using only nodal features, aiding the understanding of neural information flow in insect brains.

Contribution

The paper presents a novel node-based machine learning algorithm, NPIN, capable of classifying neuronal polarity with high accuracy using spatial and morphological features.

Findings

NPIN achieves over 96% accuracy in classifying neuronal polarity.

NPIN effectively classifies complex neurons with multiple dendrite/axon clusters.

The model successfully applies to species with limited neuronal data, like the blowfly.

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.