Identifying the direction of signal flows in neural networks is important for understanding the intricate information dynamics of a living brain. Using a dataset of 213 projection neurons distributed in more than 15 neuropils of a Drosophila brain, we develop a powerful machine learning algorithm: node-based polarity identifier of neurons (NPIN). The proposed model is trained only by information specific to nodes, the branch points on the skeleton, 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 neurons in other species (Blowfly and Moth), which have much less neuronal data available. Our results demonstrate the potential of NPIN as a powerful tool to identify the neuronal polarity of insects and to map out the signal flows in the brain’s neural networks if more training data become available in the future.

识别神经网络中信号流的方向对于理解活体大脑复杂的信息动态非常重要。使用分布在果蝇大脑超过 15 个神经细胞中的 213 个投射神经元的数据集，我们开发了一种强大的机器学习算法：基于节点的神经元极性标识符 (NPIN)。所提出的模型仅通过特定于节点（骨架上的分支点）的信息进行训练，并且包括 Soma 特征（包含从给定节点到 Soma 的空间信息）和局部特征（包含给定节点的形态信息） 。在包含节点极性之间的空间相关性后，我们的 NPIN 为神经元极性分类提供了极高的准确度 (>96.0%)，即使对于具有两个以上树突/轴突簇的复杂神经元也是如此。最后，我们进一步应用 NPIN 对其他物种（绿头蝇和蛾）的神经元极性进行分类，这些物种的可用神经元数据要少得多。我们的结果证明了 NPIN 作为一种强大工具的潜力，可以识别昆虫的神经元极性，并在未来获得更多训练数据时绘制大脑神经网络中的信号流。