Morphological neuron classification helps to reveal the functional characteristics and information transmission mechanisms of the nervous system. However, existing methods that use geometric feature extraction or image-based transformation do not consider the 3D properties of neurons, often resulting in a significant loss of valuable morphological information. To address this, we propose a spiking point cloud neural network (SPCNNet) model to improve classification performance, which is capable of directly processing 3D point clouds and applying spike signals to represent morphological features and classify neurons. A neuronal representation strategy is designed to convert original SWC data into 3D point clouds, and encode real-valued point cloud data into spike trains for further processing by the spiking neural networks. Furthermore, the SPCNNet model with spike-based deep learning algorithm learns the spatial features of neurons for classification tasks. In experiment, we analyzed the impact of different SPCNNet parameters on neuron classification performance, including the number of sampled points, simulation duration and batch size. We also conducted ablation experiments to verify the effectiveness of the proposed method. Experimental results demonstrate that our SPCNNet method precisely represents neuronal morphologies and achieves superior performance on the two NeuroMorpho datasets, with classification accuracies of 84.76% and 85.42% respectively. Compared with other mainstream machine learning methods, our spike-driven method is more plausible for solving complex morphological neuron classification problems on NeuMorph dataset.
Lin et al. (Mon,) studied this question.