Contrastive learning-driven framework for neuron morphology classification

The Neuron morphology classification is a critical task in neuroscience research, as the morphological features of neurons are closely linked to the functional characteristics of neural circuits. However, traditional classification methods often struggle with the complexity and diversity of neuronal morphologies. To address this, we propose PRT-net, a network architecture specifically designed for neuron morphology classification. By incorporating innovative data augmentation strategies and a contrastive learning framework, PRT-net effectively improves classification performance and model generalization. PRT-net leverages Complex Residual Structures and TreeLSTM to efficiently model the local features and global dependencies of neuron morphology. To address issues of data scarcity and imbalance, we designed a tailored data augmentation strategy that simulates diverse morphological variations, enhancing model robustness. Experiments conducted on three public datasets-BIL, JML, and ACT-demonstrate that PRT-net achieves classification accuracies of 78.45%, 67.11%, and 58.95%, respectively, significantly surpassing existing state-of-the-art methods. Notably, it achieves improvements of 2.9 and 3.3 percentage points on the JML and ACT datasets, respectively. Through the introduction of multiple evaluation metrics, we comprehensively analyze the classification and clustering performance of the model, validating its strong adaptability to complex data distributions. This study provides an efficient solution for neuron morphology classification, advancing research in this domain.