Contrastive learning-driven framework for neuron morphology classification.

ABSTRACT: 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 imba

SUBMITTER: Jiang Y 

PROVIDER: S-EPMC12311092 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature