Contrastive learning-driven framework for neuron morphology classification

Yikang Jiang

doi:10.1038/s41598-025-11842-w

2025-07-30

Yikang Jiang *

Hao Tian

Quanbing Zhang

DOI：10.1038/s41598-025-11842-w

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摘要

En 中文

神经元形态分类是神经科学研究中的关键任务，因为神经元的形态特征与神经回路的功能特性密切相关。然而，传统分类方法在处理神经元形态的复杂性和多样性时往往面临挑战。为解决这一问题，我们提出了PRT-net，一种专门用于神经元形态分类的网络架构。通过整合创新的数据增强策略和对比学习框架，PRT-net有效提升了分类性能和模型泛化能力。PRT-net利用Complex Residual Structures和TreeLSTM高效地建模神经元形态的局部特征和全局依赖关系。为应对数据稀缺和不平衡问题，我们设计了一种定制化的数据增强策略，模拟多样化的形态变异，增强模型鲁棒性。在BIL、JML和ACT三个公开数据集上的实验表明，PRT-net分别实现了78.45%、67.11%和58.95%的分类准确率，显著超越了现有最先进的方法。值得注意的是，它在JML和ACT数据集上分别提升了2.9和3.3个百分点。通过引入多种评估指标，我们全面分析了模型的分类和聚类性能，验证了其对复杂数据分布的强适应性。本研究为神经元形态分类提供了一种高效解决方案，推动了该领域的研究进展。

Keyword:

Neuron morphology classification

Data augmentation

Contrastive learning

TreeLSTM

期刊

Scientific Reports

IF:

3.9

论文数:

28.0W

被引数:

83.5W

机构

Institute of Electronic Information Engineering

学者数:

论文数: 1

被引数: 0

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