用生成模型从钙成像数据中学习单神经元表示，减少批次效应。
Integration of Calcium Imaging Traces via Deep Generative Modeling
- 基于变分自编码器直接从荧光信号学单神经元特征
- 在模拟和实验数据上均优于现有方法，保留生物差异性
- 适合神经科学领域研究神经元活动模式的学者
钙成像可无创、空间分辨地并行测量大量神经元，已成为神经功能研究的金标准。尽管深度生成模型已成功用于神经元集群活动分析，但其在从钙成像荧光轨迹中学习单神经元表征方面的潜力尚未充分探索，且批次效应仍是重要挑战。为此，我们研究了监督式变分自编码器架构，无需依赖尖峰推断算法，即可从荧光信号中学习紧凑的单神经元表示。结果表明，该方法优于当前最优模型，在保留生物多样性的同时有效缓解批次效应。在模拟与真实实验数据集上，该框架实现了稳健的可视化、聚类与单神经元动态解读。
原文摘要 · Abstract (English)
Calcium imaging allows for the parallel measurement of large neuronal populations in a spatially resolved and minimally invasive manner, and has become a gold-standard for neuronal functionality. While deep generative models have been successfully applied to study the activity of neuronal ensembles, their potential for learning single-neuron representations from calcium imaging fluorescence traces remains largely unexplored, and batch effects remain an important hurdle. To address this, we explore supervised variational autoencoder architectures that learn compact representations of individual neurons from fluorescent traces without relying on spike inference algorithms. We find that this approach outperforms state-of-the-art models, preserving biological variability while mitigating batch effects. Across simulated and experimental datasets, this framework enables robust visualization, clustering, and interpretation of single-neuron dynamics.
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