用概率模型统一建模多个个体脑神经活动，提升泛化能力。
Deep probabilistic model synthesis enables unified modeling of whole-brain neural activity across individual subjects
- 通过变分推断构建共享先验与个体后验，融合多主体数据
- 在斑马鱼全脑神经数据上显著优于单体模型
- 适用于回归、分类等多类模型，适合跨个体神经建模
许多学科需要能整合同一类系统多个实例实验数据的量化模型。例如，神经科学家需结合多个个体动物的大脑数据以理解物种整体脑功能。然而，传统机器学习模型仅处理单一系统实例。本文提出深度概率模型合成（DPMS）框架，利用系统辅助属性，将不同实例数据联合建模。DPMS采用变分推断，学习参数的条件先验分布（关联不同实例）和实例特异性后验分布（捕捉个体差异）。该方法可适配回归、分类、降维等多种模型类型。我们在合成数据及斑马鱼幼体全脑神经活动数据上验证，其性能优于单实例模型。
原文摘要 · Abstract (English)
Many disciplines need quantitative models that synthesize experimental data across multiple instances of the same general system. For example, neuroscientists must combine data from the brains of many individual animals to understand the species' brain in general. However, typical machine learning models treat one system instance at a time. Here we introduce a machine learning framework, deep probabilistic model synthesis (DPMS), that leverages system properties auxiliary to the model to combine data across system instances. DPMS specifically uses variational inference to learn a conditional prior distribution and instance-specific posterior distributions over model parameters that respectively tie together the system instances and capture their unique structure. DPMS can synthesize a wide variety of model classes, such as those for regression, classification, and dimensionality reduction, and we demonstrate its ability to improve upon single-instance models on synthetic data and whole-brain neural activity data from larval zebrafish.
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