无需标注，通过邻近关系自动识别果蝇电镜图像中的突触类型。
Self-Supervised Learning of Synapse Types from EM Images
- 利用同一神经元内突触的邻近相似性进行自监督分类。
- 在果蝇数据上成功分离出多个突触类型，无需预先设定类别数。
- 适合生物神经连接组研究者，可为突触功能分析提供结构依据。
基于电镜图像中突触外观对突触进行分类在生物学中有广泛应用，例如确定特定类型的神经递质，或区分可调制与不可调制强度的突触。传统方法依赖有监督学习，需提供各类别的标注样本。本文提出一种自监督方法，仅依赖同一神经元内突触彼此更相似这一观察，而非随机选择来自不同细胞的突触。该方法应用于果蝇（Drosophila）的电镜数据，优势在于无需预先知道突触类型的数量，且可提供覆盖突触结构多样性的合理真实标签基础。
原文摘要 · Abstract (English)
Separating synapses into different classes based on their appearance in EM images has many applications in biology. Examples may include assigning a neurotransmitter to a particular class, or separating synapses whose strength can be modulated from those whose strength is fixed. Traditionally, this has been done in a supervised manner, giving the classification algorithm examples of the different classes. Here we instead separate synapses into classes based only on the observation that nearby synapses in the same neuron are likely more similar than synapses chosen randomly from different cells. We apply our methodology to data from {\it Drosophila}. Our approach has the advantage that the number of synapse types does not need to be known in advance. It may also provide a principled way to select ground-truth that spans the range of synapse structure.
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