一种量化交互个体行为的无监督方法
An Unsupervised Method for Quantifying the Behavior of Interacting Individuals
摘要：
涉及多个个体相互作用的社会行为是复杂的，通常对动物的生存至关重要。这些相互作用，跨越感觉方式，长度尺度和时间尺度，往往是微妙的，难以量化。当动物之间的物理相互作用干扰常规数据分析时，例如由于视觉遮挡，对行为频率的背景效应变得更加难以量化。我们介绍了一种方法，用于量化的行为在求爱果蝇，结合高通量的视频采集和跟踪的个人与最近的无监督的方法捕捉动物的整个行为剧目。我们发现两性的成对和孤独的苍蝇的行为差异，确定特定的行为，受到社会和空间环境的影响。我们的管道允许使用无监督机器学习方法全面描述多个个体之间的相互作用，并将用于回答有关果蝇求偶复杂性和方差深度的问题。
英文摘要：
Social behaviors involving the interaction of multiple individuals are complex and frequently crucial for an animal's survival. These interactions, ranging across sensory modalities, length scales, and time scales, are often subtle and difficult to quantify. Contextual effects on the frequency of behaviors become even more difficult to quantify when physical interaction between animals interferes with conventional data analysis, e.g. due to visual occlusion. We introduce a method for quantifying behavior in courting fruit flies that combines high-throughput video acquisition and tracking of individuals with recent unsupervised methods for capturing an animal's entire behavioral repertoire. We find behavioral differences in paired and solitary flies of both sexes, identifying specific behaviors that are affected by social and spatial context. Our pipeline allows for a comprehensive description of the interaction between multiple individuals using unsupervised machine learning methods, and will be used to answer questions about the depth of complexity and variance in fruit fly courtship.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
