发表机构
HHMI Janelia Research Campus（霍华德·休斯医学研究所珍利亚研究园区）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究旨在从跟踪姿态训练以智能体为中心的动物行为自回归模型，引入相关框架及通用库，模型能捕捉果蝇群体社会行为分布，库可支持系统比较并适应新领域。
AI 中文摘要
在算法层面理解动物行为，如动物关注什么、如何形成内部模型和计划以及如何转化为行动，仍是神经科学和动物行为学的核心挑战。数据驱动的生成模型为此提供了途径。我们引入一个框架，用于从跟踪姿态训练以智能体为中心的动物行为自回归模型，适用于单只动物和群体。模型输入自我中心感官观察并输出自我中心运动。社会行为源于智能体间相互感应和反应。该以智能体为中心的公式需要管理同一数据的多个并行表示及特定机器学习转换。我们发布了一个专注于在这些表示之间转换的可组合操作序列的通用库。我们表明训练后的模型捕捉求偶果蝇群体中社会行为的分布，且库包含测量拟合度的定量工具。我们展示了该库如何支持跨输入和输出表示的系统比较，以及它如何直接适应新领域。
英文摘要
Understanding animal behavior at an algorithmic level -- what animals attend to, how they form internal models and plans, and how this maps to action -- remains a central challenge in neuroscience and ethology. Data-driven generative models offer a path toward this understanding. We introduce a framework for training agent-centric autoregressive models of animal behavior from tracked pose, applicable to single animals and to groups in which each agent senses and responds to its conspecifics. Our models input egocentric sensory observations and output egocentric movements, mirroring the biological constraint that animals observe and act on the world from their own reference frame. Social behavior emerges from agents independently sensing and responding to one another. This agent-centric formulation requires managing many parallel representations of the same data, along with ML-specific transformations like discretization. We release a general-purpose library focused on the composable sequences of operations that translate between these representations. We show that trained models capture the distribution of social behavior in groups of courting Drosophila, and our library includes quantitative tools for measuring fit. We demonstrate how the library supports systematic comparison across input and output representations and that it adapts straightforwardly to a new domain.
