Development of an Integrated System for Monitoring Home-Cage Behavior in Non-Human Primates

7：项目摘要/摘要 绒猴正在成为神经科学研究的重要模式物种，在 开发新技术，如CRISPR，允许在 这个物种。这些发展将使灵长类研究能够利用强大的 以前主要局限于啮齿动物的遗传工具，包括光遗传学、遗传学 活性记者，以及与脑功能和脑功能相关的内源性基因的靶向突变 人类疾病。绒猴非常适合这种方法，因为它们体型小，繁殖快。 与大多数灵长类动物相比。它们通常被安置在家庭团体中，并展示了各种 圈养期间的社会行为，包括复杂的演唱曲目。因此，绒猴代表着一种 在灵长类动物模型中研究社会行为和其他认知功能的有前途的系统， 它们还为影响认知功能的大脑紊乱建模带来了巨大的希望 在啮齿动物等其他物种中很难研究。要充分利用这些新出现的 对于动物模型，有必要开发新的方法来分析它们的行为，包括 标准化行为任务不能完全捕捉到的自然主义社会互动。我们 因此，计划开发一个在家自动分析绒猴行为的系统 凯奇。该系统将由一个集成的传感器阵列组成，包括摄像机、深度 传感器和带衣领的可穿戴麦克风。产生的多模式数据将是 使用计算机视觉、语音处理、机器等方法进行同步和分析 学习和多模式数据分析。具体地说，我们将跟踪分析表述为 概率图形模型，它将允许视频数据与音频集成 记录，以及今后可以探索的其他模式，包括惯性运动 传感器、生理记录和其他上下文数据。基于这种方法，我们将 制定方法对来电进行分类，识别单个呼叫者，跟踪以下人员的位置和身份 每个动物在三个维度上，并对不同的动作进行分类，包括相互作用 个人。我们预计，我们的系统将适用于广泛的基础研究和 翻译神经科学，特别是它将有助于研究行为表型 在人类精神疾病的遗传模型中，并将行为异常与 其潜在的遗传和神经原因。

7: Project Summary/Abstract Marmosets are emerging as an important model species for neuroscience research, driven by the development of new technologies such as CRISPR that allow targeted genetic modifications in this species. These developments will allow primate research to take advantage of powerful genetic tools that were previously restricted largely to rodents, including optogenetics, genetic activity reporters, and targeted mutation of endogenous genes implicated in brain function and human disease. Marmosets are well suited to this approach, being small and fast-breeding compared to most primates. They are typically housed in family groups, and exhibit a variety of social behaviors in captivity including complex vocal repertoires. Marmosets thus represent a promising system for studying social behavior and other cognitive functions in a primate model, and they also hold great promise for modeling brain disorders that affect cognitive functions that are difficult to study in other species such as rodents. To take full advantage of these emerging animal models, it is necessary to develop new methods for analyzing their behavior, including naturalistic social interactions that are imperfectly captured by standardized behavioral tasks. We therefore plan to develop a system for automated analysis of marmoset behaviors in the home cage. The system will consist of an integrated array of sensors including video cameras, depth sensors, and collar-mounted wearable microphones. The resulting multimodal data will be synchronized and analyzed using methods from computer vision, speech processing, machine learning, and multimodal data analysis. Specifically we will formulate the tracking analysis as a probabilistic graphical model, which will allow video data to be integrated with audio recordings, and with other modalities that could be explored in future, including inertial motion sensors, physiological recordings and other contextual data. Based on this approach we will develop methods to classify calls, identify individual callers, track the locations and identities of each animal in three dimensions, and classify different actions, including interactions between individuals. We envisage that our system will be useful for a wide range of studies in basic and translational neuroscience, and in particular it will be useful for studying behavioral phenotypes in genetic models of human psychiatric disorders, and for relating behavioral abnormalities to their underlying genetic and neural causes.