Neural Circuit Mechanisms of Social Homeostasis in Individuals and Supraorganismal Social Groups

复杂的社会等级经历了动态和稳定阶段存在于物种中 从人类到老鼠到昆虫，在心理学中有丰富的描述， 生态学，但几乎没有什么是已知的神经回路机制，管理 大型动物群体的协调能力。系统神经科学已经爆发出 许多新技术，但这一领域的文化在很大程度上是还原主义的- 专注于生活在隔离或与一位数的笼友一起表演的动物， 高度控制的任务。虽然在社会领域有大量正在进行的研究， 奖励（动机从事社会行为的享乐价值，社会互动 提供），没有正在进行的研究（据我所知）检查神经表征 负效价需要状态（类似孤独的状态），社会稳态设定点，或 这和社会地位有什么关系事实上，这种未被探索的社会行为可能已经 与心理健康和社会负担的关系更大。 这个建议是完全不同的任何以前的工作所做的我自己或任何调查员 因为我们将做以下工作：1）提出一个社会稳态模型， 决定了社会接触的质量/数量的设定点; 2）桥梁行为生态学， 系统神经科学通过使用复杂的，自然主义的大群小鼠的动物园， 结合新生神经记录技术和广泛的专业知识， 功能电路解剖技术; 3）同时记录许多大脑使用 无线记录设备，以确定如何表示复合支配等级 并确定群体社会稳态的元脑模式（稳定和 动态阶段）存在，并观察它们在优势等级期间如何变化 重组;以及4）通过应用识别代表社会等级的站点和电路 机器学习方法来解码能准确预测动物社会行为的集合， 排名，并使用这些信息走向社会稳态的数学模型， 一个超有机体群体的水平。

Intricate social hierarchies that go through both dynamic and stable phases exist in species ranging from humans to mice to insects and have been richly described in psychology and ecology, but virtually nothing is known about the neural circuit mechanisms that govern the remarkable coordination of large groups of animals. Systems neuroscience has exploded with a number of novel technologies, yet the culture of this field has been largely reductionist – focusing on animals living in isolation or with a single-digit number of cage mates performing highly-controlled tasks. Although there is abundant ongoing research in the domain of social reward (motivation to engage in social behavior for hedonic value that social interaction provides), there is no ongoing research (to my knowledge) examining the neural representation of a negative valence need state (a loneliness-like state), the social homeostatic set-point, or how this is related to social rank. Indeed, this unexplored face of social behavior may have greater relevance to mental health and the burden on society. This proposal is completely different from any previous work done by myself or any investigator because we will do the following: 1) present a model for social homeostasis where social rank dictates the set-point for quality/quantity of social contact; 2) bridge behavioral ecology and systems neuroscience by using complex, naturalistic vivariums of large groups of mice in combination with nascent neural recording technology and expertise across a wide range of functional circuit dissection techniques; 3) simultaneously record across many brains using wireless recording devices to determine how composite dominance hierarchy is represented and determine whether meta-brain patterns for group social homeostasis (in both stable and dynamic phases) exist and observe how they change during dominance hierarchical reorganizations; and 4) identify site(s) and circuit(s) that represent social rank by applying machine learning approaches to decode ensembles that accurately predict the animal’s social rank, and use this information to move towards a mathematical model for social homeostasis on a supraorganismal group level.