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

在物种中存在着复杂的社会等级，既经历了动态阶段，也经历了稳定阶段 从人类到老鼠再到昆虫，在心理学和 生态学，但实际上对支配大脑的神经回路机制一无所知 对大群动物的协调能力很强。系统神经科学已经爆发出一种 许多新技术，然而这个领域的文化在很大程度上是简约主义的- 重点关注与世隔绝的动物或有个位数的笼友表演 高度受控的任务。尽管在社会领域有大量正在进行的研究 奖励(从事社会行为的动机是为了享乐价值而不是社交 提供)，没有正在进行的研究(据我所知)检查神经表征 负价需要状态(类孤独状态)、社会内稳态设定值，或 这与社会等级有何关系。事实上，这种未被探索的社会行为面孔可能具有 与心理健康和社会负担的相关性更大。 这项提议与我或任何调查人员以前所做的任何工作都完全不同 因为我们将做以下工作：1)提出一个社会动态平衡模型，其中社会等级 规定了社会接触的质量/数量的设定点；2)桥接行为生态和 系统神经科学通过使用复杂的，自然的大组小鼠的隔膜 与新兴的神经记录技术和专业知识相结合，覆盖广泛的 功能电路分离技术；3)同时记录多个大脑 用于确定如何表示复合优势层次的无线记录设备 并确定群体社会动态平衡的元脑模式(在稳定和 动态阶段)存在，并观察它们在统治分层期间如何变化 重组；4)应用确定代表社会等级的场地(S)和巡回(S) 机器学习方法来解码能够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动态平衡的数学模型 一个超组织的团体级别。

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.