CAREER: Computing rules of the social brain: behavioral mechanisms of function and dysfunction in biological collectives

生物集体--相互作用的生物个体的群体--存在于组织的各个层面：从组成生物组织的个体细胞组，到以高等动物社会为特征的持久的社会群体。这种集体的一个特点是个体之间的协调，这对免疫细胞群体清除病原体、组织的协调发展以及动物群体快速但准确的决策等各种过程至关重要。然而，协调并不是集体表现出来的唯一行为。有时，它们也可能被紊乱和系统性功能障碍所主宰。例子包括动物群体中自身免疫和不受控制的错误信息级联背后的过度活跃的免疫反应。这个项目的目标是回答一个几十年来一直困扰着科学家的问题：生物集体如何以及为什么在某些情况下无缝协调，而在另一些情况下却表现出灾难性的功能障碍？该项目将通过将优雅的实验与数学和计算模型相结合来回答这个问题，这些模型试图确定导致这种变化的因素。该项目将有助于为解决各种问题奠定经验和理论基础，范围从分布式机器人和传感器网络的控制到旨在促进伤口愈合或扰乱病原体集体行为的健康干预。对集体功能障碍背后的机制的了解，也可能为动物迁徙的保护、种群和生态系统动态的管理，以及功能和功能失调的社会行为背后的神经机制等各种主题提供洞察。教育和更广泛的影响活动将集中在加强本科生和研究生水平的生命科学学生的数学和人工智能素养，并通过与自然历史教育者、科学课程开发者和教师的接触，在学术之外分享科学知识。该项目旨在揭示核心科学原理，这些原理支配着为什么生物集体在某些情况下有效运作，而在另一些情况下表现出灾难性的功能障碍。该项目将涉及理论发展和鱼群实验之间的强烈反馈--鱼群是生物集体的标志性模型。利用新颖的闭环实验系统和人工智能驱动的计算机视觉工具，该项目将研究集体如何在精确控制的实验室环境中解决与生态相关的任务(例如，躲避捕食者、决策、觅食)。该项目寻求实现三个目标。目标1关注的是个体如何应对在群体内做出决策时出现的相互冲突的要求。目标2侧重于当个人相互学习时出现的一个关键问题：如何处理错误信息。目标3专注于一种被广泛记录的被称为行为锁定的现象，即一个群体中的个人过度依赖他人的信息，以至于对环境变得反应迟钝。该项目将通过(I)确定生物集体中功能和功能障碍的一般机制，(Ii)发现个体表型、集体表型和健康相关结果之间的机制联系，(Iii)产生关于集体行为的神经基础的量化假设，以及(Iv)开发牢固植根于数据的集体行为的一般理论模型，从而促进科学知识的发展。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

Biological collectives – groups of interacting biological individuals – are found at every level of organization: from the groups of individual cells that make up biological tissue, to the persistent social groups that characterize societies of higher animals. A hallmark of such collectives is coordination among individuals, which is critical to processes as diverse as pathogen clearance by populations of immune cells, coordinated development of a tissue, and swift but accurate decision-making by animal groups. However, coordination is not the only behavior collectives exhibit. They can also, at times, be dominated by disorder and systemic dysfunction. Examples include the hyperactive immune responses behind autoimmunity and uncontrolled misinformation cascades in animal groups. The goal of this project is to answer a question that has eluded scientists for decades: how and why do biological collectives coordinate seamlessly in some situations, yet exhibit catastrophic dysfunction in others? The project will answer this question by combining elegant experiments with mathematical and computational models that seek to identify the factors contributing to this variation. This project will contribute to the empirical and theoretical foundations for solving diverse problems ranging from the control of distributed robots and sensor networks to health interventions aimed at promoting wound healing or disrupting collective behaviors of pathogens. Knowledge of the mechanisms behind collective dysfunction may also provide insights into topics as diverse as the conservation of animal migrations, management of population and ecosystem dynamics, and the neural mechanisms that underlie functional and dysfunctional social behavior. Education and broader impact activities will focus on strengthening mathematical and AI literacy among life science students at undergraduate and graduate levels and sharing scientific knowledge beyond academia by engaging with natural history educators, science curriculum developers, and teachers.This project seeks to reveal core scientific principles that govern why biological collectives function effectively under some circumstances, yet exhibit catastrophic dysfunction under others. The project will involve a strong feedback between theory development and experimentation with schooling fish – an iconic model of biological collectives. Using novel closed-loop experimental systems and AI-driven computer vision tools, the project will study how collectives solve ecologically-relevant tasks (e.g., predator evasion, decision-making, foraging) within precisely-controlled laboratory environments. The project seeks to achieve three aims. Aim 1 focuses on how individuals cope with conflicting demands that arise when making decisions within a group. Aim 2 focuses on a critical issue that arises when individuals learn from one another: how to cope with misinformation. Aim 3 focuses on a widely-documented phenomenon known as behavioral lock-in, wherein individuals in a group over-rely on information from others to the point of becoming unresponsive to the environment. The project will advance scientific knowledge by (i) identifying general mechanisms of function and dysfunction in biological collectives, (ii) discovering mechanistic links between individual phenotype, collective phenotype, and fitness-relevant outcomes, (iii) generating quantitative hypotheses about the neural basis of collective behavior, and (iv) developing general theoretical models of collective behavior that are strongly rooted in data.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.