Causal Relationships Underlying the Collective Dynamic Behavior of Swarms

点击翻译按钮获取中文摘要

Living in groups affords several benefits for animals such as better feeding opportunities and reduced predation risks. In both instances-foraging and predator avoidance-critical information is transmitted nonverbally throughout the group, at different time scales. While current methods in animal behavior allow for inferring information sharing during coordinated maneuvers, these interactions are only a small subset of the complex behavioral repertoire of animal groups. This award supports fundamental research to investigate directional information flow underlying collective animal behavior, through the integration of dynamical systems theory and behavioral studies on the zebra fish animal model. The implications of this research are potentially transformative in the area of behavioral brain research and neuropsychobiology, where zebra fish is rapidly emerging as a valid preclinical animal model. Complementing the research are interdisciplinary education and outreach activities that will foster outside-the-box and multidisciplinary thinking in underrepresented students in engineering, and benefit the education of underprivileged students in Brooklyn public schools.This research program seeks to demonstrate that an information-theoretic approach can be used to measure social animal behavior. Specifically, this award will establish a rigorous model-free framework to study causal relationships in animal interactions. A series of hypothesis-driven experiments on zebra fish will be conducted to emphasize unidirectional information transfer by controlling visual feedback between conspecifics and using independently controlled robotic replicas. Subsequently, the proposed model-free framework and established experimental paradigms will be used to investigate information flow in shoaling and schooling zebra fish along with the social implications of individual differences on their collective behavior. Toward this aim, robotics-based platforms, multi-target tracking software, and novel experimental protocols will be utilized for engineering and dissecting causal relationships that are central to the validation of the envisioned approach. The unique datasets from these laboratory experiments will support the validation of a multitude of theoretical dynamical-systems approaches toward unraveling causality in complex biological and technological systems.