IOS: Sensory networks and collective information processing in animal groups

有效的信息传递对于细胞内、神经元、社会和经济网络内的行为协调至关重要。在许多动物群体中，如鱼群和成群的鸟类，个体间交流的程度和速度使个体群体成员能够在一系列环境中做出快速而准确的集体决策，而且往往是在相当危险的条件下。这种紧急特性在许多技术应用中是非常理想的，包括机器人代理群体的协调搜索、控制和响应。本项目将采用实验方法绘制一系列生态相关情景下鱼群感官输入和行为输出之间的关系，以确定群体中感官交流的动态网络，并将其与个体表现出的基本运动行为和群体表现出的高效集体行为联系起来。这些结果将直接指导集体机器人。本科生和研究生将参与这个项目的各个方面，并将融入Couzin博士的课程。Couzin博士还将通过普林斯顿大学预备计划（Princeton University Preparatory Program， PUPP）为新泽西州当地学区的高成就低收入高中学生开发一个关于集体行为的暑期学习模块，并继续与国家地理数字媒体和国家地理学习合作，让公众参与。该项目将揭示群体信息流基础网络的复杂结构。主流的范式一直认为群体中的个体是“自我推进的粒子”，通过“社会力量”与邻居互动。这种方法的一个主要限制是，它既没有考虑动物在群体内做出运动决定时可用的感觉信息，也没有考虑生物以状态依赖和概率方式做出决定。为了绘制出视觉和侧线感觉输入之间的关系，以及在不同时间尺度和反应类型的生态相关条件下鱼群的行为，PI将使用定制软件来确定群体成员的位置、身体姿势和眼睛位置，以重建多达数百条鱼的群体中所有个体的视野。贝叶斯、无监督学习和逆方法将用于识别个体使用的视觉信息，并绘制由侧线促进的社会反应结构。多尺度网络分析将用于识别群体内的重要属性和有意义的主题/子结构，并将这些与集体能力联系起来。利用信息论技术对不同生态环境下感官网络间的信息传递进行量化。这些数据将为随后的个人行为操纵提供信息，以测试关于群体如何过滤噪音和对外来线索作出反应的预测。从这项工作中，研究人员将创建新的动物集体行为模型。

Effective information transfer is essential for the coordination of behavior within intracellular, neuronal, social and economic networks. In many animal groups, such as schooling fish and flocking birds, the degree and speed of inter-individual communication allows individual group members to make fast and accurate collective decisions across a range of contexts, and often under conditions of considerable risk. Such emergent properties are highly desirable for many technological applications, including coordinated search, control and response by groups of robotic agents. This project will employ an experimental approach to map the relationship between sensory input and behavioral output in schooling fish under a range of ecologically-relevant scenarios in order to identify the dynamic networks of sensory communication in groups and relate this to the elementary movement behaviors exhibited by individuals, and the highly effective collective behavior exhibited by groups. These results will directly inform collective robotics. Undergraduate and graduate students will be involved in all aspects of this project and it will be integrated into classes taught by Dr. Couzin. Dr. Couzin will also develop a summer learning module on collective behavior for high-achieving, low-income high school students from local school districts in NJ through the Princeton University Preparatory Program (PUPP) and continue to work with National Geographic digital media and National Geographic Learning to engage the public This project will reveal the complex structure of the networks underlying information flow in groups. The predominant paradigm has been to consider individuals in such groups as "self-propelled particles", which interact with neighbors through "social forces". A major limitation of this approach is that it neither considers the sensory information available to animals when making movement decisions within groups nor considers that organisms make decisions in a state - dependent and probabilistic fashion. To map the relationship between visual and lateral line sensory input and resulting behavior in schooling fish under ecologically-relevant conditions that vary in timescale and type of response, the PI will use custom software to determine the location, body posture and eye positions of members of the group to reconstruct the visual fields of all individuals in groups of up to several hundred fish. Bayesian, unsupervised learning and inverse methodologies will be used to identify the visual information used by individuals and to map the structure of social response facilitated by the lateral line. Multi-scale network analysis will be used to identify important properties and meaningful motifs/substructures within groups, and to relate these to collective capabilities. Information transfer across sensory networks will be quantified using information theoretic techniques under the different ecological contexts. These data will inform subsequent manipulations of individual behavior to test predictions about how groups filter noise and respond to extraneous cues. From this work the researchers will create new models of collective animal behavior.