IOS: Sensory networks and collective information processing in animal groups

IOS：动物群体的感觉网络和集体信息处理

• 批准号: 1355061
• 负责人: Iain Couzin
• 金额: $ 32.5万
• 依托单位: Princeton University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-01 至 2019-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1355061&HistoricalAwards=false
• 关键词: IOS; Sensory; networks; collective; information

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.

有效的信息传递对于协调细胞内、神经元、社会和经济网络内的行为至关重要。在许多动物群体中，例如成群的鱼类和成群的​​鸟类，个体间交流的程度和速度使个体群体成员能够在各种情况下做出快速而准确的集体决策，并且通常是在相当大的风险条件下。这种涌现的特性对于许多技术应用来说是非常理想的，包括机器人代理组的协调搜索、控制和响应。该项目将采用实验方法来绘制一系列生态相关场景下鱼群的感官输入和行为输出之间的关系，以识别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以及群体表现出的高效集体行为联系起来。这些结果将直接为集体机器人技术提供信息。本科生和研究生将参与该项目的各个方面，并将融入 Couzin 博士教授的课程中。 Couzin 博士还将通过普林斯顿大学预科项目 (PUPP) 为新泽西州当地学区的成绩优异、低收入的高中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，吸引公众参与。该项目将揭示群体中信息流背后的网络的复杂结构。 主要范式是将此类群体中的个体视为“自驱动粒子”，它们通过“社会力量”与邻居相互作用。 这种方法的一个主要限制是，它既不考虑动物在群体内做出运动决策时可用的感官信息，也不考虑生物体以状态依赖和概率的方式做出决策。为了绘制在时间尺度和反应类型不同的生态相关条件下鱼群的视觉和侧线感觉输入与由此产生的行为之间的关系，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、无监督学习和逆向方法将用于识别个人使用的视觉信息，并绘制侧线促进的社会反应的结构。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。在不同的生态背景下，将使用信息论技术来量化跨传感网络的信息传递。这些数据将为后续对个人行为的操纵提供信息，以测试有关群体如何过滤噪音和对无关线索做出反应的预测。通过这项工作，研究人员将创建集体动物行为的新模型。