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.
