Programs > Brochure
IIP-Max Planck Institute for Ornithology, Department of Collective Behavior
Konstanz, Germany (Outgoing Program)
|Partner Institution/Organization Homepage:||Click to visit|
|Restrictions:||Princeton applicants only|
|Dept Offering Program:||International Internship Program (IIP)||Program Type:||Internship|
|Language Prerequisite:||No||Program Features:||Academic Study, Field Work, Lab Based Work, Research|
|Degree Level:||1st year u/g students, 2nd year u/g students, 3rd year u/g students||Time Away:||Summer|
|Housing options:||Student Responsibilty with support from IIP and/or Host Organization||Program Group:||International Internship Program|
Max Planck Institute for Ornithology, Department of Collective Behavior
About: The goal of the Max Planck Society for the Advancement of Science is to support excellent fundamental research in the natural, life, and social sciences, as well as arts and humanities. This goal is achieved in more than eighty Max Planck Institutes, each of which focuses on a single area of research. The Institutes of the Max Planck Society is independent and autonomous in the selection and conduct of their research pursuits. Each institute has its own, internally managed, budget, which is supplemented by third-party funds through competitive research grants and collaborations. The Max Planck Institute for Ornithology has four departments: Department of Behavioral Neurobiology, Department of Behavioral Ecology and Evolutionary Genetics, Department of Migration and Immuno-Ecology and Department of Collective Behavior. The Max Planck Department of Collective Behavior consists of three labs which work on a wide range of organisms in both the laboratory and field, including fish, insects, arachnids, mammals, and birds. Their department is a highly interdisciplinary environment with a closely integrated experimental and theoretical research program to understand the fundamental principles that underlie collective behavior across levels of biological organization.
Intern Responsibilities: IIP interns will work on one or more of the following projects:
Project 1 - Collective Sensing: In the natural world, individuals constantly face the challenge of acquiring, interpreting and responding to complex sensory information. Empirical evidence suggests that animals deal with this challenge by living in groups and processing information collectively. While these collective properties are manifested at the group level, they are an outcome of decisions made by individuals. In general, it is unclear how selection of behavioral rules adopted by individuals leads to the evolution of group-level properties such as collective information processing and distributed sensing. Previous models in collective behavior have been successful in explaining experimental data in a range of contexts and species, including leadership and consensus decision-making in fish and baboons. Even though these models work well-predicting group behavior in moderate to large groups, they fail to reliably reproduce the behavior in small groups and solitary individuals. This limitation constrains our ability to truly examine the benefits of collective information processing and distributed sensing because it prevents comparison across various group sizes. The IIP intern will be involved in analyzing movement data from lab-based recordings of fish in isolation and in varying group sizes and building on top of existing schooling models to account for individual behavior. These more realistic models will then enable comparison across scales from individuals to collectives.
Project 2 - Collective behavior in locust swarms: Understanding how organisms process sensory information in the brain to produce behavior is one of the most exciting scientific problems of the 21st century. More specifically, understanding the sensory and behavioral mechanisms that animals use to successfully migrate long distances is one of the great scientific challenges of our time. Movement in migrating swarms of locusts is driven by cannibalistic interactions where individual movement decisions are made based on the threat of being cannibalized from behind and the motivation to cannibalize others ahead. The behavior of individuals within these marching bands is the result of visual and physical contact between individuals. The lab studies questions related to how sensory information and individual behavior influence the movement dynamics of group migration. Marching behavior in juvenile desert locusts is used as a model system to address two broad questions: 1) How do sensory information networks drive individual decision-making and group-level movement dynamics in migrating animal groups? 2) How do individual differences in behavioral state and group composition influence movement dynamics in migrating animal groups? The team conducts experiments in behavioral arenas that are filmed using multiple synchronized high-resolution 4K video cameras. Using computer vision techniques we measure the movement of individuals while also maintaining individual identities with 2-D barcode tags (similar to QR codes) attached to individuals. The visual fields of individuals are calculated using ray casting algorithms like those used in video game engines. By measuring visual and physical interactions between individuals we can infer the underlying social networks that drive both individual and group-level movement.
IIP interns will help design and conduct experiments and analyze these data using unsupervised machine learning methods to classify the behavior of individuals and describe changes in the behavioral state across time and context.
Project 3 - The dynamics of group hunting and collective evasion: One benefit of sociality in prey animals is collective predator detection. For collective detection to occur, information regarding the presence of predators must be transferred from knowledgeable individuals (detectors) to naive individuals (non-detectors). For this project, the lab uses drone-mounted cameras to capture aerial videos of ungulate (hoofed animal) groups in Kenya to study individual and group level vigilance patterns. To observe the process of information transfer, we present model predators to groups and record their reactions. From these videos, the lab uses software to extract continuous movement and behavioral data for every member of the group. Because of the complex background of these aerial videos, the data is extracted from the videos with lab built deep learning based object detection and recognition algorithms. Using the data extracted from these videos we can computationally reconstruct visual fields of individuals and, along with 3D habitat models, explicitly consider the information available to each individual and investigate how this information affects individual behavioral decisions. Other people will carry out the actual filming, but the IIP intern can help with every other aspect of this project spending the most time working on what most interests them.
Project 4 - Revealing the structure of sensory interaction networks in animal groups: The predominant paradigm in the study of animal collectives has been to consider individuals as self-propelled particles which interact via social forces such as local repulsion, and longer-range attraction. This approach fails to consider key aspects of biology for many group-living species such as identity, social status, relatedness and informational status. The social relationships within these groups can change the interactions among individuals and have strong effects on the function of animal groups, yet understanding of social hierarchy in the context of collective behavior is limited.
In this project, the team explores collective decision-making in an organism that forms stable, highly coordinated and socially stratified groups – the damselfish, Dascyllus marginatus. This is a tropical marine species that forms stable size-based social hierarchies of unrelated individuals in close association with branching coral species. I employ multi-camera imaging technology in order to track simultaneously the motion and behavior of each member of D. marginatus groups, in three dimensions, in the field (Red Sea, Israel). Using various stimuli, the team will explore the relationship between individual- and group-behavior in three ecologically relevant contexts: (A) Detection of potential threats (B) Individual and socially-mediated escape maneuvers and (C) Decision-making regarding emergence. Acquiring data from the videos constitutes a challenging computer vision problem. Interns can either be involved in developing programs to automate data acquisition, developing tools to visualize the data in three dimensions, or developing techniques for the actual analysis of the resulting data.
Qualifications: IIP candidates with interests in mathematics, physics, electrical engineering, computer science, operations research and financial engineering, mechanical and aerospace engineering, economics or ecology and evolutionary biology or related fields are encouraged to apply. Programming skills in python or similar are a strong positive. Interests in either machine learning and computer vision, high dimensional data analysis, and modeling, or the study of animal behavior are expected.
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