PHYS-727 / 2 credits

Teacher(s): Berman Gordon, Rahi Sahand Jamal, Stephens Greg

Language: English


Frequency

Only this year

Summary

This doctoral class will focus on high-dimensional multi-agent behavioral data, modeled based on first principles or with statistical methods.

Content

The behavior of animals, as individuals and in groups, represents some of the most fascinating phenomena in the living world. Yet, its complexity challenges our ability to understand, model, and mimic. What rules govern the complex dynamics of an animal as it seeks out food, attracts a mate, or challenges a predator? Topics in this realm vary from quantification and modeling of individuals in controlled laboratory settings, to systems of flocking birds and schooling fish performing natural collective behavior, to the intricate interplay between small groups of individuals during activities such as courtship and aggression. In the past decades, significant progress has been made in quantifying what animals do and modeling their dynamics. We will study behavior and social interactions across biological systems. The primary goals are to learn the modeling approaches that are currently used, identify key questions and organizing principles in the nascent field of the Physics of Behavior, and encourage new directions over the coming years. We will draw from a diverse range of efforts to address these challenges, including experimental work as well as theoretical, computational, and robotic models.

Note

Host: Prof. Sahand Rahi

Keywords

Behavior, phenomenological models

Learning Prerequisites

Recommended courses

Dynamical systems, statistical methods and models

Learning Outcomes

By the end of the course, the student must be able to:

  • Ability to recognize and apply statistical dynamical models to behavioral data

Resources

Moodle Link

In the programs

  • Number of places: 30
  • Exam form: Project report (session free)
  • Subject examined: Physics of Behavior
  • Lecture: 16 Hour(s)
  • Exercises: 10 Hour(s)
  • Type: optional

Reference week

Related courses

Results from graphsearch.epfl.ch.