Units:

12

Description:

Outline: The world is filled with nonlinear dynamical systems requiring control, from climate and robotics to human physiology. Understanding and controlling these systems can lead to groundbreaking solutions, including improved treatments for complex conditions. This course focuses on concepts and techniques for analyzing and controlling dynamical systems, particularly in biomedical and neural domains. With a foundation in basic probability and statistics, linear algebra, and machine-learning, the students will explore system dynamics, both linear and nonlinear, using brain and brain-body interactions as key examples.

- A key part of the course will be projects, where students will work with simulated and real neural and physiological data. These projects will be weighted heavily in the grade.
- The students will apply techniques learned in the course to two neurostimulation problems.
- Pre-collected stimulation datasets will be shared on which students will iterate, resulting in new datasets (simulated and real) being created.
- Real data will be collected by the teaching team using human experiments (stimulation of peripheral or central nervous system) based on student-suggested sampling.
- Students will gain experience applying more traditional data-driven control techniques and comparing (or combining) them with data-driven AI.
- Integration of student interests: during bring-your-own-problem weeks, some students may discuss their own research problems

Goals: Concepts of dynamical systems, linear and nonlinear dynamical systems, system identification and control. Examplar dynamical systems in human bioscience, especially in brain dynamics and brain-body interactions. Examples of how treatment techniques interact with these systems and affect them, and how optimized intervention problems are formulated and solved. Application and refinement of techniques on simulated and real data.

Last Modified: 2024-11-22 4:08PM

Semesters offered:

• Spring 2025
• Fall 2014
• Fall 2013
• Fall 2012
• Fall 2011
• Fall 2010