Nonlinear Systems Laboratory

The Nonlinear Systems Laboratory studies general mathematical principles of nonlinear system stability, adaptation and learning, and how these principles apply to robots and models of biological control. Biological systems evolve through the accumulation and combination of simple modules through a series of stable intermediate states. We are developing mathematical techniques to describe, model and simulate these processes, focusing on the brain as a model system. We are interested in very large recursive systems that arise through the accumulation of simpler elements.

One focus of this work is stability, a key to biological survival, which is maintained in complex systems by non-linear mechanisms such as parallel and series signal processing and positive and negative feedback loops. A second focus is the phenomenon of stable emergence, in which many systems act together to produce a particular result that is more than the sum of its parts. The accumulation of individual components into large and complex but dynamically stable systems also is a recurrent theme in cybernetics, artificial intelligence, neurophysiology and biological motor control.