Our research is positioned at the interface between physics, applied mathematics, and neuroscience. One of our main targets is the development of innovative nonlinear time series analysis techniques. These techniques aim to characterize dynamical systems for which nonlinearities cause a complicated temporal evolution. A further main target of our work is the application of nonlinear time series techniques to electrophysiological recordings from the brain of epilepsy patients. We furthermore study coupled oscillator network models that show so-called chimera states.
Research seminars which summarize our work for non-specialists
2015: Why is our nonlinear time series analysis group not linear?
2018: Concepts and tools from nonlinear dynamics: Helpful to understand and diagnose epilepsy?