My research interests focus on the investigation of noise-signal-relationships and I aim to understand how signal can be utilized while the nervous system is sensitive to random perturbations.
My approach is to develop analytical tools and theoretical understanding of the dynamical states in neural networks and systemic models that mimic aspects of the dynamics and architecture of local neuronal populations in the nervous system. These theoretical studies are based on statistical models, the theory of stochastic systems in statistical physics, and the mathematics of non-linear dynamical systems. I then extend the theoretical understanding by numerical simulations of biologically realistic network models. In my work, both theoretical motives and their application for neuronal data analysis are intermingled and are used for providing experimentally testable predictions.