Collaborative approach

We enjoy developing quantitative methods to understand data recorded by our collaborators and design models that predict new effects that can be tested. We believe in the development of new models and analysis techniques that draws on mathematics, physics and computer science and it drives our understanding of neural networks. A biological model can operate at different levels of abstraction, from micro scale of protein interaction to the macroscale of network dynamics. We work across network, synaptic and molecular level and build models that zoom-in on the question how neurons represent information and how they organize their global network dynamics to achieve a task. While we specialize in neuronal signal processing, we strive to make our models and methods applicable for other disciplines and hope to impact applications such as machine learning and brain-computer interfaces.