Our goal is to accelerate scientific discovery using machine learning and artificial intelligence: We develop computational methods that help scientists interpret empirical data and use them to gain scientific insights.
We closely collaborate with experimental researchers from various disciplines. We are particularly interested in applications in the neurosciences: We build data-driven mechanistic models of neuronal functions in order to understand how neuronal networks in the brain process sensory information and control intelligent behaviour.
We are part of the Excellence Cluster Machine Learning Tübingen and the Tübingen AI Center. You can find out more about us on our lab website.
In addition to the repositories in this organization, (former) lab members have also developed the following toolboxes:
- sbi, a toolbox for simulation-based inference,
- DECODE, a deep learning tool for single molecule localization microscopy,
- sbibm, a benchmark for simulation-based inference,
- flyvis, a connectome constrained deep mechanistic network (DMN) model,
- Jaxley, a differentiable simulator for biophysical neuron models.