About us
How does the brain give rise to intelligent behavior, and how can we build machines that match its flexibility and efficiency? Our lab develops data-driven digital twins — deep learning models trained on large-scale neural, behavioral, and physiological recordings that faithfully replicate the input–output relationships of biological systems. These models serve as in silico laboratories: once trained, they can be interrogated, perturbed, and optimized to derive scientific insight that would be difficult or impossible to obtain from experiments alone. This approach connects our four research directions: we build predictive models of neural population activity, use them to uncover computational principles of the brain, extend the digital-twin methodology to bodies and behavior, and translate what we learn toward restoring lost biological function.
We are based at the University Göttingen. We closely collaborate with experimental neuroscientists and medical doctors to develop new tools and experimental paradigms to understand biological intelligence.
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Collaborators
- Tobias Rose (University Bonn)
- Katrin Franke (University Tübingen/Stanford University)
- Alireza Modirshanechi (Helmholtz Munich/MPI biol. Cyb. Tübingen)
- Tolias Lab (Stanford University)
- Ecker Lab (University Göttingen)
- Alexander Gail (Deutsches Primatenzentrum Göttingen)
- Jacob Reimer (Baylor College of Medicine)
- Leif Saager (Universitätsmedizin Göttingen)
Affiliations
