Despite huge advances in artificial intelligence (AI), the mammalian brain is still unrivaled in terms of sustainability and speed of learning, and robustness in inference. One central goal of AI research is to build intelligent systems that exceed the capabilities of biological brains. However, to date we know very little about how computations in neuronal circuits give rise to biological intelligence.
Our group uses AI both as a testbed and a tool on large scale neuro-physiological and -anatomical data to better understand the constituent elements of neuronal intelligence. We are inspired by the idea that a deeper understanding of computational motifs in cortical circuits can help build the next generation of intelligent systems.
We are based at the University Göttingen and the University Tübingen as part of the Cybervalley initiative. We closely collaborate with experimental and computational neuroscientists to develop new tools and experimental paradigms to discover principles of biological intelligence.
On Tuesday, Mohammad successfully defended his thesis. Congratulations Dr. Bashiri!
A special episode about our workshop “How can machine learning be used to generate insights and theories in neuroscience?” is now online. Thanks for Paul Middlebrooks for moderating the panel discussion and making it into a podcast episode. The workshop was joint work with the Ecker lab. Thanks for Mohammad, Michaela, and Pavi for leading the effort!
Our paper “Energy Guided Diffusion for Generating Neurally Exciting Images” was accepted at NeurIPS 2023. Joint effort with many contributions from the lab, led by Pawel.
- Tolias Lab (Baylor College of Medicine, Rice University)
- Alex Ecker (University Göttingen)
- Katrin Franke (University Tübingen)
- Kathrin Brockmann (University Tübingen)
- Anne-Christin Hauschild (Universitätsmedizin Göttingen)
- Leif Saager (Universitätsmedizin Göttingen)
- Günther Hahn (Universitätsmedizin Göttingen)
- Alexander Gail (Deutsches Primatenzentrum Göttingen)