Our paper Inception loops discover what excites neurons most using deep predictive models got accepted in Nature Neuroscience.
New biorxiv report on global orientation organization in mouse V1.
Our perspective paper Engineering a less artificial intelligence got accepted at Neuron.
We are hiring more people. Looking for a PostDoc for ML and clinical neuroscience, and a PhD student for inductive bias transfer in artificial neuronal networks. Please check our open positions.
Our Neuronal Likelihood paper was accepted for poster presentation at CCN 2019.
Our Touch a Brain VR demo was featured in Uni Tübingen Campus TV
We were awarded an Amazon AWS Machine Learning Research Award!
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 will are based at 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.
- Tolias Lab (Baylor College of Medicine, Rice University)
- Alex Ecker (Bethge Lab) (University Tübingen)
- Jan Grewe (Neuroethology) (University Tübingen)
- Xaq Pitkow (Baylor College of Medicine, Rice University)
- Aristides Arrenberg (University Tübingen)
- Thomas Gasser (Hertie Institute for Clinical Brain Science)
- Holger Lerche (Hertie Institute for Clinical Brain Science)
- Hendrikje Nienborg (Centre for Intergrative Neuroscience; University Tübingen)