News

September 2019

Our paper Inception loops discover what excites neurons most using deep predictive models got accepted in Nature Neuroscience.

August 2019

New biorxiv report on global orientation organization in mouse V1.

August 2019

Our perspective paper Engineering a less artificial intelligence got accepted at Neuron.

July 2019

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.

July 2019

Our Neuronal Likelihood paper was accepted for poster presentation at CCN 2019.

July 2019

Our Touch a Brain VR demo was featured in Uni Tübingen Campus TV

July 2019

We were awarded an Amazon AWS Machine Learning Research Award!


About us

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.



Collaborators



Affiliations


university tuebingen
cybervalley
bernstein center
baylor college of medicine