Machine Learning

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Deep Neural Networks can challenge humans in complex perceptual tasks. At the same time, neural networks are extremely limited in terms of data efficiency, robustness to minimal perturbations and generalisation to distribution shifts. A major goal of the lab is to shed light on the inner workings of neural networks and to use these insights to rethink the foundations of Deep Learning.


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We are interested in how neural circuits perform the computations that mediate behavior. To tackle this question, we develop machine learning tools, statistical modeling approaches and data analytics solutions for making sense of large-scale high-dimensional neural and behavioral data acquired by our experimental collaborators.

Human Perception

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The human visual system allows us to effortlessly perceive complex properties of the world. While current machine vision systems have made amazing progress on specific tasks in recent years, they do not yet match the generality and robustness of biological vision. We compare how humans and machines see the world in order to better understand both systems.

Please also see our previous research interests


University of Tuebingen BCCN CIN MPI