We bring together a diverse group of researchers who share the common belief that theory and computational methods are an integral part of neuroscience research at all levels - from behavior, to circuits, cells and molecules. One particular focus is to bring machine learning research and neuroscience closer together. We use and develop machine learning approaches for analyzing the vast and complex datasets that are an integral part of modern neuroscience today. In addition, we use artificial neural systems as model systems for understanding what particular feats biological systems have evolved to achieve.
Currently, the center conducts research in the following areas:
- Neural data analysis and machine learning for neuroscience
- Robust sensory processing in biological and artificial systems
- Quantitative psychophysics and computational psychiatry
- Neuroprosthetics and computational motor control
- Optimality principles in neuroscience
BMBF funding (2010-2017)
The Bernstein Center for Computational Neuroscience was funded in 2010 thanks to generous funding provided by the German Ministry of Science and Education through the Bernstein Initiative. The goal of this initial phase of the center was to establish lasting structures in computational neuroscience in Tübingen and build a lively community bridging the gap between experimental neuroscience and machine learning.
In future, three projects at the University of Tübingen will be funded by the Neurobotics research programme of the Baden-Württemberg Foundation. The foundation is providing a total of four million euros to support research ideas from the fields of Neurotechnology, Neuronal Computing and Neuronally-inspired Robotics in order to do justice to the increasing importance of these fields.
CRC 1233 "Robust Vision"
The DFG-funded collaborative research center “Robust Vision – Inference Principles and Neural Mechanisms” (CRC 1233) studies basic principles of biological and machine vision, and is a close collaboration between scientists from neuroscience and computational vision.
Although neuroscience has inspired many elements of artificial neuronal networks, the mammalian visual system is still markedly different from current state-of-the-art deep neural networks in terms of its circuit architecture, robustness, and ability to learn. Two groups at the Bernstein center (Bethge, Sinz) are part of a multi-university consortium funded by the MICrONs program within the Obama BRAINinitiative.
switchBoard is an Innovative Training Network (ITN) funded by the European Commission's Horizon 2020 programme under the Marie Curie Actions 2015-2019 involving several of the BCCN-associated groups.