In order to build closed-loop devices that read the status of a certain brain area to steer a prosthetic limb and provide feedback information from a sensor via stimulation, it is necessary to learn about the coding mechanism in the involved brain areas. To this end, we develop ways to predict body motion from neural recordings (decoding) and vice-versa (encoding) using latest deep learning methods. (Collaboration with Professor Thomas Brox, Computer Vision Laboratory, University of Freiburg, Germany and Dr Patrick Ruther, Department of Microsystems Engineering – IMTEK, University of Freiburg, Germany)
We apply artificial neural networks (Deep Learning) for automatic object recognition and precise behavior tracking. (Collaboration with Professor Thomas Brox, Computer Vision Laboratory, University of Freiburg, Germany)
Causal interference with the brain is one of the currently leading themes in basic and clinical neuroscience. For this, brain stimulation paradigms have been proven to be extremely valuable tools. We aim to develop paradigms which allow powerful interactions with the brain with clinical relevance. (Collaboration with Professor Carsten Mehring, Institute of Biology III & Bernstein Center Freiburg, University of Freiburg, Germany and Professor Thomas Stieglitz IMTEK - Institut für Mikrosystemtechnik, University of Freiburg, Germany)
How does the local organization of motor cortex look like in terms of encoding of different movement types? We address this question with a combination of experimental and computational tools.
What makes us start or stop a movement? Prefrontal areas are executively involved in this process. In this project, we investigate the impact of prefrontal input on motor cortex activity
What drives a movement locally? The role of different cell types are investigated.
How are different outputs of motor cortex organized and coordinated?