Computational Neurophysics

Research

For living and surviving in a complex environment, our brain has to constantly process large amounts of sensory signals and to integrate this information into meaningful percepts. Hereby processing must be highly adaptive and has to adjust rapidly to the actual situation, such as the current behavioural task, contextual information in the subjects’ environment, and internal states of the brain. It is one of the great puzzles in theoretical neuroscience how our brain orchestrates a modular network towards such changing goals, hereby optimally reallocating limited neuronal resources to the current computational needs.

In our lab, we aim to understand neural mechanisms and computational principles underlying flexible and robust information processing – with particular focus on the visual system. For our research, we employ different computational approaches comprising theoretical studies, modelling, numerical simulations, mathematical analysis, and application of tools from machine learning and AI research. Data analysis and development of novel data processing tools is done in collaboration with partner labs performing psychophysical and electrophysiological experiments in Bremen and abroad.

Our current research foci are on understanding the collective dynamics in recurrent neural networks and investigating flexible information processing and integration in modular systems. We actively pursue the transfer of knowledge from basic research into technical and medical applications in cooperation with engineers, physiologists, computer scientists and medical doctors (for example, for developing more efficient intracortical visual prostheses or for constructing adaptive intelligent machines).

The group started in 2010 and was initially funded by Bernstein Award in Computational Neuroscience provided by the BMBF for research on the topic 'Rapid Parallel Configuration of Visual Information Processing'. Our lab is part of the Theoretical Physics Department.