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About Bernstein Center for Computational Neuroscience (BCCN) Göttingen

When we are parsing a sentence, recognizing a face, or are recalling an episode from memory, the underlying neuronal processes are carried out by the dynamics of large and complex networks of neurons of our brains, for the analysis of which the neurosciences presently do not possess systematic methods or successful general paradigms. It is a generally accepted view that the successful approaches will need to integrate advanced mathematical modelling and analysis methods of network dynamics together with experimental recording and manipulation of large networks in vivo. While mathematical modelling has a long tradition e.g. in theoretical physics, in the neurosciences only now a new interdisciplinary generation of scientists is emerging that equally command biological competence and theoretical-mathematical skills, which are both considered necessary prerequisites to finding the desired new routes to the understanding of brain function.

In the Bernstein Center for Computational Neuroscience (BCCN) Göttingen, established in 2005, theoreticians with a long-standing involvement in neuroscience collaborate with advanced experimental groups in order to decipher the functioning of the central nervous system from single synapses to the brain. Moreover, based on parts of this research adaptive controllers for prosthetic limbs and data analysis methods for neuroprostheses are studied, evaluated, and prototyped in cooperation with the company Otto Bock HealthCare.

Please find out more on the research projects of the first and second funding period in the respective research section of the BCCN-website.

The coordinator of the BCCN Göttingen is Prof Wolf, Professor in the Department of Nonlinear Dynamics at the Max Planck Institute for Dynamics and Self-Organization.

All institutions participating in the Bernstein Center
and other Neuroscience research facilities can be reached within 10 minutes,
where the distances between most institutions are much shorter.