Research at the BCCN Berlin
Research at the BCCN Berlin evolves from the relation between the precision of neural computations and significant neural variability. The apparent discrepancy between the reliable performance of the computing brain and the trial-to-trial variability of neural processes is also of direct clinical and biotechnological interest: Which brain signals need to be extracted if we want to infer the occurrence of a specific cognitive process, discriminate between a healthy and a diseased state, or optimize learning through EEG feedback - all on a single-trial basis and in real- time? Coherence amongst researchers contributing to the BCCN Berlin comes from a joint interest in discovering common computational principles by identifying similar problems across topics and scales of neuroscience.
The Center’s development of fMRI technology applications will broaden the competence in these areas. The combination of machine learning and psychophysics to obtain a general framework for feature identification of human observers is an important source of information for computational models of perceptual cognition. The Center is also pioneering in miniaturized technologies for intracellular recordings, a development from which a great number of neuroscientists will benefit. Machine learning and support vector machines are also key technologies with a spectrum of technical applications. In addition, machine learning algorithms are highly useful for a wide range of complex data analysis problems in the biomedical and neuroscience domains. Brain-machine interfaces is a mutually matching research area of the BCCN Berlin in cooperation with the Bernstein Focus Neurotechnology (BFNT).