This workshop shall cover three main topics:

First, a general outline of problems occuring in computational neuroscience shall be given. Here, the connection between microscopic measurement and modeling to macroscopic observation shall be outlined.

Second, we discuss issues of decomposition techniques applied on fMRI and EEG/MEG data. A present trend in this area is to increase the tensorial order of the data representation which, at least in principle, allows for unique decomposition under fairly mild conditions unless the data are 'pathological'. A specific question here is whether real data are so close to being 'pathological' that the decomposition lacks robustness. More generally, decomposition methods like PCA, ICA, Parafac or the construction of general "dictionaries" make different kinds of assumptions. The question is which of these assumptions are met in real data and whether or not some assumptions are useful to make even if they are not met.

Third, a specific application of data analysis methods is the brain computer interface. In practice it appears that the most simple methods are surprisingly successful. One reason could be that uninteresting background noise is so complicated and diverse that ignoring the background as much as possible should have priority over interpreting details of the signal of interest. The respective priorities set the range of promising methods.

We shall discuss in this workshop the present experience with various methods and the most promising directions of research to improve the results.