Self-Organizing Maps
Pattern computation in neural communication systems
Biological Cybernetics
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Cortical dynamics can be recorded in various ways. Theoretical works suggest that analyzing the dynamics of recorded activities might reveal the workings of the underlying neural system. Here we describe the extraction of an activity pattern language that characterizes the dynamics of high-resolution EEG data recorded. We show that the language can be formulated in terms of probabilistic continuation rules which predict reasonably well the dynamics of activity patterns in the data.