Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Self-Organizing neural networks
Modelling Speech Processing and Recognition in the Auditory System with a Three-Stage Architecture
ICANN 96 Proceedings of the 1996 International Conference on Artificial Neural Networks
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
IEEE Transactions on Information Technology in Biomedicine
Node merging in Kohonen's self-organizing mapping of fMRI data
Artificial Intelligence in Medicine
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In the analysis of biological data artificial neural networks are a useful alternative to conventional statistical methods. Because of its advantage in analyzing time courses the Multilevel Hypermap Architecture (MHA) is used for analysis of stimulus related data, exemplified by fMRI studies with auditory stimuli. Results from investigations with the MHA show an improvement of discrimination in comparison to statistical methods. With an interface to the well known BrainVoyager software and with a GUI for MATLAB an easy usability of the MHA and a good visualization of the results is possible. The MHA is an extension of the Hypermap introduced by Kohonen. By means of the MHA it is possible to analyze structured or hierarchical data (data with priorities, data with context, time series, data with varying exactness), which is difficult or impossible to do with known self-organizing maps so far.