Context Learning with the Self Organizing Map

In this paper a Recurrent Self-Organizing Map (RSOM) algorithm is proposed for temporal sequence processing. The RSOM algorithm is close in nature to the Kohonen's Self-Organizing Map, except that in the RSOM context of the temporal sequence is involved both in the best matching unit nding and in the adaptation of the weight vectors of the map via an introduced recursive di erence equation associated for each unit of the map. The experimental results in the paper demonstrate that the RSOM is able to learn and distinguish temporal sequences, and that the RSOM algorithm can be utilized, for instance, in electroencephalogram (EEG) based epileptic activity detection.