Self–organized maps of sensory events

Over the years, many divergent meanings have been associated with the term ‘self–organization’, e.g. automatic creation of structured systems and optimization of parameters in adaptive learning. In this paper, we shall discuss a special type of data–driven self–organization, namely, automatic formation of ordered, compressed representations of sensory events. Such ordered and organized representations of an organism's experiences and environment exist in the nervous systems, where specific feature–sensitive information–processing functions are usually associated with these representations. As a matter of fact, three types of neuronal organization called ‘brain maps’ can be distinguished: sets of feature–sensitive cells, ordered projections between neuronal layers, and ordered maps of abstract features, respectively. The latter are most intriguing as they may also reflect quite abstract properties of the input data in an orderly fashion. It is proposed that such ‘maps’ are learned in a process that involves competition between sets of neural cells on common input data, and sensitization or tuning of the most strongly responding cells and their local neighbours to this input. While serving as a model for brain maps, the ‘self–organizing map’ principle has been used as an analytical tool in exploratory data analysis. In the latter, it has had practical applications ranging from industrial process control to marketing analyses, and from linguistics to bioinformatics.