Self-organized maps of sensory events.

Abstract

Restricted accessMoreSectionsView PDF ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinked InRedditEmail Cite this article Kohonen Teuvo 2003Self–organized maps of sensory eventsPhil. Trans. R. Soc. A.3611177–1186http://doi.org/10.1098/rsta.2003.1192SectionRestricted accessSelf–organized maps of sensory events Teuvo Kohonen Teuvo Kohonen Helsinki University of Technology, Neural Networks Research Centre, PO Box 5400, 02015 HUT, Finland Google Scholar Find this author on PubMed Search for more papers by this author Teuvo Kohonen Teuvo Kohonen Helsinki University of Technology, Neural Networks Research Centre, PO Box 5400, 02015 HUT, Finland Google Scholar Find this author on PubMed Search for more papers by this author Published:06 May 2003https://doi.org/10.1098/rsta.2003.1192AbstractOver 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. Previous ArticleNext Article VIEW FULL TEXT DOWNLOAD PDF FiguresRelatedReferencesDetailsCited by Thill S, Caligiore D, Borghi A, Ziemke T and Baldassarre G (2013) Theories and computational models of affordance and mirror systems: An integrative review, Neuroscience & Biobehavioral Reviews, 10.1016/j.neubiorev.2013.01.012, 37:3, (491-521), Online publication date: 1-Mar-2013. Baldassarre G, Caligiore D and Mannella F (2013) The Hierarchical Organisation of Cortical and Basal-Ganglia Systems: A Computationally-Informed Review and Integrated Hypothesis Computational and Robotic Models of the Hierarchical Organization of Behavior, 10.1007/978-3-642-39875-9_11, (237-270), . 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This Issue15 June 2003Volume 361Issue 1807Theme Issue ‘Self–organization: the quest for the origin and evolution of structure’ compiled by J. Skår and P. V. Coveney Article InformationDOI:https://doi.org/10.1098/rsta.2003.1192Published by:Royal SocietyPrint ISSN:1364-503XOnline ISSN:1471-2962History: Published online06/05/2003Published in print15/06/2003 License: Citations and impact Keywordsself–organizing mapneural networkbrain modelrepresentation of sensory eventsdata–driven self–organizationdata mining