Learning topography in neural networks

  • Our brain is functionally organized in \(\textit {topographic structure}\): typically, nerve cells (neurons) that are anatomically close to each other are also functionally close to each other. The functions of individual neurons are not genetically determined, but instead result from \(\textit {learning processes}\) that enable the system to adapt to its environment. The learning of topography may be a significant reason for the \(\textit {amazing capabilities}\) of biological information processing systems. \(\textit {Learning topographies in the cerebral cortex}\) is the subject of this work. The following results are obtained: 1. From biological investigations well-known topographic structures can be learned stimulus-induced in natural environments, that is, with natural sensor information. 2. \(\textit {Temporal signal relations}\) are of significance for topographic structures. The work offers a \(\textit {new perspective}\) on cortical topography and dynamic signal coding. Moreover, it predicts \(\textit {further topographic structures}\) in the cerebral cortex.

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Metadaten
Author:Jan C. Wiemer
URN:urn:nbn:de:hbz:294-2741
Subtitle (English):towards a better understanding of cortical topography
Referee:Werner von SeelenGND, Christoph von der MalsburgGND
Document Type:Doctoral Thesis
Language:English
Date of Publication (online):2003/03/18
Date of first Publication:2003/03/18
Publishing Institution:Ruhr-Universität Bochum, Universitätsbibliothek
Granting Institution:Ruhr-Universität Bochum, Fakultät für Physik und Astronomie
Date of final exam:2000/12/20
Creating Corporation:Fakultät für Physik und Astronomie
GND-Keyword:Selbstorganisation; Lernen; Dynamik; Topographie; Großhirnrinde
Dewey Decimal Classification:Naturwissenschaften und Mathematik / Physik
Licence (German):License LogoKeine Creative Commons Lizenz - es gelten der Veröffentlichungsvertrag und das deutsche Urheberrecht

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