 |
| Research article summary (published 29 Apr 2003): |
The time-organized map algorithm: extending the self-organizing map to spatiotemporal signals.
Full Abstract
The new time-organized map (TOM) is presented for a better understanding of the self-organization and geometric structure of cortical signal representations. The algorithm extends the common self-organizing map (SOM) from the processing of purely spatial signals to the processing of spatiotemporal signals. The main additional idea of the TOM compared with the SOM is the functionally reasonable transfer of temporal signal distances into spatial signal distances in topographic neural representations. This is achieved by neural dynamics of propagating waves, allowing current and former signals to interact spatiotemporally in the neural network. Within a biologically plausible framework, the TOM algorithm (1) reveals how dynamic neural networks can self-organize to embed spatial signals in temporal context in order to realize functional meaningful invariances, (2) predicts time-organized representational structures in cortical areas representing signals with systematic temporal relation, and (3) suggests that the strength with which signals interact in the cortex determines the type of signal topology realized in topographic maps (e.g., spatially or temporally defined signal topology). Moreover, the TOM algorithm supports the explanation of topographic reorganizations based on time-to-space transformations (Wiemer, Spengler, Joublin, Stagge, & Wacquant, 2000).
Learn Faster Today Improve your study skills
Author information
Author/s: Wiemer, Jan C (JC);
Affiliation: Institut für Neuroinformatik, Ruhr-Universität Bochum, Germany. j.wiemer@dkfz.de
Journal and publication information
Publication Type: Journal Article; Research Support, Non-U.S. Gov't
Journal: Neural computation (Neural Comput), published in United States. (Language: eng)
Reference: 2003-May; vol 15 (issue 5) : pp 1143-71
Dates: Created 2003/06/13; Completed 2003/07/17; Revised 2006/11/15;
PMID: 12803960, status: MEDLINE (last retrieval date: 11/6/2008)
Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.
External Links for this article (including full text providers, if available):
Click Electronic Full-text Provider Links to see options for finding the electronic full text links to this article. Note there may be a subscription or fee required for access to the full text. See our FAQ for information on finding FREE full text articles.
This article may also be located in paper journal collections available in many libraries. Use the Journal and Publication Information above to find the full article.
MeSH headings (categories)
This article was linked to the MESH Headings shown below.
|
|
Related articles
This article has not been indexed for related articles as yet, however you can still use the live related article search links below.
See a large map of 100+ related articles.