The time-organized map algorithm: extending the self-organizing map to spatiotemporal signals.
Neural Comput
; 15(5): 1143-71, 2003 May.
Article
em En
| MEDLINE
| ID: mdl-12803960
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).
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Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Algoritmos
/
Córtex Cerebral
/
Redes Neurais de Computação
Tipo de estudo:
Prognostic_studies
Limite:
Humans
Idioma:
En
Revista:
Neural Comput
Assunto da revista:
INFORMATICA MEDICA
Ano de publicação:
2003
Tipo de documento:
Article
País de afiliação:
Alemanha