A theoretical basis for emergent pattern discrimination in neural systems through slow feature extraction

Stefan Klampfl; Wolfgang Maass

doi:10.1162/NECO_a_00050

A theoretical basis for emergent pattern discrimination in neural systems through slow feature extraction

Institut für Grundlagen der Informationsverarbeitung (7080)

Publikation: Beitrag in einer Fachzeitschrift › Artikel › Begutachtung

Originalsprache	englisch
Seiten (von - bis)	2979-3035
Fachzeitschrift	Neural Computation
Jahrgang	22
Ausgabenummer	12
DOIs	https://doi.org/10.1162/NECO_a_00050
Publikationsstatus	Veröffentlicht - 2010

Treatment code (Nähere Zuordnung)

Basic - Fundamental (Grundlagenforschung)
Theoretical

Zugriff auf Dokument

10.1162/NECO_a_00050

202.pdfEndgültige, publizierte Fassung, 2,81 MB

Andere Dateien und Links

ORGANIC - Self-organized recurrent neural learning for language processing
Maass, W.
1/04/09 → 31/03/12
Projekt: Forschungsprojekt
EU SECO - Self-Constructing Computing Systems
Pfeiffer, M. & Maass, W.
1/03/08 → 29/02/12
Projekt: Forschungsprojekt
EU - FACETS - Fast Analog Computing with Emergent Transient States in Neural Architecture
Pecevski, D., Klampfl, S., Maass, W. & Mills, A.
1/09/05 → 31/08/09
Projekt: Forschungsprojekt

Dieses zitieren

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A theoretical basis for emergent pattern discrimination in neural systems through slow feature extraction

Treatment code (Nähere Zuordnung)

Zugriff auf Dokument

Andere Dateien und Links

Projekte

ORGANIC - Self-organized recurrent neural learning for language processing

EU SECO - Self-Constructing Computing Systems

EU - FACETS - Fast Analog Computing with Emergent Transient States in Neural Architecture

Dieses zitieren