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

Institute of Theoretical Computer Science (7080)

Research output: Contribution to journal › Article › peer-review

Original language	English
Pages (from-to)	2979-3035
Journal	Neural Computation
Volume	22
Issue number	12
DOIs	https://doi.org/10.1162/NECO_a_00050
Publication status	Published - 2010

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Basic - Fundamental (Grundlagenforschung)
Theoretical

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10.1162/NECO_a_00050

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title = "A theoretical basis for emergent pattern discrimination in neural systems through slow feature extraction",

author = "Stefan Klampfl and Wolfgang Maass",

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AU - Klampfl, Stefan

AU - Maass, Wolfgang

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UR - http://www.mitpressjournals.org/doi/abs/10.1162/NECO_a_00050

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DO - 10.1162/NECO_a_00050

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EP - 3035

JO - Neural Computation

JF - Neural Computation

IS - 12

ER -

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

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Other files and links

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

Cite this

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

Treatment code (Nähere Zuordnung)

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Projects

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

Cite this