Modelling memory functions with recurrent neural networks consisting of input compensation units: II. Dynamic situations

Kühn S, Cruse H (2007)
Biological Cybernetics 96(5): 471-486.

Zeitschriftenaufsatz | Veröffentlicht | Englisch
 
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DOI
Autor*in
Kühn, SimoneUniBi; Cruse, HolkUniBi
Einrichtung
Fakultät für Biologie > Biologische Kybernetik
Fakultät für Psychologie und Sportwissenschaft > Abteilung Sportwissenschaft
Erscheinungsjahr
2007
Zeitschriftentitel
Biological Cybernetics
Band
96
Ausgabe
5
Seite(n)
471-486
ISSN
0340-1200
eISSN
1432-0770
Page URI
https://pub.uni-bielefeld.de/record/1594061

Zitieren

Kühn S, Cruse H. Modelling memory functions with recurrent neural networks consisting of input compensation units: II. Dynamic situations. Biological Cybernetics. 2007;96(5):471-486.
Kühn, S., & Cruse, H. (2007). Modelling memory functions with recurrent neural networks consisting of input compensation units: II. Dynamic situations. Biological Cybernetics, 96(5), 471-486. https://doi.org/10.1007/s00422-006-0138-9
Kühn, Simone, and Cruse, Holk. 2007. “Modelling memory functions with recurrent neural networks consisting of input compensation units: II. Dynamic situations”. Biological Cybernetics 96 (5): 471-486.
Kühn, S., and Cruse, H. (2007). Modelling memory functions with recurrent neural networks consisting of input compensation units: II. Dynamic situations. Biological Cybernetics 96, 471-486.
Kühn, S., & Cruse, H., 2007. Modelling memory functions with recurrent neural networks consisting of input compensation units: II. Dynamic situations. Biological Cybernetics, 96(5), p 471-486.
S. Kühn and H. Cruse, “Modelling memory functions with recurrent neural networks consisting of input compensation units: II. Dynamic situations”, Biological Cybernetics, vol. 96, 2007, pp. 471-486.
Kühn, S., Cruse, H.: Modelling memory functions with recurrent neural networks consisting of input compensation units: II. Dynamic situations. Biological Cybernetics. 96, 471-486 (2007).
Kühn, Simone, and Cruse, Holk. “Modelling memory functions with recurrent neural networks consisting of input compensation units: II. Dynamic situations”. Biological Cybernetics 96.5 (2007): 471-486.

4 Zitationen in Europe PMC

Daten bereitgestellt von Europe PubMed Central.

Supervised spike-timing-dependent plasticity: a spatiotemporal neuronal learning rule for function approximation and decisions.
Franosch JM, Urban S, van Hemmen JL., Neural Comput 25(12), 2013
PMID: 24047322
Compact internal representation of dynamic situations: neural network implementing the causality principle.
Villacorta-Atienza JA, Velarde MG, Makarov VA., Biol Cybern 103(4), 2010
PMID: 20589508
Elements for a general memory structure: properties of recurrent neural networks used to form situation models.
Makarov VA, Song Y, Velarde MG, Hübner D, Cruse H., Biol Cybern 98(5), 2008
PMID: 18350312
Selforganizing memory: active learning of landmarks used for navigation.
Cruse H, Hübner D., Biol Cybern 99(3), 2008
PMID: 18797951

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