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.

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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.
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.
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4 Citations in Europe PMC

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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, Hubner D, Cruse H., Biol Cybern 98(5), 2008
PMID: 18350312

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