Modelling memory functions with recurrent neural networks consisting of input compensation units: I. Static situations

Kühn S, Beyn W-J, Cruse H (2007)
Biological Cybernetics 96(5): 455-470.

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Zeitschriftenaufsatz | Veröffentlicht | Englisch
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Abstract / Bemerkung
Humans are able to form internal representations of the information they process?_"a capability which enables them to perform many different memory tasks. Therefore, the neural system has to learn somehow to represent aspects of the environmental situation; this process is assumed to be based on synaptic changes. The situations to be represented are various as for example different types of static patterns but also dynamic scenes. How are neural networks consisting of mutually connected neurons capable of performing such tasks? Here we propose a new neuronal structure for artificial neurons. This structure allows one to disentangle the dynamics of the recurrent connectivity from the dynamics induced by synaptic changes due to the learning processes. The error signal is computed locally within the individual neuron. Thus, online learning is possible without any additional structures. Recurrent neural networks equipped with these computational units cope with different memory tasks. Examples illustrate how information is extracted from environmental situations comprising fixed patterns to produce sustained activity and to deal with simple algebraic relations
Erscheinungsjahr
Zeitschriftentitel
Biological Cybernetics
Band
96
Ausgabe
5
Seite(n)
455-470
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eISSN
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Kühn S, Beyn W-J, Cruse H. Modelling memory functions with recurrent neural networks consisting of input compensation units: I. Static situations. Biological Cybernetics. 2007;96(5):455-470.
Kühn, S., Beyn, W. - J., & Cruse, H. (2007). Modelling memory functions with recurrent neural networks consisting of input compensation units: I. Static situations. Biological Cybernetics, 96(5), 455-470. doi:10.1007/s00422-006-0137-x
Kühn, S., Beyn, W. - J., and Cruse, H. (2007). Modelling memory functions with recurrent neural networks consisting of input compensation units: I. Static situations. Biological Cybernetics 96, 455-470.
Kühn, S., Beyn, W.-J., & Cruse, H., 2007. Modelling memory functions with recurrent neural networks consisting of input compensation units: I. Static situations. Biological Cybernetics, 96(5), p 455-470.
S. Kühn, W.-J. Beyn, and H. Cruse, “Modelling memory functions with recurrent neural networks consisting of input compensation units: I. Static situations”, Biological Cybernetics, vol. 96, 2007, pp. 455-470.
Kühn, S., Beyn, W.-J., Cruse, H.: Modelling memory functions with recurrent neural networks consisting of input compensation units: I. Static situations. Biological Cybernetics. 96, 455-470 (2007).
Kühn, Simone, Beyn, Wolf-Jürgen, and Cruse, Holk. “Modelling memory functions with recurrent neural networks consisting of input compensation units: I. Static situations”. Biological Cybernetics 96.5 (2007): 455-470.

6 Zitationen in Europe PMC

Daten bereitgestellt von Europe PubMed Central.

Neural network architecture for cognitive navigation in dynamic environments.
Villacorta-Atienza JA, Makarov VA., IEEE Trans Neural Netw Learn Syst 24(12), 2013
PMID: 24805224
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Cruse H, Wehner R., PLoS Comput Biol 7(3), 2011
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Villacorta-Atienza JA, Velarde MG, Makarov VA., Biol Cybern 103(4), 2010
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Makarov VA, Song Y, Velarde MG, Hübner D, Cruse H., Biol Cybern 98(5), 2008
PMID: 18350312
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PMID: 18797951

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