Static mental representations in recurrent neural networks for the control of dynamic behavioural sequences

Kühn S, Cruse H (2005)
Connection Science 17(3-4): 343-360.

Download
Es wurde kein Volltext hochgeladen. Nur Publikationsnachweis!
Zeitschriftenaufsatz | Veröffentlicht | Englisch
Abstract / Bemerkung
What enables an organism to perform behaviour we would call cognitive and adaptive, like language? Here, it is argued that an essential prerequisite is the ability to build up mental representations of external situations to uncouple the behaviour from direct environmental control. Such representations can be realized by building up cell assemblies. The recurrent neural network presented to cope with this task has been used for generation of action but can also be utilized as a basis for mental representations due to its attractor characteristics. In this context, a new learning algorithm (Dynamic Delta Rule) is proposed, which leads to a self-organized weight distribution yielding stable states on the one hand and which, on the other hand, only activates subpopulations of larger networks that code for the respective situation. In a second step, ways are shown of how the static information of these internal models can be transformed into time-dependent behavioural sequences
Erscheinungsjahr
Zeitschriftentitel
Connection Science
Band
17
Zeitschriftennummer
3-4
Seite
343-360
ISSN
eISSN
PUB-ID

Zitieren

Kühn S, Cruse H. Static mental representations in recurrent neural networks for the control of dynamic behavioural sequences. Connection Science. 2005;17(3-4):343-360.
Kühn, S., & Cruse, H. (2005). Static mental representations in recurrent neural networks for the control of dynamic behavioural sequences. Connection Science, 17(3-4), 343-360. doi:10.1080/09540090500177638
Kühn, S., and Cruse, H. (2005). Static mental representations in recurrent neural networks for the control of dynamic behavioural sequences. Connection Science 17, 343-360.
Kühn, S., & Cruse, H., 2005. Static mental representations in recurrent neural networks for the control of dynamic behavioural sequences. Connection Science, 17(3-4), p 343-360.
S. Kühn and H. Cruse, “Static mental representations in recurrent neural networks for the control of dynamic behavioural sequences”, Connection Science, vol. 17, 2005, pp. 343-360.
Kühn, S., Cruse, H.: Static mental representations in recurrent neural networks for the control of dynamic behavioural sequences. Connection Science. 17, 343-360 (2005).
Kühn, Simone, and Cruse, Holk. “Static mental representations in recurrent neural networks for the control of dynamic behavioural sequences”. Connection Science 17.3-4 (2005): 343-360.