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Authoritative: http://dx.doi.org/10.1080/09540090500177638   (Publisher's PDF... likely be available here.)

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Abstract

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.

Keywords: Mental representations, Recurrent neural networks, Learning; Action-based model, Simple language

@article{Kuhn05recurrentNeuralNetworks,
  author={Simone Kuhn and Holk Cruse},
  title={Static mental representations in recurrent neural networks for the control of dynamic behavioural sequences},
  journal={Connection Science},
  year={2005},
  month={December},
  volume={17},
  number={3-4},
  pages={343-360},
  doi={10.1080/09540090500177638},
  url={http://www.isrl.uiuc.edu/~amag/langev/paper/Kuhn05recurrentNeuralNetworks.html},
  keywords={Mental representations, Recurrent neural networks, Learning; Action-based model, Simple language}
}