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

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Abstract

Modelling the cognitive abilities of humans or animals or building agents that are supposed to behave cognitively requires modelling a memory system that is able to store and retrieve various contents. The content to be stored is assumed to comprise information about more or less invariant environmental objects as well as information about movements. A combination of information about both objects and movements may be called a situation model. Here we focus, in part, on models storing dynamic patterns. In particular, two abilities of humans in representing dynamical systems receive special focus: the capability of representing the acceleration of objects, as can be found in the movement of a pendulum or freely falling objects, and the capability of representing actions of transfer, i.e. motion from one point to another, have been modelled using recurrent networks consisting of input compensation units. In addition, possibilities of combining static and dynamic properties within a single model are studied.

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  1. Department of Biological Cybernetics, Faculty of Biology, University of Bielefeld, Bielefeld, 33501, Germany

    Simone Kühn & Holk Cruse

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  1. Simone Kühn
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  2. Holk Cruse
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Correspondence to Holk Cruse.

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Kühn, S., Cruse, H. Modelling memory functions with recurrent neural networks consisting of input compensation units: II. Dynamic situations. Biol Cybern 96, 471–486 (2007). https://doi.org/10.1007/s00422-006-0138-9

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  • DOI: https://doi.org/10.1007/s00422-006-0138-9

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