Show simple item record Mario en_US Frank en_US Martin en_US
dc.contributor.editor A. en_US 2008-02-28T16:49:27Z 2008-02-28T16:49:27Z 2008-01 en_US
dc.identifier.citation Negrello , M , Pasemann , F & Hülse , M 2008 , ' Adaptive Neurodynamics ' . in A Yang (ed.) , S. Shan . IGI Global , pp. 82-108 . en_US
dc.identifier.other PURE: 75731 en_US
dc.identifier.other dspace: 2160/516 en_US
dc.description.abstract Neurodynamics is the application of dynamical systems theory (DST) to the analysis of the structure and function of recurrent neural networks (RNNs). In this chapter, we present RNNs artificially evolved for the control of autonomous robots (evolutionary robotics [ER]) and further analyzed within dynamical systems tenets (neurodynamics). We search for the characteristic dynamical entities (e.g., attractor landscapes) that arise from being-environment interactions that underpin the adaptation of animat's (biologically inspired robots). In that way, when an efficient controller is evolved, we are able to pinpoint the reasons for its success in terms of the dynamical characteristics of the evolved networks. The approach is exemplified with the dynamical analysis of an evolved network controller for a small robot that maximizes exploration, while controlling its energy reserves, by resorting to different periodic attractors. Contrasted to other approaches to the study of neural function, neurodynamics' edge results from causally traceable explanations of behavior, contraposed to just orrelations. We conclude with a short discussion about other approaches for artificial brain design, challenges, and future perspectives for neurodynamics. en_US
dc.format.extent 27 en_US
dc.publisher IGI Global en_US
dc.relation.ispartof S. Shan en_US
dc.title Adaptive Neurodynamics en_US
dc.contributor.pbl Department of Computer Science en_US
dc.contributor.pbl Intelligent Robotics Group en_US

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