TY - CHAP
T1 - Adaptive Neurodynamics
AU - Negrello, Mario
AU - Pasemann, Frank
AU - Hülse, Martin
N1 - Negrello, M., Huelse, M., Pasemann, F.: Adaptive Neurodynamics. In: S. Shan, A. Yang (Eds.) Applications of Complex Adaptive Systems, IGI Global Publishing, USA, 82-108, 2008.
PY - 2008/1
Y1 - 2008/1
N2 - 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.
AB - 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.
M3 - Chapter
SP - 82
EP - 108
BT - S. Shan
A2 - Yang, A.
PB - IGI Global
ER -