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GECCO
2004
Springer

Evolved Motor Primitives and Sequences in a Hierarchical Recurrent Neural Network

10 years 11 months ago
Evolved Motor Primitives and Sequences in a Hierarchical Recurrent Neural Network
This study describes how complex goal-directed behavior can evolve in a hierarchically organized recurrent neural network controlling a simulated Khepera robot. Different types of dynamic structures self-organize in the lower and higher levels of a network for the purpose of achieving complex navigation tasks. The parametric bifurcation structures that appear in the lower level explain the mechanism of how behavior primitives are switched in a top-down way. In the higher level, a topologically ordered mapping of initial cell activation states to motor-primitive sequences self-organizes by utilizing the initial sensitivity characteristics of nonlinear dynamical systems. A further experiment tests the evolved controller’s adaptability to changes in its environment. The biological plausibility of the model’s essential principles is discussed.
Rainer W. Paine, Jun Tani
Added 01 Jul 2010
Updated 01 Jul 2010
Type Conference
Year 2004
Where GECCO
Authors Rainer W. Paine, Jun Tani
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