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. © Springer-Verlag Berlin Heidelberg 2004.
CITATION STYLE
Paine, R. W., & Tani, J. (2004). Evolved motor primitives and sequences in a hierarchical recurrent neural network. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3102, 603–614. https://doi.org/10.1007/978-3-540-24854-5_63
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