Evolved motor primitives and sequences in a hierarchical recurrent neural network

Abstract. 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 ex-plain the mechanism of how behavior primitives are switched in a top-down way. In the higher level, a topologically ordered mapping of initial cell activa-tion states to motor-primitive sequences self-organizes by utilizing the initial sensitivity characteristics of nonlinear dynamical systems. A further experi-ment tests the evolved controller’s adaptability to changes in its environment. The biological plausibility of the model’s essential principles is discussed.