Motor primitive and sequence self-organization in a hierarchical recurrent neural network.

This study describes how complex goal-directed behavior can be obtained through adaptation processes in a hierarchically organized recurrent neural network using a genetic algorithm (GA). Our experiments, using a simulated Khepera robot, showed that different types of dynamic structures self-organize in the lower and higher levels of the 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 non-linear dynamical systems. The biological plausibility of the model's essential principles is discussed.