Multiobjective Optimization of Echo State Networks for multiple motor pattern learning

Echo State Networks are a special class of recurrent neural networks, that are well-suited for attractorbased learning of motor patterns. Using structural multiobjective optimization, the trade-off between network size and accuracy can be identified. This allows to choose a feasible model capacity for a follow-up full-weight optimization. It is shown to produce small and efficient networks, that are capable of storing multiple motor patterns in a single net. Especially the smaller networks can interpolate between learned patterns using bifurcation inputs.