Echo State Networks are a special class of recurrent neural networks, that are well suited for attractor-based learning of motor patterns. Using structural multi-objective 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. Both optimization steps can be combined into a nested, hierarchical optimization procedure. 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.
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