Computing with activities II: ruling robots by the activity patterns of hierarchical sums

In this paper a first example is given, in which way robots can be steered by global activity patterns of self-organizing maps (SOMs). In the context of the "Computing with Activities" a hierarchical neural network structure was trained which codes the diverse situations of (simulated) soccer playing robots not by a winner neuron but by the whole activity structure of the SOMs.