Learning Topology-Preserving Maps Using Self-Supervised Backpropagation

Self-supervised backpropagation is an unsupervised learning procedure for feedforward net­ works, where the desired output vector is identical with the input vector. For backpropagation, we are able to use powerful simulators running on parallel machines. Topology-preserving mapa, on the other band, can be developed by a variant of the competitive learning procedure. Bow­ ever, in a degenerate cue, self-supervlsed backpropagation is a version of competitive learning. A simple extension of the cost function of backpropagation leads to a competitive version of aelf'­ supervised backpropagation, which can be used to produce topographic mapa. We demonstrate the approach applied to the Traveling Salesman Problem (TSP).