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Inductive Modelling of Temporal Sequences by Means of Self-organization

In this paper we present a new self-organizing neural network, which builds a spatiotemporal model of an input temporal sequence inductively. The network is an extension of Kohonen’s Self-organizing Map with a modified Hebb’s rule for update of temporal synapses. The model building behavior is shown on inductive learning of a transition matrix from a data generated by a Markov Chain.