Sequence Learning with Incremental Higher-Order Neural Networks, University of Texas at Austin AI lab technical report, 1993.

An incremental, higher-order, non-recurrent neural-network combines two properties found to be useful for sequence learning in neural-networks: higher-order connections and the incremental introduction of new units.  The incremental, higher-order neural-network adds higher orders when needed by adding new units that dynamically modify connection weights.  The new units modify the weights at the next time-step with information from the previous step. Since a theoretically unlimited number of units can be added to the network, information from the arbitrarily distant past can be brought to bear on each prediction. Temporal tasks can thereby be learned without the use of feedback, in contrast to recurrent neural-networks.  Because there are no recurrent connections, training is simple and fast. Experiments have demonstrated speedups of two orders of magnitude over recurrent networks.