Learning Sequential Tasks by Incrementally Adding Higher Orders

An incremental, higher-order, non-recurrent network combines two properties found to be useful for learning sequential tasks: higherorder connectionsand incremental introduction of new units. The network adds higher orders when needed by adding new units that dynamically modify connection weights. Since the new units modify theweights at the next time-step with information from the previous step, temporal tasks can be learned without the use of feedback, thereby greatly simplifying training. Furthermore, a theoretically unlimitednumber of units can be added to reach into the arbitrarily distant past.