Encoder networks can implement a kind of recognition memory, as would be appropriate for simulation of habituation and familiarization studies. If an encoder network can learn to encode a stimulus onto a small number of hidden units and then decode this hidden unit representation onto its output units with very little error, then it has recognized the stimulus as familiar.
	The essential change from ordinary cascade-correlation (CC) is the elimination of any direct input-to-output unit connection weights. If such direct connections are retained, then learning an encoder problem is trivial, requiring only a weight of 1.0 between each input unit and its corresponding output unit. With such a trivial solution, a network learns nothing useful that could enable such phenomena as pattern completion or prototype abstraction.