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.
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.