We use local input representation. A feedforward neural net cannot learn the problem, due to the arbitrary time lags that may occur. Instead, we need something like a recurrent net (e.g.          ).
A conventional fully recurrent net with 5 input units, 5 hidden units, 1 output unit, 1 ``bias unit'', and a learning rate of 0.5, needed less than 10000 training sequences to come up with correct classifications in 100% of all test sequences. (class 1: output activation , class 2: output activation at the end of a sequence).
This sets the stage for the more difficult task described in the next subsection. It involves highly redundant sequences and will be harder to learn.