exception of the lack of direct input-output connections, training proceeds
as in normal cascade-correlation. When error reduction stagnates, a hidden
unit is recruited. As the first hidden unit is added, its input weights are
frozen (shown in solid arrows), and training of the output weights resumes.
The network is growing as it learns.