If a second hidden unit is required, it is installed downstream of the first hidden unit.
After each hidden unit is recruited, training of the output weights resumes. Thus, these phases are known as output phases. Learning continues in this fashion until network error is reduced to the extent that all output units have activations within a certain range of their targets on all training patterns. That range is called score-threshold, a parameter that can be manipulated to control depth of learning.