It might be useful to track the changes in a ANN during the learning process.
Certain algorithms for assessing the state of a SOFM can take a long time if the training dataset in very large.
If the state of the Network changes a lot (due to the continuing training process) during the evaluation computation, its output will not represent the state at some definite time.
Stopping the training process during the whole evaluation would result in a waste of time.
An alternative is to create a deep copy of the Network instance: the evaluation will be performed on the copy while the training can continue on the original instance.