Skeletonization ([MM89]) prunes units by estimating the change of the error function when the unit is removed (like OBS and OBD do for links). For each node, the attentional strength is introduced which leads to a different formula for the net input:
Figure 
illustrates the use of the attentional strength.
Figure: Neural network with attentional strength for each input and
hidden neuron
Defining the relevance of a unit as the change in the error function while removing the unit we get
In order to compute the saliency, the linear error function is used:
