Networks must be
trained before they can provide correct solutions to problems. CC training
involves both adjusting connection weights and adding new hidden units. CC
networks begin without any hidden units. Trainable connection weights are
drawn in this slideshow as dashed arrows. Initially, these weights have
random values, generating random performance. Weights are adjusted to reduce
discrepancy (error) between the actual output vector and a target output
vector of correct activations. 

One of the units
at the input level in CC is called the bias unit because it always has
an input activation of 1.0, regardless of the input pattern being presented.
With trainable connection weights to all downstream units, the bias unit
effectively implements a learnable resting activation level for each hidden and
output unit. 