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.