A second hidden unit, if required, is installed downstream of the first hidden unit. After each hidden unit is recruited, training of output weights resumes.
	With a relatively small number of hidden units, an encoder network is forced to achieve a relatively compact, and thus abstract, representation of the inputs. Inputs are encoded onto this abstract hidden-unit representation using input-side weights. The hidden unit representation is then decoded onto the output units using output-side weights. Because the discrepancy between input and output activations constitutes network error, there is a sense in which encoder networks do not require any external feedback other than the training inputs.