Before revision:  With fixed feedback weights the performance degrades quickly as the depth of the network increases  , when they are updated the performance is comparable to regular back-propagation .
After revision:  With fixed feedback weights the performance degrades quickly as the depth of the network increases . If the feedforward and feedback weights are initialized with the same values, as proposed in Zipser and Rumelhart (1990), they remain the same throughout training thus precisely implementing back-propagation. We show that even when the weights are initialized differently and at random, and the algorithm is no longer performing back-propagation , when they are updated the performance is comparable to regular back-propagation .