9 9 7 ) ▁have ▁also ▁proposed ▁a ▁two - network ▁artificial ▁neural ▁architecture ▁with ▁memory ▁self - ref res hing ▁that ▁over comes ▁cat ast roph ic ▁inter ference ▁when ▁sequ ential ▁learning ▁tasks ▁are ▁carried ▁out ▁in ▁distributed ▁networks ▁trained ▁by ▁back prop ag ation . ▁The ▁principle ▁is ▁to ▁inter le ave , ▁at ▁the ▁time ▁when ▁new ▁external ▁patterns ▁are ▁learned , ▁those ▁to - be - lear ned ▁new ▁external ▁patterns ▁with ▁internally ▁generated ▁pseud op at tern s , ▁or ▁' pse udo - mem ories ', ▁that ▁reflect ▁the ▁previously ▁learned ▁information . ▁What ▁mainly ▁distingu ishes ▁this ▁model ▁from ▁those ▁that ▁use ▁classical ▁pseud ore he ars al ▁in ▁feed forward ▁mult il ayer ▁networks ▁is ▁a ▁rever ber ating ▁process ▁that ▁is ▁used ▁for ▁generating ▁pseud op at tern s . ▁This ▁process ▁which , ▁after ▁a ▁number ▁of ▁activity ▁re - in jections ▁from ▁a ▁single ▁random ▁seed , ▁tends ▁to ▁go ▁up ▁to ▁non linear ▁network ▁attract ors , ▁is ▁more ▁suitable ▁for ▁optim ally ▁capt uring ▁the ▁deep ▁structure ▁of ▁previously ▁learned ▁knowledge ▁than ▁a ▁single ▁feed forward ▁pass ▁of ▁random ▁activation . ▁Ans ▁and ▁R ous set ▁( 2 0 0 0 ) ▁have ▁shown ▁that ▁the ▁learning ▁mechanism ▁they ▁proposed ▁avoid ing ▁cat ast roph ic ▁forget ting , ▁provides ▁a ▁more ▁appropriate ▁way ▁to ▁deal ▁with ▁knowledge ▁transfer ▁as ▁measured ▁by ▁learning ▁speed , ▁ability ▁to ▁general ize ▁and ▁vulner ability ▁to ▁network ▁dam ages . ▁Mus ca