▁final ▁storage ▁area ▁The ▁new ▁pattern ▁is ▁learned ▁by ▁the ▁early ▁processing ▁area ▁by ▁the ▁standard ▁back prop ag ation ▁algorithm ▁At ▁the ▁same ▁time ▁random ▁input ▁is ▁also ▁fed ▁into ▁the ▁network ▁and ▁causes ▁pseud op at tern s ▁to ▁be ▁generated ▁by ▁the ▁final ▁storage ▁area ▁Output ▁from ▁the ▁final - storage ▁area , ▁in ▁the ▁form ▁of ▁pseud op at tern s , ▁will ▁be ▁used ▁as ▁a ▁teacher ▁for ▁the ▁early - processing ▁area . ▁In ▁this ▁way , ▁the ▁pseud op at tern s ▁are ▁inter le aved ▁with ▁the ▁' real ▁inputs ' ▁from ▁the ▁environment ▁ ▁Once ▁the ▁new ▁pattern ▁and ▁the ▁pseud op at tern ▁are ▁learned ▁by ▁the ▁early ▁processing ▁area , ▁its ▁weights ▁are ▁copied ▁to ▁the ▁corresponding ▁weights ▁in ▁the ▁final ▁storage ▁area . ▁ ▁When ▁tested ▁on ▁sequ ential ▁learning ▁of ▁real ▁world ▁patterns , ▁categor ization ▁of ▁ed ible ▁and ▁poison ous ▁m ush rooms , ▁the ▁pseudo - rec urrent ▁network ▁was ▁shown ▁less ▁inter ference ▁than ▁a ▁standard ▁back prop ag ation ▁network . ▁This ▁improvement ▁was ▁with ▁both ▁memory ▁sav ings ▁and ▁exact ▁recognition ▁of ▁old ▁patterns . ▁When ▁the ▁activation ▁patterns ▁of ▁the ▁pseudo - rec urrent ▁network ▁were ▁investig ated , ▁it ▁was ▁shown ▁that ▁this ▁network ▁automatically ▁formed ▁semi - dist ributed ▁representations . ▁Since ▁these ▁types ▁of ▁representations ▁involve ▁fewer ▁nodes ▁being ▁activ ated ▁for ▁each ▁pattern , ▁it ▁is ▁likely ▁what ▁helped ▁to ▁reduce ▁inter ference . ▁ ▁Not ▁only ▁did ▁the