▁of ▁the ▁learning ▁rate ▁parameter , ▁over training ▁on ▁the ▁A - B ▁list , ▁free zing ▁certain ▁connection ▁weights , ▁changing ▁target ▁values ▁ 0 ▁and ▁ 1 ▁instead ▁ 0 . 1 ▁and ▁ 0 . 9 . ▁However ▁none ▁of ▁these ▁manip ulations ▁satisf actor ily ▁reduced ▁the ▁cat ast roph ic ▁inter ference ▁exhib ited ▁by ▁the ▁networks . ▁ ▁Over all , ▁McC los key ▁and ▁C ohen ▁( 1 9 8 9 ) ▁concluded ▁that : ▁ ▁at ▁least ▁some ▁inter ference ▁will ▁occur ▁whenever ▁new ▁learning ▁al ters ▁the ▁weights ▁involved ▁representing ▁ ▁the ▁greater ▁the ▁amount ▁of ▁new ▁learning , ▁the ▁greater ▁the ▁dis ruption ▁in ▁old ▁knowledge ▁ ▁inter ference ▁was ▁cat ast roph ic ▁in ▁the ▁back prop ag ation ▁networks ▁when ▁learning ▁was ▁sequ ential ▁but ▁not ▁concurrent ▁ ▁Con stra ints ▁Im posed ▁by ▁Learning ▁and ▁For getting ▁Function s : ▁Rat cl iff ▁( 1 9 9 0 ) ▁ ▁Rat cl iff ▁( 1 9 9 0 ) ▁used ▁multiple ▁sets ▁of ▁back prop ag ation ▁models ▁applied ▁to ▁standard ▁recognition ▁memory ▁procedures , ▁in ▁which ▁the ▁items ▁were ▁sequ entially ▁learned . ▁After ▁inspect ing ▁the ▁recognition ▁performance ▁models ▁he ▁found ▁two ▁major ▁problems : ▁ ▁Well - lear ned ▁information ▁was ▁cat ast roph ically ▁forgotten ▁as ▁new ▁information ▁was ▁learned ▁in ▁both ▁small ▁and ▁large ▁back prop ag ation ▁networks . ▁ ▁Even ▁one ▁learning ▁trial ▁with ▁new ▁information ▁resulted ▁in ▁a ▁significant ▁loss ▁of ▁the