▁than ▁un sh ar pen ed ▁weights ▁and ▁this ▁is ▁due ▁specifically ▁to ▁the ▁way ▁that ▁back prop ag ation ▁algorithm ▁calcul ates ▁weight ▁changes . ▁Activ ations ▁near ▁ 0 ▁will ▁change ▁the ▁weights ▁of ▁links ▁less ▁than ▁activ ations ▁near ▁ 1 . ▁Con sequently , ▁when ▁there ▁are ▁many ▁nodes ▁with ▁low ▁activ ations ▁( due ▁to ▁sh ar pen ing ), ▁the ▁weights ▁to ▁and ▁from ▁these ▁nodes ▁will ▁be ▁modified ▁much ▁less ▁than ▁the ▁weights ▁on ▁very ▁active ▁nodes . ▁As ▁a ▁result , ▁when ▁a ▁new ▁input ▁is ▁fed ▁into ▁the ▁network , ▁sh ar pen ing ▁will ▁reduce ▁activation ▁overlap ▁by ▁limit ing ▁the ▁number ▁of ▁highly ▁active ▁hidden ▁units ▁and ▁will ▁reduce ▁the ▁lik elihood ▁of ▁represent ational ▁overlap ▁by ▁reducing ▁the ▁number ▁of ▁weights ▁that ▁are ▁to ▁be ▁changed . ▁ ▁Thus , ▁node ▁sh ar pen ing ▁will ▁decrease ▁the ▁amount ▁of ▁dis ruption ▁in ▁the ▁old ▁weights , ▁which ▁store ▁prior ▁input ▁patterns , ▁thereby ▁reducing ▁the ▁lik elihood ▁of ▁cat ast roph ic ▁forget ting . ▁ ▁Nov el ty ▁rule ▁K ort ge ▁( 1 9 9 0 ) ▁proposed ▁a ▁learning ▁rule ▁for ▁training ▁neural ▁networks , ▁called ▁the ▁' nov el ty ▁rule ', ▁to ▁help ▁alle vi ate ▁cat ast roph ic ▁inter ference . ▁As ▁its ▁name ▁suggests , ▁this ▁rule ▁helps ▁the ▁neural ▁network ▁to ▁learn ▁only ▁the ▁components ▁of ▁a ▁new ▁input ▁that ▁differ ▁from ▁an ▁old ▁input . ▁Con sequently , ▁the ▁novel