) ▁and ▁the ▁sh ar pen ed ▁activation ▁( A new ) ▁as ▁an ▁error , ▁back prop ag ate ▁this ▁error ▁to ▁the ▁input ▁layer , ▁and ▁modify ▁the ▁weights ▁of ▁input - to - output ▁appropri ately ▁▁ <0x09> Do ▁a ▁full ▁forward ▁pass ▁with ▁the ▁input ▁through ▁to ▁the ▁output ▁layer ▁▁ <0x09> Back prop ag ate ▁as ▁usual ▁from ▁the ▁output ▁to ▁the ▁input ▁layer ▁▁ <0x09> Re peat ▁ ▁In ▁his ▁tests ▁of ▁an ▁ 8 - 8 - 8 ▁( input - hidden - output ) ▁node ▁back prop ag ation ▁network ▁where ▁one ▁node ▁was ▁sh ar pen ed , ▁French ▁found ▁that ▁this ▁sh ar pen ing ▁parad ig m ▁did ▁result ▁in ▁one ▁node ▁being ▁much ▁more ▁active ▁than ▁the ▁other ▁seven . ▁Moreover , ▁when ▁sh ar pen ed , ▁this ▁network ▁took ▁one ▁fourth ▁the ▁time ▁to ▁re learn ▁the ▁initial ▁inputs ▁than ▁a ▁standard ▁back prop ag ation ▁without ▁node ▁sh ar pen ing . ▁Re learning ▁is ▁a ▁measure ▁of ▁memory ▁sav ings ▁and ▁thus ▁extent ▁of ▁forget ting , ▁where ▁more ▁time ▁to ▁re learn ▁suggests ▁more ▁forget ting ▁( E bb ing haus ▁sav ings ▁method ). ▁A ▁two - node ▁sh ar pen ed ▁network ▁performed ▁even ▁slightly ▁better , ▁however ▁if ▁more ▁than ▁two ▁nodes ▁were ▁sh ar pen ed ▁forget ting ▁increased ▁again . ▁ ▁According ▁to ▁French , ▁the ▁sh ar pen ed ▁activ ations ▁inter f ere ▁less ▁with ▁weights ▁in ▁the ▁network