▁L AM ST AR ▁neural ▁network ▁may ▁serve ▁as ▁a ▁dynamic ▁neural ▁network ▁in ▁spatial ▁or ▁time ▁domains ▁or ▁both . ▁Its ▁speed ▁is ▁provided ▁by ▁Heb bian ▁link - we ights ▁that ▁integrate ▁the ▁various ▁and ▁usually ▁different ▁filters ▁( pre processing ▁functions ) ▁into ▁its ▁many ▁layers ▁and ▁to ▁dynamically ▁rank ▁the ▁significance ▁of ▁the ▁various ▁layers ▁and ▁functions ▁relative ▁to ▁a ▁given ▁learning ▁task . ▁This ▁v agu ely ▁im it ates ▁bi ological ▁learning ▁that ▁integr ates ▁various ▁pre process ors ▁( co ch lea , ▁ret ina , ▁etc .) ▁and ▁cor tex es ▁( aud itory , ▁visual , ▁etc .) ▁and ▁their ▁various ▁regions . ▁Its ▁deep ▁learning ▁cap ability ▁is ▁further ▁en hanced ▁by ▁using ▁in hib ition , ▁correlation ▁and ▁by ▁its ▁ability ▁to ▁co pe ▁with ▁incomplete ▁data , ▁or ▁" lost " ▁neur ons ▁or ▁layers ▁even ▁amid st ▁a ▁task . ▁It ▁is ▁fully ▁transparent ▁due ▁to ▁its ▁link ▁weights . ▁The ▁link - we ights ▁allow ▁dynamic ▁determ ination ▁of ▁innov ation ▁and ▁redund ancy , ▁and ▁facil itate ▁the ▁ranking ▁of ▁layers , ▁of ▁filters ▁or ▁of ▁individual ▁neur ons ▁relative ▁to ▁a ▁task . ▁ ▁L AM ST AR ▁has ▁been ▁applied ▁to ▁many ▁domains , ▁including ▁medical ▁and ▁financial ▁predictions , ▁adapt ive ▁filtering ▁of ▁no isy ▁speech ▁in ▁unknown ▁noise , ▁still - image ▁recognition , ▁video ▁image ▁recognition , ▁software ▁security ▁and ▁adapt ive ▁control ▁of ▁non - linear ▁systems . ▁L AM ST AR