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1、PrefaceThisvolumerepresentsaturningpointinneuralnetworkadvancements.Thefirstneuralnetworksposed,suchasthemultilayerperceptron,werestaticnetworksthatclassifiedstaticpatterns—fixedvectors—andresultedinanetworkoutputthatwasyetanotherstaticpattern,anotherfix
2、ed-valuedvector.Neitherpatternchangedwithtime.Todaythefieldofneuralnetworksisadvancingbeyondthesestaticneu•ralnetworks,tomoreadvancedconceptsthatincorporatetime-dynamicsintheirinputs,outputs,andinternalprocessing.Neuralnetworksnowcanaccept,asinput,time-v
3、aryingsignals,evenmultichannelsignalsthatcor•respondtoavectororimagethatchangesovertime,andoftenprovideclassificationofdatathatvariesovertime.Somenetworksproduceresultsthataretime-dynamic,includingoscillationsandtemporalpatterns,andsometimesself-sustaine
4、dactivitycanbeasignatureuniquetothenetwork'sstructureortothepatternsthatstimulatethenetwork.Whataretheelementsandarchitecturesthatmakeitpossibletoad•vancefromstaticarchitecturestodynamiccomputation?Whatapproachesprovideincreasedcapabilitiesforneuralnetwo
5、rks?Thesequestionsarean•swered,inpart,bythisvolume.Pulse-coupledneuralnetworksincorporateprocessingelements,neurons,thatcommunicatebysendingpulsestooneanother.Pulse-coupledneuralnetworkscanrepresentspatialinformationinthetimestructureoftheiroutputpulsetr
6、ainsandcansegmentanimageintomulti-neurontime-synchronousgroups.Johnson,Ranganath,Kuntimad,andCaulfield,inChapter1,illustratethesecapabilitiesandshowthearchitecturalstruc•tureofthepulse-couplednetworks.Motionperceptionisanessentialcapabilityforadvancedorg
7、anisms,yettheabilitytodetectmotionsandimageflowcomputationallyisadifficultproblem.InChapter2,LiandWangproposearecurrentneuralnetworkmodelthatcanbeoperatedasynchronouslyinparalleltoachieveareal•timesolution.InChapter3,temporalpatternmatchingisperformedwhe
8、ndynamictimewarpingiscombinedwithaHopfieldnetwork.UnalandTepedelenli-oglushowhowadynamicprogrammingalgorithmthatcomparesaninputtestsignalwithareferencetemplatesignal,reducingthenonlineartimemisalignmentsbetweenthetwopatter
