Probabilistic Models of the Brain- Perception and Neural Function.pdf

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1、ProbabilisticModelsoftheBrainNeuralInformationProcessingSeriesMichaelI.JordanandSaraA.Solla,editorsAdvancesinLargeMarginClassifiersAlexanderJ.Smola,PeterL.Bartlett,BernhardSch¨olkopf,andDaleSchuurmans,eds.,2000AdvancedMeanFieldMethods:TheoryandPracticeManfredOpperandDavidSaad,eds.,2001Probabi

2、listicModelsoftheBrain:PerceptionandNeuralFunctionRajeshP.N.Rao,BrunoA.Olshausen,andMichaelS.Lewicki,eds.,2002ProbabilisticModelsoftheBrain:PerceptionandNeuralFunctionEditedbyRajeshP.N.RaoBrunoA.OlshausenMichaelS.LewickiABradfordBookTheMITPressCambridge,MassachusettsLondon,Englandc
2002Massa

3、chusettsInstituteofTechnologyAllrightsreserved.Nopartofthisbookmaybereproducedinanyformbyanyelectronicormechanicalmeans(includingphotocopying,recording,orinformationstorageandretrieval)withoutpermissioninwritingfromthepublisher.PrintedandboundintheUnitedStatesofAmericaLibraryofCongressCatalo

4、ging-in-PublicationDataProbabilisticmodelsofthebrain:perceptionandneuralfunction/editedbyRajeshP.N.Rao,BrunoA.Olshausen,MichaelS.Lewickip.cm.(Neuralinformationprocessingseries)“ABradfordbook.”Includesbibliographicalreferencesandindex.ISBN0-262-18224-6(hc.:alk.paper)1.Brain–Mathematicalmodels

5、.2.Neurology–StatisticalMethods.I.Rao,RajeshP.N.II.Olshausen,BrunoA.III.Lewicki,MichaelS.IV.Series.
DNLM:1.BrainMapping–methods.2.Models,Neurological.3.Models,Statistical.4.Neurons–physiology.5.VisualPerception–physiology.WL335P96152002QP376.P6772002612.8’2’011–dc212001042806ContentsPreface

6、ixIntroduction1PartI:Perception1BayesianModellingofVisualPerception13PascalMamassian,MichaelLandy,andLaurenceT.Maloney2Vision,PsychophysicsandBayes37PaulSchraterandDanielKersten3VisualCueIntegrationforDepthPerception61RobertA.Jacobs4VelocityLikelihoodsinBiologicalandMachineVision77YairWeissa

7、ndDavidJ.Fleet5LearningMotionAnalysis97WilliamFreeman,JohnHaddon,andEgonPasztor6InformationTheoreticApproachtoNeuralCodingandParameterEstimation:APerspective117Jean-PierreNadal7FromGenerictoSpecific:AnInformationTheoreticPerspectiveontheValueofHigh-