Bernal S D_2010.pdf - University of Plymouth

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A cortical model of object percepti
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A cortical model of object percepti
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Contents Ab.stract V AcknowlcdRcnie
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4.7 Original contrihutions in this
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3.9 Example of belief propagation i
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5.19 SI and CI model responses to a
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XVI
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2009 Durabernal S. Wennekers T, Den
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J.I. OVERVIEW retinal stimulation (
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J.2. MAIN CONTRfBUTlONS • A revie
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2,1. OBJECT RECOGNITION ihe princip
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2.1. OBJECT REC(JGNmON The dorsal s
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2.1. OBJECT RECOGNJTION properties
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2.1. OBJECT RECOGNITION of input em
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2.1. OBJEOTRBCXiGNrnON ta) u I/I ^.
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2.1. OBJECT RECOGNITION 2.1.2.1 HMA
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2.1. OBJECT RECnCNmON unit will be
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2.1. OBJECT REC(X}NIT!ON tivity, st
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2.1. OBJECT RECOGNITION response fr
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2.2. HSGH-LEVEL FEEDBACK feature an
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2.2. HIGH-LEVEL FEEDBACK Thirdly, p
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2.2. mCH-LBVEL FEEDBACK Kandom 2 LO
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1.2. HIGH-LEVEL FEEDBACK « 70 U M
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2.2. HIGH-LEVEL FEEDBACK D Receptiv
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1.1. HIGH-LEVEL FEEDBACK context of
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2.2. HIGH-LEVEL FEEDBACK detailed i
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2.2. HIGH-LEVEL FEEDBACK activity g
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2.2. HIGH-LEVEL FEEDBACK task, such
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2.2. HIGH'lM^im,_WEDBACK 2004), and
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2.2. HIGH-LEVEL FEEDBACK However, t
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2.2. HIGH-LEVEL FEEDBACK sizes that
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2.3. ILLUSORY AND OCCLUDED CONTOURS
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2.3. a.LVSORY AND OCCLUDED CONTnURS
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2.4. ORIGINAL CONTRIBUTIONS IN THIS
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3.1. THE BAYBSIAN BRAIN HYPOTHESIS
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XI. THEBAYESIANBRAINHYPfmBSIS poste
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3.1. THE BAYESJAN BRAIN HYPOmESIS T
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3.2. EVIDENCE FROM THE BRAIN ity in
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3.3. DEFINITION AND MATHEMATlCACPOR
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3.3. DEFINITION AND MATHEMATICAL FO
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XX DEFINITION AND MATHEMATICAL FORM
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.1,3. DEHNITION AND MATHEMATICAL FO
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3 J. DEFINITION AND MATHEMATICAL FO
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3.3. DEFINITION AND MATHFMAnCAL FOR
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.1.3. DERNrnON AmMAnWMATtCAL FORMUL
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3.3. DEFINITION Am MATHEMATICAL FOR
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33. DEFINITION AND MATHEMATICAL FOR
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3.3. OmNmON AND MATHEMATSOALFORMVLA
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3.3. DEFlNmON AND MATHEMATICAL FORM
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3 J. DEFINITION AND MATHEMATICAL f-
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3.3. D^JmnON AND MATHEMATICAL F0RMU
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.1.3. DEFI^anON AND MATHFMATICAL FO
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3.3. DEFINITION AND MATHEMATICAl. F
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3J. DEFINITION AND MATHEMATICAL FOR
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3.4. EXISTING MODELS is on models t
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.1.4. EXISTING MODELS Figure J. 10:
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3.4. EXISTING MODELS the node encod
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3.4. EXISTING MOOm^ Type of f>raph
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3.4. EXISTING MODELS The model comp
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X4. EXISTING MODELS ;v 1,1 gi (8>^8
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3A. EXISTING MODELS proposes a triv
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3.4. EXISTING MODELS by the higher
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3.4. EXISTING MODELS aleni lo findi
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3.4. EXISTING MODELS Model Epshtein
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3.4. EXISTING MODELS Fristonelal. 2
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3.4. EXISTING MODELS Oulgoing teedl
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4.1 HMAX AS A BAYESIAN NETWORK 4.1
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4.1. HMAX AS A BAYBSIAN NETWORK ini
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4.1. HMAX AS A BAYESIAN NETWORK fea
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4.1. HMAX AS A BAYESIAN NETWORK S3
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4.1. HMAX AS A BAYESIAN NETWORK Nod
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4.2. ARCHITECTURES 4.2 Architecture
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4,2. ARCHITECTURES S3 C2 1 node Kj,
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4.2. ARCmm^TVRES S3 I C2 s f S2 o o
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4.2. ARCHITECTURES S4 f C3 S3 I C2
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4.3. LEARNING 4.3.2 S1-C1 CRTs The
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4.3. LEARNING Weight matrix applied
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4.3. LEARNING CI group -1 •B •
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4.3. LEARNING 2. The list of select
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4.3. LEARNING node=«. Therefore, t
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4.3. LEARNING 4.3.4 S2-C2CPTS The w
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4.3. WARNING 60 S3 tealures (object
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4.4. FEEDFORWARD PROCESSING (he sim
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4.4. FEEDFORWARD PROCFSSING Figure
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4.5. FEEDBACK PROCESSING is proport
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4.5. F^mSACK PRCKESSING n^ (U,) i^(
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4.5. FEEDBACK PfiOCESSWG t.(VJ,) ji
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4.5. {REDBACK PROCESSING true vs. r
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4.5. FEEDBACK PROCES.SING 4.5.3.2 D
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4.5. FEEDBACK PROCESSING evidence i
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4.6. SUMMARY OF MODEL APPROXIMATION
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5,1. FEEDFORWARD PROCESSING using t
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5.L FEEDFORWARD PROCESSING Slate =
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5.1. FEEDFORWARD PROCESSING 1^ Onem
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5.1. FEEDFORWARD PROCESSING j • H
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5.1. FEEDFORWARD PROCESSING 5.1.2 O
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5.1. FEEDFORWARD PROCESSING Normal
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5.1. FEEDFORWARD PROCESSING c g ra
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5-1. FEEDFORWARD PROCESSING 4 5 Non
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5.1. FEEDFORWARD PROCESSING 100 8 9
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S.i. FEEDFORWARD PnOCESSING 5.1.2.4
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5.2. FEEDBACK-MEDIATED ILLUSORY CON
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5.2. FEEDBACK MEDIATED ILLUSORY CON
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5.2. FEiiDBACK-MEDWTHD ILLUSORY CON
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5.2. FEEDBACK MEDIATED ILLUSORY CON
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5.2. FEEDBACK-MHDIATED ILLUSORY CON
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Page 251 and 252: 6.1. ANALYSIS OF RESULTS dence and
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Page 279 and 280: 6.5. CONCLUSIONS AND SUMMARY OF CON
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