- Page 1 and 2: A cortical model of object percepti
- Page 3 and 4: A cortical model of object percepti
- Page 6 and 7: Contents Ab.stract V AcknowlcdRcnie
- Page 8: 4.7 Original contrihutions in this
- Page 11 and 12: 3.9 Example of belief propagation i
- Page 13 and 14: 5.19 SI and CI model responses to a
- Page 15 and 16: XVI
- Page 17 and 18: 2009 Durabernal S. Wennekers T, Den
- Page 19 and 20: J.I. OVERVIEW retinal stimulation (
- Page 21 and 22: J.2. MAIN CONTRfBUTlONS • A revie
- Page 23 and 24: 2,1. OBJECT RECOGNITION ihe princip
- Page 25 and 26: 2.1. OBJECT REC(JGNmON The dorsal s
- Page 27 and 28: 2.1. OBJECT RECOGNJTION properties
- Page 29 and 30: 2.1. OBJECT RECOGNITION of input em
- Page 31 and 32: 2.1. OBJEOTRBCXiGNrnON ta) u I/I ^.
- Page 33: 2.1. OBJECT RECOGNITION 2.1.2.1 HMA
- Page 37 and 38: 2.1. OBJECT REC(X}NIT!ON tivity, st
- Page 39 and 40: 2.1. OBJECT RECOGNITION response fr
- Page 41 and 42: 2.2. HSGH-LEVEL FEEDBACK feature an
- Page 43 and 44: 2.2. HIGH-LEVEL FEEDBACK Thirdly, p
- Page 45 and 46: 2.2. mCH-LBVEL FEEDBACK Kandom 2 LO
- Page 47 and 48: 1.2. HIGH-LEVEL FEEDBACK « 70 U M
- Page 49 and 50: 2.2. HIGH-LEVEL FEEDBACK D Receptiv
- Page 51 and 52: 1.1. HIGH-LEVEL FEEDBACK context of
- Page 53 and 54: 2.2. HIGH-LEVEL FEEDBACK detailed i
- Page 55 and 56: 2.2. HIGH-LEVEL FEEDBACK activity g
- Page 57 and 58: 2.2. HIGH-LEVEL FEEDBACK task, such
- Page 59 and 60: 2.2. HIGH'lM^im,_WEDBACK 2004), and
- Page 61 and 62: 2.2. HIGH-LEVEL FEEDBACK However, t
- Page 63 and 64: 2.2. HIGH-LEVEL FEEDBACK sizes that
- Page 65 and 66: 2.3. ILLUSORY AND OCCLUDED CONTOURS
- Page 67 and 68: 2.3. ILLUSORY AND OCCLUDED CONTOURS
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- Page 71 and 72: 2.3. ILLUSORY AND OCCLUDED CONTOURS
- Page 73 and 74: 2.3. ILLUSORY AND OCCLUDED CONTOURS
- Page 75 and 76: 2.3. ILLUSORY AND OCCLUDED CONTOURS
- Page 77 and 78: 2.3. ILLUSORY AND OCCLUDED CONTOURS
- Page 79 and 80: 2.3. a.LVSORY AND OCCLUDED CONTnURS
- Page 81 and 82: 2.4. ORIGINAL CONTRIBUTIONS IN THIS
- Page 83 and 84: 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 AND MATHEMATICAL FO
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3.5. DEFINITION AND MATHEMATICAL FO
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3.3. DEFINITION AND MATHEMATICAL FO
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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. DEFINITION AND MATHEMATICAL FO
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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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3.5. ORIGINAL CONTRIBUTIONS IN THIS
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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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4.7. ORIGINAL CONTRIBUTIONS IN THIS
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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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5.2. FEEDBACK'MEDIATED ILLUSORY CON
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5.2. FEEDBACK-MEDIATED ILLVSORY CON
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.5.2. FEEDBACK-MEDIATED ELUSORY CON
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5.2. FEEDBACK-MEDIATED SILUSORY CON
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5.2. FEEDBACK-MEDIATED ILLUSORY CON
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5.4. ORIGINAL CONTRIBUTIONS IN THIS
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6.1. ANALYSIS OF RESULTS dence and
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6.1. ANALYSIS OF RESULTS The graph
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6.t. ANALYSIS OF RESULTS used 10 co
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6.1. ANALYSIS OF RF.SULTS the image
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6.1. ANALYSIS OF RESULTS feedback w
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6.1. ANALYSIS OF RESULTS over lime.
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6.1. ANALYSIS OF RESULTS 6.1.2.5 Fe
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6.1. ANALYSIS Ot RESULTS step. Alth
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6.1. ANALYSIS OF RESULTS operations
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6.J. ANALYSIS OFRBSULTS model could
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6.1. ANALYSIS OF RESULTS Alternativ
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6.2. COMPARISON WITH EXPERIMENTAL B
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6.3. COMPARISON WITH PRKVIOUS MODBL
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6.4. FUTURE WORK to temporal contex
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6.5. CONCLUSIONS AND SUMMARY OF CON
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6.5. CONCLUSIONS AND SUMMARY OF CON
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• The HTM paliems correspond lo t
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268
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270
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Bolz, J.& Gilbert, CD. (1986), CJcn
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Friston, K. & Kiebel, S. (2009). 'C
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Hoffman. K. L. & Lngothetis, N K. (
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Lanyon. L. & Denham. S. (2(X)9), 'M
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Murray, M. M.. Wylie. G, R., Higgin
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Reynolds, J. H. &, Chelazzi, L. (20
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Stanley. D. A. & Rubin, N. (2003),