Bernal S D_2010.pdf - University of Plymouth

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3.3. DEHNJTION AND MATHEMATICAL FORMULATION However, the most interesting effect happens at the mxJe Moon, where its belief shows a re duction with respect to the previous scenario (0.524 vs. 0616). This is a consequence of the increased probability of Gales, which suggests il is the cause responsible of Waves, thus explain ing away the Moon cause. In other words, once there is an explanation for Waves, namely Gales, the probabilily of alternative explanations, such as Mnon, is reduced. Note the probabihly of Moon is still relatively high, as according to the conditional probabilily table P(W\G,M), both high-level causes can coexist, and in fact, when hoih are present, the conditional probability of Waves is higher. 3.3.3.9 Example of belief propagation with no evidence This example serves to illustrate how belief propagation operales when there is no evidence available. All the resulting beliefs and ihe flow of messages are depicted in detail in Figure .1.8. Strictly speaking Ihe resulting beliefs arc not the posterior probabilities, P{X\Q), as there is no evidence available. Instead ihey represent the marginal probabilities of the variables when the network is in an initial equilibrium stale before presenting any evidence. Therefore it is also useful to compare the resulting beliefs in the network in equilibrium with those when there is evidence, to obtain a better understanding of the effects of evidence propagation. When all the bottom-up A mes,sages received by a node show Hal distributions (i.e. no evidence below), inevitably all the A messages sent to its parents will also show llai distrihuiions, regard less of the incoming n messages. This is the case of node W in Figure 3.8. The prior probability (or evidence) at the lop causal nodes
3 J. DEFINITION AND MATHEMATICAL f-ORMULATION 1) 2) mi = fo, t; '(81 = (1.'J nfBJ'IO.IUl.O.Jti) ^.(mj-f0.*78. OS;* nsf«J = (0-'".lll Figure 3.7: For caption see footnote^ 102 Mm)- nM = (OS. O.JJ SafM = (Otn. O.JHJ A(ni) = (O.JK, i iW) n[m) = (CS, O.JJ I Be/(g) = (D, t) '(g) ^ (0,1) • nro;Mll.Wi,D.r31) • o -• -* EvklanH Billil lipifau A nwtiaoe TT miiiigo
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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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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
Page 85 and 86: XI. THEBAYESIANBRAINHYPfmBSIS poste
Page 87 and 88: 3.1. THE BAYESJAN BRAIN HYPOmESIS T
Page 89 and 90: 3.2. EVIDENCE FROM THE BRAIN ity in
Page 91 and 92: 3.3. DEFINITION AND MATHEMATlCACPOR
Page 93 and 94: 3.3. DEFINITION AND MATHEMATICAL FO
Page 95 and 96: XX DEFINITION AND MATHEMATICAL FORM
Page 97 and 98: .1,3. DEHNITION AND MATHEMATICAL FO
Page 99 and 100: 3 J. DEFINITION AND MATHEMATICAL FO
Page 101 and 102: 3.3. DEFINITION AND MATHFMAnCAL FOR
Page 103 and 104: .1.3. DERNrnON AmMAnWMATtCAL FORMUL
Page 111 and 112: 3.3. DEFINITION Am MATHEMATICAL FOR
Page 113 and 114: 33. DEFINITION AND MATHEMATICAL FOR
Page 115 and 116: 3.3. OmNmON AND MATHEMATSOALFORMVLA
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Page 123 and 124: 3.3. D^JmnON AND MATHEMATICAL F0RMU
Page 125 and 126: .1.3. DEFI^anON AND MATHFMATICAL FO
Page 127 and 128: 3.3. DEFINITION AND MATHEMATICAl. F
Page 129 and 130: 3J. DEFINITION AND MATHEMATICAL FOR
Page 131 and 132: 3.4. EXISTING MODELS is on models t
Page 133 and 134: .1.4. EXISTING MODELS Figure J. 10:
Page 135 and 136: 3.4. EXISTING MODELS the node encod
Page 137 and 138: 3.4. EXISTING MOOm^ Type of f>raph
Page 139 and 140: 3.4. EXISTING MODELS The model comp
Page 141 and 142: X4. EXISTING MODELS ;v 1,1 gi (8>^8
Page 143 and 144: 3A. EXISTING MODELS proposes a triv
Page 145 and 146: 3.4. EXISTING MODELS by the higher
Page 147 and 148: 3.4. EXISTING MODELS aleni lo findi
Page 149 and 150: 3.4. EXISTING MODELS Model Epshtein
Page 151 and 152: 3.4. EXISTING MODELS Fristonelal. 2
Page 153 and 154: 3.4. EXISTING MODELS Oulgoing teedl
Page 155 and 156: 3.5. ORIGINAL CONTRIBUTIONS IN THIS
Page 157 and 158: 4.1 HMAX AS A BAYESIAN NETWORK 4.1
Page 159 and 160: 4.1. HMAX AS A BAYBSIAN NETWORK ini
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Page 163 and 164: 4.1. HMAX AS A BAYESIAN NETWORK S3
Page 165 and 166: 4.1. HMAX AS A BAYESIAN NETWORK Nod
Page 167 and 168: 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),
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