Bernal S D_2010.pdf - University of Plymouth

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4.17 Kullback-Leibler divergence between the true and the approximate prior function ;r(X) distribution for dilTerent values of *„,„„, and Wmm 1*42 4.18 Dynamicsof loopy belief propagation in the proposed model IS.'i 4.19 Comparison of three different belief update methods during loopy belief propagation 187 5.1 ResponseoftheGabor fillers usedlo generate the Arfum,„v[>^l} messages .... 193 5.2 Likelihood response of the SI nodes.A(5l) 194 5.3 Likelihood response of the CI nodes,A(Cl) 195 5.4 Response of the CI units in the original HM AX model 196 5.5 Likelihood response oftheS2 nodes. A(52) 197 5.6 Likelihood respon.se of the C2 nodes, A(C2), a( all locations and S2 RF sizes . 198 5.7 Likelihood response of the S3 nodes, A (53), at all locations and RF sizes of the alternative 3-layer archileclure 199 5.8 Datasetof 60 object silhouette images used to train and test the model 201 5.9 Examples of object transformations 202 5.10 Categorization peribrmante as a funciion of the number of slates per group in theC! layer. Kcigrou/'. for ihe 3-level arthileclure 203 5.11 Categorization performance as a function of the number of states per group in the C2 layer, Kcxgroiip' l^t"" 'he 3-level architecture 204 5.12 Categorization performance as a function of the number of non-zero elements in the S2-C2 weight matrix for the alternative 3-level architecture, using S2 RF size ^4x4 205 5.13 Categorization performance as a function of the number of non-zero elements in the S2-C2 weight matrix for the alternative 3-lcvel architecture, using S2 RF size ::^ 8x8 206 5.14 Categorization performance as a function of the number of non-zero elements in the S2-C2 weight matrix for the alternative 3-level archileclure. using .S2 RF size - 12x12 207 5.15 Categorization performance as a function of the number of non-zero elements in the S2-C2 weight matrix for the alternative 3-lcvel architecture, using S2 RF size= 16x16 208 5.16 Categorization performance as a function of the S2 RF size for the alternative 3-level architecture 209 5.17 Comparison of categorization performance for different models 211 5.18 SI model response to a Kanizsa square input image with feedback arising from theCI layer containing a square representation 213 xiii
5.19 SI and CI model responses to a Kanizsa square input image with feedback arising IVom the S2 layer containing a square representation 214 5.20 Temporal response of the SI and CI belief for the region corresponding to the lop horizontal illusory contour of ihe Kani/sa ligure 215 5.21 Comparison of the CI belief responses ai i-4, as a function of the S2 RF size and the scale band, to a Kanizsa square input and feedback arising from a square representation in layer S2 216 5.22 Comparison of the SI and CI model responses to the occluded Kanizsa and blurred Kanizsa input images at times t= I and t^ 217 5.23 Comparison between the feedback generated by a square representation in the C2 layer (feedforward S2-C2 matrix with 1 non-zero element) 219 5.24 Comparison between the feedback generated by a square representation in the C2 layer (feedforward S2=C2 matrix with 2 non-zero elements) 220 5.25 S1, CI and S2 model responses to a Kanizsa square input image while the topdown feedback from C2, ;r(52), is (7
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: 3.9 Example of belief propagation i
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 and 34: 2.1. OBJECT RECOGNITION 2.1.2.1 HMA
Page 35 and 36: 2.1. OBJECT RECnCNmON unit will be
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
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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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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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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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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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