Bernal S D_2010.pdf - University of Plymouth

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This copy of the ihesis has been supplied on condition that anyone who consults il is understood 10 recognize thai ils copyright rests with its author and dial no quotaiion from the thesis and no information derived from il may be pubhshed without the author's prior consent.
A cortical model of object perception based on Bayesian networks and belief propagation. Salvador Duia Bernal. Centre for Robotics and Neural Systems. School of Psychology. University of Plymouth A thesis Bubmitted lothe Univcrsily of Plymouth in partial fulfilinient of the requirements for the degree of: Philosophy Doctor in Compulalional Neuroscience. October 21)10.
Page 1: 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 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
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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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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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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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