Bernal S D_2010.pdf - University of Plymouth

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5.1. FEEDFORWARD PROCESSING tmageil knftgQ? •rrragB 3 flnage^ ima^ 5 Jmagafi enage 7 Image B ImagflQ tmngn 10 tmageiH Fmage 17 Image 13 Image M Ifnage 15 \n\aQB IE Image 1/ linsge 16 Image 19 Image 20 - - i - E - ? - t ' F G « - H J K lmBBB.21 lmSBB22 Image'za Image 24 (fnago 25 Imaae 26 Image.27 ImagBiSS ImagarM hnHasSO - — H t M N P l Q R S lfnsge.31 Imaga 32 linaqe 33 Imaga 34 Image 3S bnaBB 36 Image 37 Image.SS Ifnage 39 Imaga 40 T U V W X Y Z M -^ • Imagell lmHga'42 lmBgg43 mage 44 image 45 bnaga 4e image 47 image48 Image's Image 50 litBOBSI Imige 52 Imana ^3 Image 54 Imaga 55 Imnge 56 lms(]e 57 Image 5B iFiiage 59 Imago 80 Figure 5.fi: Dataset nf 60 object silhouette images used to train and test the model. The .S2 and S3 prototypes were learned from this set of images. The trained network was then tested on differeni transformations of the same images including occluded, translated and scaled versions. 201
5.1. FEEDFORWARD PROCESSING Normal Occluded Scaled 10% Scaled 20% Translated ,0^ ^ ^ ^ G O G G G Figure 5.9.- Bxamplcs of object transformations. The trained networlt was then tested on differeni iransformaiions of the training iniagtis inuluding occluded, translated and scaled versions. Kxainples of these transformations are shown here for five objects. 202
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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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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
Page 167 and 168: 4.2. ARCHITECTURES 4.2 Architecture
Page 169 and 170: 4,2. ARCHITECTURES S3 C2 1 node Kj,
Page 171 and 172: 4.2. ARCmm^TVRES S3 I C2 s f S2 o o
Page 173 and 174: 4.2. ARCHITECTURES S4 f C3 S3 I C2
Page 175 and 176: 4.3. LEARNING 4.3.2 S1-C1 CRTs The
Page 177 and 178: 4.3. LEARNING Weight matrix applied
Page 179 and 180: 4.3. LEARNING CI group -1 •B •
Page 181 and 182: 4.3. LEARNING 2. The list of select
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Page 185 and 186: 4.3. LEARNING 4.3.4 S2-C2CPTS The w
Page 187 and 188: 4.3. WARNING 60 S3 tealures (object
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Page 251 and 252: 6.1. ANALYSIS OF RESULTS dence and
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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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