Bernal S D_2010.pdf - University of Plymouth

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5.1. FEEDFORWARD PROCESSING Normal Occluded Scaled 10% Scaled 20% Translated Object dataset -•-HMAX -*-BBP3-leveI -•-BBP 3-level Yamane -•~BBP4-level -•-HTM (Vision Toolkit) Rgure 5.17: Comparison of categorization perfomiunee hy the three versions ol the model proposed, namely the 3-level architecture, the 4-level architeciua' and Ihe alternative :i-level architect lire, the HMAX model and an HTM network. For Ihe 4-lcvel architecture only the results for the nomiiil dalasel were ohtained. The HMAX-like model was implemented using Matlah and replicates the model described in Serre ei a), (2007c), i.e. ilie .'i-level HMAX implemenlalion. Tlie HTM-like results were obtained using Ihe Nunienla Vision Toolkit (George and Hawkins 2(K)9) which allows one to train and test an HTM network. Note for this graph the dilfereni object daiaseis are ploiied ulonp the x-axis, while ihe different models are shown wiih independent line graphs us detailed in Ihe ligiire legend. 211
5.2. FEEDBACK-MEDIATED ILLUSORY CONTOUR COMPLETION 5.2 Feedback-mediated Illusory contour completion This section describes the model responses after feedback modulation has taken effect. This illustrates the interaclion between the feedforward and feedback infonnation in the network, reflected in the evolution over time of the l>elief at the dilTerenl layers. The scenario chosen to illustrate these results consists of using the Kani/sa square as the inpul image and feeding back the representation of a square from higher layers. This section is structured lo show the effects of feedback arising from progressively higher layers. In other words, the first set of results illustrates the simplest case, when feedback originates in the CI layer, while (he final set shows results for feedback originating in the lop layer, S3, and iiu^geiing all inferior layers, including SI. All ihe results in this section were obtained using the alternative 3-level architecture, as this provided the best feedforward recognition results. 5.2.1 Feedback from C1 to S1 Figure 5.18 shows the SI model response to a Kanizsa square inpul image while the CI layer is clamped to a square representalion. Thus, the resuhs illustrate how the bniiom-up evidence from the inpul image, A(51), is combined with top-down infonnalion from the CI layer, ;r(51). 5.2.2 Feedback rrom S2 to SI Figure 5.19 shows the SI and CI model responses to a Kanizsa square input image while the S2 layer is clamped lo a square representation. Thus, the results illustrate how the bottom-up evidence from the input image, A(6"l), is combined with top-down infonnalion from ihe S2 layer, Bel{S2), and how the representation at the SI and C1 layers evolves over time. Figure 5.20 shows the temporal response of the S1 and C1 belief for the region corresponding to Uie top horizontal illusory contour of the Kanizsa figure for the setup depicted in Figure 5.19. Figure 5.21 compares the C1 belief responses as a function of the S2 RF size and the scale band for the setup shown in Figure 5.19. Figure 5.22 compares the SI and CI model responses lo the occluded Kanizsa and blurred Kanizsa input images ai limes i-l and 1=4, for ihe setup depicted in I'igure 5.19. 212
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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
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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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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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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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Reynolds, J. H. &, Chelazzi, L. (20
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Stanley. D. A. & Rubin, N. (2003),
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