Bernal S D_2010.pdf - University of Plymouth

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2.3. ILLUSORY AND OCCLUDED CONTOURS when inducing excitation comes from neurons at both sides of the target neuron, it summates. Additionally, this excitation falls onto the shared pool of inhibitory intemeurons. which also inhibit each other, normalizing the total amount of inhibition sent to the target pyramidal neuron. The combination of summating excitation and normalized inhibition leads to neurons without direct retinal stimulation, representing contours as a result of lateral connectivity. Tlie key mechanism to achieve contour completion in Grossberg's model is interpolation as defined by the bipole property. Extrapolation is also present in a sense but only when supported by the interpolation bipole mechanism. The model also supports the involvement of figural feedback but is limited to enhancing contours already formed by lateral connections, in what has been called the attentive stage (Halko el al. 2008). As explained in Section 2.1.2, during the attentive stage feedback can only provide subthreshold modulatory effects but never directly drive Ihe cell. This is consistent with the idea that feedback connections from areas V2, V4 and IT have a role in shaping Ihe Itjcal association lield (Roelfsema 2006). A similar theoretical approach, which places a sU-onger emphasis on figuraJ feedback, was pro posed by Lee (2003). h suggested illusory contours originated in higher levels wilh bigger receptive fields which could take into account a greater contextual range. This spatially diffuse activity can act as a top-down prior which feeds back to VI 's high resolution buffer. The local association field implemented via VI horizontal connections can then reline the feedback to construct spatially sharp and precise contours. Figure 2.14 summarizes the described contour completion process resulting from the interaction between feedback and lateral connections. Further evidence suggests that, although global feedback can interact with local boundary com pletion, these two processes are distinguishable and indcpendenl. Feedback, which emerges after processing the available partial information during feedforward recognition, leads to im precise boundary completion unless guided by the appropriate local cues dictated by contour relatability (Kellman ct al. 200.'1). This was shown using the dot localization paradigm, whereby an occluded image is presented, followed by a dot in front of the occluder which is rapidly masked. Subjects need to judge whether the dot is perceived to appear inside or outside of the occluder's contour. S9
2.3. ILLUSORY AND OCCLUDED CONTOURS v: VI ^ ^ Time Figow 2.M: Contour compleiion resulting from ihe inleraetion between global fefdbai:k and local horizontal connections as proposed by lê (2003). I.efl; A V2 neuron receives feedl'orward connections fnun a .set of VI neurons of a particular orieniaiion activated by real edges. Right: Tlie V2 neuron projecis leedbuck to the same set of VI neurons. The excited V2 neuron broadcasts (he possibility that an extended contour exists lo all VI neurons. This distributed feedback signal introduces the global context that motivates the completion of contour by the V1 neurons based on local evidence precisely represented in V!. 60 ^ ^
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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
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
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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'lMîm,_WEDBACK 2004), and
Page 61 and 62: 2.2. HIGH-LEVEL FEEDBACK However, t
Page 63 and 64: 2.2. HIGH-LEVEL FEEDBACK sizes that
Page 65 and 66: 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
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Page 115 and 116: 3.3. OmNmON AND MATHEMATSOALFORMVLA
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Page 119 and 120: 3 J. DEFINITION AND MATHEMATICAL f-
Page 123 and 124: 3.3. D^JmnON AND MATHEMATICAL F0RMU
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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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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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