Bernal S D_2010.pdf - University of Plymouth

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2.3. HIVSORY AND OCCLUDED CONTOURS more lime-consuming process, which gradually strengthens the responses of features which are perceptually grouped together. An important concept for these models is the local association field which formalizes the Gestalt good continuation principle. It states that contour elements which are well aligned will tend to be grouped together, by mutually exciting and increasing each other's saliency, while non- collinear element.', tend to inhibit each other. Several studies have shown the neural interactions thai represent contour elements in VI and V2 are dictated by the selectivity of horizontal con nections which follow the local association field principle (Roelfsema 2006). The local associ ation field provides an alternative method of implementing the bipole principle, based on lateral recurrent circuits (Li 2001) instead of strictly feedforward connections. Grossberg and colleagues proposed the Boundary Contour System (BCS), one of the most prominent models based on the above principles. The BCS is encompassed by the more general Adaptive Resonance Theory (see Section 2.2.3), and comprises a number of stages thai perfonn detection, competition and cooperation of boundary segments. Later versions of the model combine both feedback, lateral competition and feedforward integration of responses through the so-called bipole cells to achieve contour completion (Grossberg ei al. 2(X)7). Here we fo cus on previous models (Grossberg et al. 1997, Raizada and Grossberg 2001) that show how contour completion is achieved by implementing the hipole propeny using strictly horizontal connections. Bipole cells are activated only when both sides of their receptive Held are sufficiently stimulated as shown in the schematic diagram of Figure 2.13. Ovals represent pyramidal celts located in layer 2/3 with collinear and co-oriented receptive fields. They are connected to each other via excitatory long-range horizontal synapses. These connections also excite a pool of inhibitory iniemeurons (black circles) connected via short range synapses to the pyramidal cell. The balance of excitation and inhibition accomplishes the desired bipole property. When only one of a pair of pacmen is present, the excitation from the inducing pyramidal cell to the target pyramidal cell is not enough to elicit the cell's response. This is because the excitation also targets the inhibitory neurons that balance out the excitation. On the other hand, 57
2.3. ILLUSORY AND OCCLUDED CONTOURS a ^ TMJ Bound»f¥ [ 1 _, r*pra»«ntatton V y Sllmulua b Bound iry rapreHnUHon stimuli!* C m? ^ Figure 2.IS: Bipole impiemenlalion by kaizuduand Grossberg (2001), Ovuls represent pyramidal cells located in layer 2/3 wiih collinear and co-oriented receptive fields. They arc connected lo each other via excitatory long-range hori/.onlul synapses. These conticclions also excite a pool of inhihilury interneuroiis (black circles) connected via short range synapses to the pyramidal cell, a) Input froin jusl one side is insullicient lo elicit a response in the larget pyramidal cell. This is ihe result of exciiaiion also targeting the inhihiiory neurons whicli balance out Ihe excitation, h) When inpui arrives from collinearly aligned inducers on either side, the bipole property arises due to the circuits' exciiaiory/inhibitory balance, leading to coniour completion. The target neuron summates inducing excitation arising from neurons at both sides. Additionally, this excitation falls onto the shared pool of inhibitory intcrneurons, which also inhibii each other, normalizing Ihe lolal amount of inhibition .sent to the larget pyramidal neuron. 58
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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
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
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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
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Page 59 and 60: 2.2. HIGH'lM^im,_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
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Page 95 and 96: XX DEFINITION AND MATHEMATICAL FORM
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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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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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