Bernal S D_2010.pdf - University of Plymouth

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22. HIGH-LEVEL FEEDBACK lack of understanding of the role of top-down cortical feedback. Apparently divergent interpre tations mighi in fact be modelling a common phenomenon, but until this has been more thor oughly characterized by experimental evidence, several semantic definitions must be allowed to co-exist. 2.2.3.2 Feedback in biasi'd competition model!*. The biased compelilion theory (Desimone and Duncan 1995) prop
2.2. HIGH'lM^im,_WEDBACK 2004), and perceptual grouping mechanisms (Roelfsema 2006). Furthermore, some research (Tiesinga and Buia 2009) has focused on the detailed circuitry required for biased compelilion 10 emerge in V4, and concluded it can feasibly arise as a result of feedforward projections from VI and surround suppression mechanisms. 2.2.3.3 Feedback in Adaptive Resonance Theory (ART). Although included in a different category, ART (Carpenter and Grossberg 1987. 1998) can be considered a type of biased competition model as it has many similar properties. A central feature in ART is the matching process that compares the bottom-up input with the stored pat tern. Unlike other networks, ART encodes only the matched or 'resonant" pattern and not the actual input, as it suppresses all the portions which do not match the lop-down expectation, A parameter, which specilies the minimum fraction thiii must remain in the matched palicni for resonance to occur, ensures thai if the input is too novel or unexpected a new pattern code is learned by the system. The LAMINART model (Grossberg 2003. Grossberg et al. 2007, Raizada and Grossberg 2003) implements the described ART mechanisms, mapping them over laminar visual cortical cir cuits. These mechanisms are hypothesized to occur in the neocortex to help stabilize cortical development and learning. The model employs feedforward, feedback and horizontal interac- lions to achieve the unification of several processes including development, learning, perceptual grouping, attention and 3D vision. A special emphasis has been placed on mtxielling the detailed laminar circuits of V1 and V2 in order to achieve extra-classical receptive field effects such as perceptual grouping and allenlion. The authors identify a list of requirements that any successful cortical model of visual percep tion should accomplish. Cortical models must allow perceptual grouping to generate activity in a classical receptive Held with no direct visual stimuli (as happens with illusory contours) but must prevent lop-down feedback from doing the same (i.e. producing above-threshold activity on its own) in order to avoid hallucinations. However top-down feedback must be allowed to provide modulatory sublhreshnid activity to enhance matching incoming sensory signals. This is known as the prcattentive-attcntive interface problem. 42
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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
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
Page 53 and 54: 2.2. HIGH-LEVEL FEEDBACK detailed i
Page 55 and 56: 2.2. HIGH-LEVEL FEEDBACK activity g
Page 57: 2.2. HIGH-LEVEL FEEDBACK task, such
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Page 63 and 64: 2.2. HIGH-LEVEL FEEDBACK sizes that
Page 65 and 66: 2.3. ILLUSORY AND OCCLUDED CONTOURS
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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3.3. DEFINITION AND MATHEMATICAL FO
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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. DEFINITION AND MATHEMATICAL FO
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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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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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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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