Bernal S D_2010.pdf - University of Plymouth

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2.2. HIGH-LEVEL FEEDBACK this suggests that, despite large differences in axonal lengths, initial processing stages can he considered to act within a common temporal window. This is consistent with the fast decrease of response observed (within the first !0 ms bin) on areas VI, V2, and V3 when inactivating area MT(Hupe el al.2001). Horizontal connections are intralaminar, although also usually reciprocal and linking cortical points of similar functional properties. Typically they do not drive largei neurons but elicit subthreshold modulatory responses. However Angelucci and Bullier (2003) demonstrated that the monosynaptic range of hori/onial connections cannot account for surround effects extend ing beyond the classical receptive held (approx. 2'), Although polysynaptic circuits could in principle underlie these long-distance effects, the slow conduction velocity of horizontal con nections makes it highly unlikely. Girard ct al, (2001) showed horizontal connections have a speed of approx. 0.1 to0.2m/s, which is more than ten times slower than feedback connections. For example, the effects of surround stimuli located 13' away (equivalent to approx. I cm of VI cortical surface), al a speed of 0.1 m/s, would take over 100 ms lo arrive. This is inconsistent with long range effects observed during the very early stages of the response (Hupeel al. 200]). Although this section is restricted to describing feedback within the visual cortex, there are examples present in many other areas. These include higher-level areas related to decision making, expectations (Summeriield and Egner 2{H)9), emotions (Sabatinelli et al. 2009) and motor-planning (Grossberg el al. 2007). One such example is the orbitofronial cortex, which has been found to feed back to the fusiform gyrus in the temporal cortex, providing top-down facilitation during object recognition (Bar et al, 2(K}6). Similarly, there is evidence suggesting feedback is present in other hierarchical sensory processing areas, such as the auditory system. where the superior temporal gyrus is believed to exert top-down modulating effects on the pri mary auditory cortex (Garrido et al. 2007). This strongly argues for considering feedback as a fundamental element in cortical processing. 2.2.2 Theoretical implications In the past few years a great cITurt has been made lo try to understand the experimental results relating to feedback. Recent reviews (Carandini ct al. 2005, Olshau,sen and Field 200.^) of our 31
2.2. HIGH-LEVEL FEEDBACK D Receptive Field Center (approx. V) Proximal surround (approx, 2") Distal surround (over 13") Horizontal connections Feedback connections Hûre2.7: Comparisiin of ihe spatial cxieni ofhori/onial and feedback conntuiions. The diagram scliemuiicaliy rcprusenis results iihtained using injeciions of sensitive bidirectional tracers in VI to estimate the oxicni in visual field coordinaie.s of feedback connections from areas V2, V3 and MT, Note thai the siK of the receptive fitid centre will depend on eccentricity. Results confirmed that feedback spatial properties provided a substrate for all surround moilulalions, including! those originating from the disml surround {red region). However, Ihe monosynaptic range of horizontal connections could only account for surround effecls wiihin ihc classical receptive lield (red region) and the proximal surround (yellow region) (Angelucci and Bullier 2(KB). Note diagram is nol to scale. 32
Page 1 and 2: A cortical model of object percepti
Page 3 and 4: A cortical model of object percepti
Page 6 and 7: 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: 1.2. HIGH-LEVEL FEEDBACK « 70 U M
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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
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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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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 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. D^JmnON AND MATHEMATICAL F0RMU
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.1.3. DEFIânON 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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