Bernal S D_2010.pdf - University of Plymouth

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5.1. FEEDFORWARD PROCESSING _ _ _ _ _ _ — B«nd-1 Band-2 . ^ . , _„^ .MJL _ _ i ^ . Lul. , ti. _ ^ _ • • • J L ^ 1.1.1 OLA J_ L_ '4S^ . ^ j . i ij Lj_l Ui jlJ J_L k^ ..u -J l_J a_l Jl-J. 1 1 _a _1 Vi J_ 1; I a^jUt ^{S2RFg|„=4^4 band= 2) v.jlll U-j LU LJ u^ JL _L LLL i_ Ji^ 1 j ^ l U uJ LU li bi L 4-1 — _ 1 , Ll .^ -J • 1 - LLJl Ih 1^ uJi li-J. _ J J_ 1 L_Jl —L Li 1 _ k^ L^ -.L. „L 1—L JU. _»_ _. i. 1- _L -L IL li L. lU ^ L. ]_L L L U _ -L. J_ _ L L _1 -U ILI _1 -U _J l_ ill -L- J. -1 !_ u_i. U4. i-L _. -^ _ ^^^ T'lil'.P' ^Wffl "J ''..-ef.J' '..-!?,/ IBM IRM •.*;(; 17 RF siM=4>i4 RF slze=6xB J_ ilL JJ •LU U ^ 1_ L u j _ — _i _ L OA- Al Li LU L L. U i_. Li IILi IL j _ 1 — i*- HJ J*. JL ^ 1. U 1. J_ Li_ j j 1- i_ ,ii M - b-: ii. J- U LA U U L- ± JJ -^ M^ l b JU ILL U —. U .ul — ii_ L J_ CI reconstruction taking max feature at each location .|^^.ii T^>'^:i i'-.- •-••Si[i»t ;r!a .'•n ii RF8lze'12x12 J_ !_L -J — — — " — ^ ^ 4 ^ _ _ Jit_ Jl^ . LL i± ^ — a-J RFel2e=16x16 Figure 5.5: Likelihood response of the S2 nixjes, A(52). 7(;/;) Tlie A(52) probahility dislribulions for the S2 nodes wiih RF si/e=4x4 (W.v? 4) and hand=2 ai all ihc 21x21 spatial locations. Boiiomj Reconsiniciion of the S2 internal representation using the CI features ihai make up each S2 prototype (.S2 feaiure). At each locaiion the S2 prototype (a group of spatially arranged CI leaiures) correspond in j; lo the max S2 feature is shown. This provides a graphical view of the S2 internal representation. Note that there is much more information contained in all the other values of the distribution, which is not represented graphically. 197
5.1. FEEDFORWARD PROCESSING j • HH D lUg KW 0 IDDa JOB c iwe »t\ 0- 4S- I I .Jja« 90- 13S- . r S2 RF SlIS: 4ll4 Wft •«• "D 4U4 IDH 1 1 >• Jll Dt Be BB 1 •OB »» IHB Mtt ~t im 1VB *- -itr Dl 'U Dl p 40 m 1 1 IDU nA , 45' r. 90 135' S2RF8i7e:12xl2 A{C2) C1 reconstrucKon (aKing max feature at each location A(C2) CI reconstruction taking max leatute at each location 1 1 len ran B VB H» 1 ei iioa KM nv • ^1 32 RF alia: 8xB ^B ]DB B ttao K» 'n iB» KB 'D AU »» (".•^7-i-. I-'*- 0* • « • 90' /A" lx»' ILDU JCDQ m HO ' " W^"^ "«,'? 135' WW. . i J « Ik-' S2RF5l2e: 16x16 Figure 5.6: Likelihood response orthcC2 nodes. A(C2), at all locations and S2 RF sizes. Below each disfribution is shown the reconstruction of the C2 internal represenlation using the CI tealures correspoiiiiing lo the maximum C2 pimoiype at each location. This graphical view of the C2 iniernal representation is limited in that it is basedonly on one of the 2500 values of each disiribulion. Out-lo the large size and greal overlap of the KF of liie C2 nodes, the maximum feature is likely to be the same for adjacent nodes. The C2 response shown here is for Ihe allernalive 3-level archiletlure (based on Yamane el al. (2006)). 198
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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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Page 165 and 166: 4.1. HMAX AS A BAYESIAN NETWORK Nod
Page 167 and 168: 4.2. ARCHITECTURES 4.2 Architecture
Page 169 and 170: 4,2. ARCHITECTURES S3 C2 1 node Kj,
Page 171 and 172: 4.2. ARCmm^TVRES S3 I C2 s f S2 o o
Page 173 and 174: 4.2. ARCHITECTURES S4 f C3 S3 I C2
Page 175 and 176: 4.3. LEARNING 4.3.2 S1-C1 CRTs The
Page 177 and 178: 4.3. LEARNING Weight matrix applied
Page 179 and 180: 4.3. LEARNING CI group -1 •B •
Page 181 and 182: 4.3. LEARNING 2. The list of select
Page 183 and 184: 4.3. LEARNING node=«. Therefore, t
Page 185 and 186: 4.3. LEARNING 4.3.4 S2-C2CPTS The w
Page 187 and 188: 4.3. WARNING 60 S3 tealures (object
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Page 251 and 252: 6.1. ANALYSIS OF RESULTS dence and
Page 253 and 254: 6.1. ANALYSIS OF RESULTS The graph
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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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