Bernal S D_2010.pdf - University of Plymouth

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4.3. LEARNING S\ii node. Therefore, ihe origintil weighl niiilrices are convened inio one CPT per SI node as shown al the bottom left of Figure 4.7. The implementation in the real model follows that described in the loy example. The method employed lo learn Ihe Kc\group mosi common arrangemcnls of SI units at each orientation feed ing to a CI unit is a clustering technique, namely the k-means algorithm (Bishop 1995). This method can be understood as a type of Expectation-Maximizalion algorithm. This clustering method is applied over a set of SI patches obtained from the training input images. To select and pre-process the set of patches to be clustered for the / SI state (orientation) or C1 group, the following steps are performed: 1. Select the next patch, Pp„„;,r,at of size n = ANc\ •ANr\ -2 (bands) from each state of the SI nodes' A response, i.e. ^(51(^1,,.] - '),vjheTe {b,j:._v} represent the band and location of the S1 node and match the patch size, n; and / h a specific SI orientation or CI group. 2. Keep patch Ppomuiai on'y i' iis maximum value is above a threshold 7;,„„, where T„,in is given as a function of the maximum overall value of X{S\b_i.v - ') Vi,A',y. Formally, if maxiPpateîai) > T,„i„ ihcn l\,.iecied = Ppoicnimi- "^^CK T„„„ = a - maT(A (i'l,,,,-^,. = ()), and a typical value for a would be 0.9. This ensures that the weight matrix is calculated based on significant A (SI) responses which are close to the overall maximum response elicited by SI nodes, and not based just on the local maximum of each patch. 3. For each selected patch, PwUi-iedt keep only the values above Tmin and set all other values to 0. i.e. Pseiirii'd - fl
4.3. LEARNING Weight matrix applied 10 SI atferBnt noilei tor each CI feature ^n ^M ^^h • >•• ail I ^^P ^^P ^^P Approximating the max operation Aa, (C1 = I)=IMS1„H)P(S1„ = 1|C1.| n-1 D A,,,(S2)= ^MC1 = i)-P(C1- i|S2) /ncfax notation /e{1 12)=C1 feature yejl 4} -S^ feature ne{1 9} =S1 node location Weight matrices {for each CI feature) CPTs (for each SI node) i II D 1" E-7 f 1-^ fi hi t" 1 B''" B'r-7 D o Q >M B D S n-i 0 e v l 0 g a < S1,-J|C1 •»] IM Q 1 4 1 0 0 J a J ft ii4 a E> P PI D d 0 0 1 0 ti a ft •I). * rî. 0 Q 0 0 0 a 1114 0 a a 0 Q D 0 n-r 0 d Q n-4 0 B 0 t~ ~a~ t t 0 0 D 0 fi-t 1 PtSI.-HC1-l].>*T
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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'lMîm,_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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Page 135 and 136: 3.4. EXISTING MODELS the node encod
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Page 139 and 140: 3.4. EXISTING MODELS The model comp
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Page 143 and 144: 3A. EXISTING MODELS proposes a triv
Page 145 and 146: 3.4. EXISTING MODELS by the higher
Page 147 and 148: 3.4. EXISTING MODELS aleni lo findi
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Page 167 and 168: 4.2. ARCHITECTURES 4.2 Architecture
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Page 171 and 172: 4.2. ARCmm^TVRES S3 I C2 s f S2 o o
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Page 175: 4.3. LEARNING 4.3.2 S1-C1 CRTs The
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
Page 189 and 190: 4.4. FEEDFORWARD PROCESSING (he sim
Page 191 and 192: 4.4. FEEDFORWARD PROCFSSING Figure
Page 193 and 194: 4.5. FEEDBACK PROCESSING is proport
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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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5.4. ORIGINAL CONTRIBUTIONS IN THIS
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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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