Bernal S D_2010.pdf - University of Plymouth

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4.3. LEARNING AN., = 4 AN„ = 8 ANs2=12 AN„ = 16 *S2 •i^ IT *. i, * •'«. .* »*: -» -. : V V" ^ ^' 'V ^V •V 'V; '^V Jf i' gGL:i^>5;.v?;5;f; «m*;i5i;v^/:';i^^ ^:5îS^^^''i,^^; ^^??^^v=;^.•r'^;=^.^^^v W 3C^t '^ftV ^'r ?V.' ^^ '^'•' ^( ? V rr " •> •'^ T*: ^ S2 RF size » AN^^ x ANgj (locations) x 4 (orientations or C1 groups) Figure 4.10.- Weight matrices between an S2 node and its afferent CI nodes. These are learned from the training dataset of 60 object silhoucites fiiMowtng the miiiimum-disiance algorilbin and ure shuwn before being converted to the CITs. The (ijiure shows a sample of 50 of the 250 S2 prototypes for each of the 4 RF sizes or A/V.n values. These prototypes are common for all scale bands. Bach prototype represents the weights for each of the four orientations in a 2-by-2 grid of adjacent images, where lop-leri=0°,top-right-45". bottom-leff-90" and botlom-rigbi^l35°. 167
4.3. LEARNING 4.3.4 S2-C2CPTS The weights between each C2 node and its afferent S2 nodes are learned using the same methodology described for the SI -CI layers, i.e. k-means clustering to obtain the most com mon arrangements of S2 units. In this case the algorithm extracts Kci^roup clusters of size ^ci 'X Nc2 X Niinndi for each C2 group or S2 state. The parameter Nj,a„ds represents the number of S2 bands being pooled from, and varies for the different architectures presented. The resulting weight matrices, learned from the training dalasel of 60 object silhouettes follow ing the clustering procedure are shown in Figure 4.11 for a value of Kc2gniup - 10- Note that each C2 node receives input from Ihe S2 nodes of up to 8 scale bands. The weight matrices shown in Figure 4,11 are converted to one CPT per each S2 node using an equivalent procedure to that described for the Sl-Cl CPTs. 4.3.5 C2-S3CPTS The weights between each S3 node and its afferent C2 nodes are learned in a supervised manner for each of the KST, — 60 training images. For the 3-layer architecture, the A(C2) response for each training image becomes the prototype weight matrix. The CPT /'(C2|53) containing Kcii- 10000) X Ks•^{= 60) elements can be easily obtained in ihis manner, by normalizing the weight matrices for each prototype. In other words, the prototype of each input image is learned as a function of the X{C2) response and converted to a CPT relating C2 and S3, as shown in Figure 4.11. In the case of Ihe allemalive 3-layer architecture where there are 9 C2 nodes and 4 S3 nodes, the learning method is also supervised but the size and number of proiotypcs varies. The size of the S3 prototypes is now AA'y^ x A^'s•.l — 2 x 2 C2 units; and these are learned from the 4 possible locations within the C2 units (see lop of Figure 4.5), leading lo Ks^ = 60 objects • 4 locations - ^Caption for Figure 4,11. Weighl matricej, bulween a C.2 node and its afferent S2 nodes. These are learned from the training datasei of 60 object silhuaeties following the clustering pnicedun; described, and represent the fCc2gniup = 10 mt>sl common arrangemeni of CI nodes for each C2 group and scale hand. Note that each C2 node receives inpui from the S2 niiden of up lo K scale bands, where, tor each scale band, the pooling range, ANcj. is differeni. .Similarly, the weighl matrices are divideU according Ui the S2 RF' sizes, as the S2 response maps for S2 Rl- siK, have differeni sizes and yield different S2-C2 wcighls. For purpio.ses of clarity, a single C2 feature is highlighted using a red dolled ellipse. 168
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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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.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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Page 167 and 168: 4.2. ARCHITECTURES 4.2 Architecture
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Page 175 and 176: 4.3. LEARNING 4.3.2 S1-C1 CRTs The
Page 177 and 178: 4.3. LEARNING Weight matrix applied
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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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