Bernal S D_2010.pdf - University of Plymouth

More documents

Recommendations

Info

3.3. DEFINITION AND MATHEMATICAL FORMULATION all of its parents; and %(«;) represents the support the assertion t/,- = M,- receives from the nodes above Uj. %(»,) can also be understood as the message X receives from its parent node Ui, which combined with the messages from all other parent nodes, and multiplied by the appropriate values of the conditional probability function, yields the n{x) function. Therefore, as has been demonstrated, the generic node X in Figure 3.4 can calculate its own belief if it receives the messages V; (••:) from its children and KX{UI} from its parents. In the rest of this section we will consider how to generate these messages, which allow the evidence lo propagate across the network. 3.3.3.3 Bottom-up mes-sattes Taking into account that all nodes in a Bayesian networit perform the same operations, we will consider the generic message Aj^(«,}, which node X must send lo its parent node IJ, (Figure 3,4). It is therefore convenient to treat all parents of X, except the one receiving the message, as a common set V, such that V —V-U, = {f/i,....(7,_i,t/n.|,...,iy/i/} as shown in Figure 3.5. The message Xx{ui) must lake into account all evidence under die link Uj —> X, which includes evidence coming from all other parent-s of X (Sy^ — U *(/ ;f)'- and evidence arriving from k-\..N\i ' the descendcnts of X (e^). Given thatX separates eyj^ from ejj^ and V separates e,^;^ from U„ we can write, [conditioning on .V and v] = ^Y,fi^vx^^x\"
3.5. DEFINITION AND MATHEMATICAL FORMULATION e uix -vx P{evx*.ex-|u,) = A,(Ui) ') = nk.iTrx(Uk) e% x) = A(x) Figure 3.5: Bollom-up k messages explanalory diagram. The message Ax(«,) musi take into account all evidence under the link [/, -+ X, which includes evidence coming from all other parents of X (ey^ -- IJ Cy x); and evidence airiving from ihe descendenls of X (e^). It is therefore convenieni lo Ireal all piirenls of X, except the one receiving the message, as a common set V, such thai t' = U - f/, = {'^1 t^,-hf^.+ i Â- 90
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 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 and 58: 2.2. HIGH-LEVEL FEEDBACK task, such
Page 59 and 60: 2.2. HIGH'lMîm,_WEDBACK 2004), and
Page 61 and 62: 2.2. HIGH-LEVEL FEEDBACK However, t
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
Page 105: 3.3. DEFINITION AND MATHEMATICAL FO
Page 111 and 112: 3.3. DEFINITION Am MATHEMATICAL FOR
Page 113 and 114: 33. DEFINITION AND MATHEMATICAL FOR
Page 115 and 116: 3.3. OmNmON AND MATHEMATSOALFORMVLA
Page 117 and 118: 3.3. DEFlNmON AND MATHEMATICAL FORM
Page 119 and 120: 3 J. DEFINITION AND MATHEMATICAL f-
Page 123 and 124: 3.3. D^JmnON AND MATHEMATICAL F0RMU
Page 125 and 126: .1.3. DEFIânON AND MATHFMATICAL FO
Page 127 and 128: 3.3. DEFINITION AND MATHEMATICAl. F
Page 129 and 130: 3J. DEFINITION AND MATHEMATICAL FOR
Page 131 and 132: 3.4. EXISTING MODELS is on models t
Page 133 and 134: .1.4. EXISTING MODELS Figure J. 10:
Page 135 and 136: 3.4. EXISTING MODELS the node encod
Page 137 and 138: 3.4. EXISTING MOOm^ Type of f>raph
Page 139 and 140: 3.4. EXISTING MODELS The model comp
Page 141 and 142: X4. EXISTING MODELS ;v 1,1 gi (8>^8
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
Page 149 and 150: 3.4. EXISTING MODELS Model Epshtein
Page 151 and 152: 3.4. EXISTING MODELS Fristonelal. 2
Page 153 and 154: 3.4. EXISTING MODELS Oulgoing teedl
Page 155 and 156: 3.5. ORIGINAL CONTRIBUTIONS IN THIS
Page 157 and 158:
4.1 HMAX AS A BAYESIAN NETWORK 4.1
Page 159 and 160:
4.1. HMAX AS A BAYBSIAN NETWORK ini
Page 161 and 162:
4.1. HMAX AS A BAYESIAN NETWORK fea
Page 163 and 164:
4.1. HMAX AS A BAYESIAN NETWORK S3
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
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
Page 195 and 196:
4.5. F^mSACK PRCKESSING n^ (U,) i^(
Page 197 and 198:
4.5. FEEDBACK PfiOCESSWG t.(VJ,) ji
Page 199 and 200:
4.5. {REDBACK PROCESSING true vs. r
Page 201 and 202:
4.5. FEEDBACK PROCES.SING 4.5.3.2 D
Page 203 and 204:
4.5. FEEDBACK PROCESSING evidence i
Page 205 and 206:
4.6. SUMMARY OF MODEL APPROXIMATION
Page 207 and 208:
4.7. ORIGINAL CONTRIBUTIONS IN THIS
Page 209 and 210:
5,1. FEEDFORWARD PROCESSING using t
Page 211 and 212:
5.L FEEDFORWARD PROCESSING Slate =
Page 213 and 214:
5.1. FEEDFORWARD PROCESSING 1^ Onem
Page 215 and 216:
5.1. FEEDFORWARD PROCESSING j • H
Page 217 and 218:
5.1. FEEDFORWARD PROCESSING 5.1.2 O
Page 219 and 220:
5.1. FEEDFORWARD PROCESSING Normal
Page 221 and 222:
5.1. FEEDFORWARD PROCESSING c g ra
Page 223 and 224:
5-1. FEEDFORWARD PROCESSING 4 5 Non
Page 225 and 226:
5.1. FEEDFORWARD PROCESSING 100 8 9
Page 227 and 228:
S.i. FEEDFORWARD PnOCESSING 5.1.2.4
Page 229 and 230:
5.2. FEEDBACK-MEDIATED ILLUSORY CON
Page 231 and 232:
5.2. FEEDBACK MEDIATED ILLUSORY CON
Page 233 and 234:
5.2. FEiiDBACK-MEDWTHD ILLUSORY CON
Page 235 and 236:
5.2. FEEDBACK MEDIATED ILLUSORY CON
Page 237 and 238:
5.2. FEEDBACK-MHDIATED ILLUSORY CON
Page 239 and 240:
5.2. FEEDBACK'MEDIATED ILLUSORY CON
Page 241 and 242:
5.2. FEEDBACK-MEDIATED ILLVSORY CON
Page 243 and 244:
.5.2. FEEDBACK-MEDIATED ELUSORY CON
Page 245 and 246:
5.2. FEEDBACK-MEDIATED SILUSORY CON
Page 247 and 248:
5.2. FEEDBACK-MEDIATED ILLUSORY CON
Page 249 and 250:
5.4. ORIGINAL CONTRIBUTIONS IN THIS
Page 251 and 252:
6.1. ANALYSIS OF RESULTS dence and
Page 253 and 254:
6.1. ANALYSIS OF RESULTS The graph
Page 255 and 256:
6.t. ANALYSIS OF RESULTS used 10 co
Page 257 and 258:
6.1. ANALYSIS OF RF.SULTS the image
Page 259 and 260:
6.1. ANALYSIS OF RESULTS feedback w
Page 261 and 262:
6.1. ANALYSIS OF RESULTS over lime.
Page 263 and 264:
6.1. ANALYSIS OF RESULTS 6.1.2.5 Fe
Page 265 and 266:
6.1. ANALYSIS Ot RESULTS step. Alth
Page 267 and 268:
6.1. ANALYSIS OF RESULTS operations
Page 269 and 270:
6.J. ANALYSIS OFRBSULTS model could
Page 271 and 272:
6.1. ANALYSIS OF RESULTS Alternativ
Page 273 and 274:
6.2. COMPARISON WITH EXPERIMENTAL B
Page 275 and 276:
6.3. COMPARISON WITH PRKVIOUS MODBL
Page 277 and 278:
6.4. FUTURE WORK to temporal contex
Page 279 and 280:
6.5. CONCLUSIONS AND SUMMARY OF CON
Page 281 and 282:
6.5. CONCLUSIONS AND SUMMARY OF CON
Page 283 and 284:
• The HTM paliems correspond lo t
Page 285 and 286:
268
Page 287 and 288:
270
Page 289 and 290:
Bolz, J.& Gilbert, CD. (1986), CJcn
Page 291 and 292:
Friston, K. & Kiebel, S. (2009). 'C
Page 293 and 294:
Hoffman. K. L. & Lngothetis, N K. (
Page 295 and 296:
Lanyon. L. & Denham. S. (2(X)9), 'M
Page 297 and 298:
Murray, M. M.. Wylie. G, R., Higgin
Page 299 and 300:
Reynolds, J. H. &, Chelazzi, L. (20
Page 301 and 302:
Stanley. D. A. & Rubin, N. (2003),
show all

Bernal S D_2010.pdf - University of Plymouth

Create successful ePaper yourself

Delete template?

Save as template?