Bernal S D_2010.pdf - University of Plymouth

More documents

Recommendations

Info

4.7 Original contrihutions in this chapter 189 5 Results 1!>1 5.1 Feedforward processing 191 5.1.1 Layer by layer response 191 5.1.2 Object categorization 200 5.2 Feedback-mediated illusory contour completion 212 5.2.1 Feedback fromCI loSI 212 5.2.2 Feedback from S2 to S! 212 5.2.3 Feedback from C2 to S2 218 5.2.4 Feedback from C2 to SI 221 5.2.5 Feedback from S3 lo SI 228 5.2.6 Object categorization with feedback 228 5.3 Feedback to S3; attention and priming 231 5.4 Onginal contributions in this chapter 232 ft Discussion and conclusions 233 6 1 Analysis of results 233 6.1.1 Feedforward processing 233 6.1.2 Feedback modulation and illusory contour completion 238 6.1.3 Beneliis and limitations of Bayesian networks 249 6.2 Comparison with experimental evidence . 255 6.3 Comparison with previous models 258 6.4 I-uiurc work 259 6.5 Conclusions and summary of contributions 261 A HMAX as a Hierarchical Temporal Memory network 265 Clossary. 268 List of references. 271 IX
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 10 and 11: List of Figures 2.1 Hierarchical sl
Page 12 and 13: 4.17 Kullback-Leibler divergence be
Page 14 and 15: Acknowledgements First of all, I wo
Page 16 and 17: Authors declaration At no time duri
Page 18 and 19: Chapter 1 Introduction 1.1 Overview
Page 20 and 21: 1.2. MAIN CONTRIBUTIONS work that c
Page 22 and 23: Chapter 2 Object perception in the
Page 24 and 25: 2.1. OBJECT RECOGNmON MSTd ta) WHER
Page 26 and 27: 2.1. OBJECT RECOGNITION even more c
Page 28 and 29: 2.1. OBJECT RBCOGNIT/ON This led to
Page 30 and 31: 2.!. OBJECTRECOGNTTION :;i=:;ii;i:i
Page 32 and 33: 2.;. OBIECTRECOGNITION erty of high
Page 34 and 35: 2.1. OBJECT RECOGNmON '*MI!\ Ht tt.
Page 36 and 37: 2.1. OBJECT RECOGNJTION field. The
Page 38 and 39: 2 J. OBJECT RECOGNITION shows high
Page 40 and 41: 2.1. OBJECT RECOGNITION 2.1.2.4 Vis
Page 42 and 43: 2.2. HIGH-LEVEL FEEDBACK likely to
Page 44 and 45: 2.Z HIGH-LEVEL fEEDBACK ing back to
Page 46 and 47: 2.2. HIGH-LEVEL FEEDBACK and compar
Page 48 and 49: 2.2. HIGH-LEVEL FEEDBACK this sugge
Page 50 and 51: 2.2. HIGH-LEVEL FEEDBACK understand
Page 52 and 53: 2.2. HIGH'LEVEL FEEDBACK t LCN ^ "
Page 54 and 55: 2.2. HIGH-LEVEL FEEDBACK 1©© cS\
Page 56 and 57: 2.2. HIGH-LEVEL FEEDBACK 2.2.3 Func
Page 58 and 59:
22. HIGH-LEVEL FEEDBACK lack of und
Page 60 and 61:
Z2. mOti-LEVEL FEEDBACK A solution
Page 62 and 63:
2.2. HIGH-LEVEL FEEDBACK PREDICTIVE
Page 64 and 65:
2.2. HIGH-LEVEL FEEDBACK 2.2.3.5 Fe
Page 66 and 67:
2.3. ILLUSORY AND OCCLUDED CONTOURS
Page 68 and 69:
2.3. JLIVSORY AND OCCLUDED CONTOURS
Page 70 and 71:
Page 72 and 73:
Page 74 and 75:
2.3. HIVSORY AND OCCLUDED CONTOURS
Page 76 and 77:
Page 78 and 79:
Page 80 and 81:
2.4. ORIGINAL CONTRIBUVONfi IN THIS
Page 82 and 83:
Chapter 3 Bayesian networks and bel
Page 84 and 85:
3.1. THE BAYESIAN BRAIN HYPOTHESIS
Page 86 and 87:
3.1. THE BAYESIAN BRAIN HYPOTHESIS
Page 88 and 89:
3.2. EVIDENCE FROM THE BRAIN hierar
Page 90 and 91:
3.2. EVIDENCE FROM tm BRAIN feedbac
Page 92 and 93:
3.3. DEFINJTIONAND MATHEMATICAL FOR
Page 94 and 95:
3.3, DEFINmON AND MATHEMATICAL FORM
Page 96 and 97:
3.3. DEHNTHON AND MATHEMATICAL FORM
Page 98 and 99:
3.3. DEFINITION AND MATHEMATICAL FO
Page 100 and 101:
3.3. DEPINITION AND MATHFMATICAL FO
Page 102 and 103:
3..1. DEHSmOS AND MATHEM/mCAL FORMU
Page 104 and 105:
Page 106 and 107:
Page 108 and 109:
Page 110 and 111:
3 J. DEFINITION AND MATHEMATICAl. F
Page 112 and 113:
3.3. DEFINWONAND MATHEMATICAL FORMU
Page 114 and 115:
3.3. DEFINITION AND MATHEMATICAl^ F
Page 116 and 117:
3.3. DEFINITION AND MATHEMATICM. mR
Page 118 and 119:
3.3. DEHNJTION AND MATHEMATICAL FOR
Page 120 and 121:
3,3. i^^lNITION AND MATHEMATICAL FO
Page 122 and 123:
3.3. DEFINITION AND MJOUEMATICAL FO
Page 124 and 125:
IX DEFINTTJON AND MATHEMATICAL FORM
Page 126 and 127:
3 J. DEFINITION AND MATHEMATICAL FO
Page 128 and 129:
3.3. OmmnON AND MMOEMATICAL FORMULA
Page 130 and 131:
3.4. EXISTING MODELS e.g. 8^/(^2^')
Page 132 and 133:
3.4. EXISTING MODELS attenlmn. The
Page 134 and 135:
3.4. EXISTING MODELS 3.4.1.3 Local
Page 136 and 137:
3.4. EXISmm MODELS X. UNC \-\-]- .V
Page 138 and 139:
3.4. EXISTING MODFXS using two of t
Page 140 and 141:
3.4. EXISTING MODELS ihL- output of
Page 142 and 143:
3.4. EXISTING MODELS During the lea
Page 144 and 145:
3.4. EXISTING MODELS Bollom-up cues
Page 146 and 147:
3.4. EXISTING MODELS maintaining th
Page 148 and 149:
3.4. EXISTING MODELS representation
Page 150 and 151:
3.4. EXISTING MODELS such as mullip
Page 152 and 153:
3.4. EXISTING MODELS Section 3.4,2)
Page 154 and 155:
3.5. ORIGINAL CiWmBUTIONS IN THIS C
Page 156 and 157:
Chapter 4 Methods This chapter desc
Page 158 and 159:
4.1. HMAX AS A BAYKSIAN NETWORK (7.
Page 160 and 161:
4.1. HMAX AS A BAYESIAN NETWORK -t,
Page 162 and 163:
4.J. HMAX AS A BAYESJAN m-TWORK The
Page 164 and 165:
4.1, HMAX AS A BAYESIAN NETWORK lop
Page 166 and 167:
4.1. HMAX AS A BAYESIAN NETWORK Nod
Page 168 and 169:
4.2. ARCHITECTURES Scale band RF si
Page 170 and 171:
4.2. ARCHITECTURES Scale band RF si
Page 172 and 173:
4.2. ARCHITECTURES RF size", ANfa S
Page 174 and 175:
4.3. LEARNING 4.3 Learning This sec
Page 176 and 177:
4.3. LEARNING S\ii node. Therefore,
Page 178 and 179:
4.3. LEARNING The k-means clusterin
Page 180 and 181:
4.3. LEARNING when all afferent SI
Page 182 and 183:
4.3. LEARNING i" Wolghtmatrix appli
Page 184 and 185:
4.3. LEARNING AN., = 4 AN„ = 8 AN
Page 186 and 187:
4.3. LEARNING Q 2 QrOUP *! /'«•>
Page 188 and 189:
4.4. FEEDFORWARD PROCESSmG 240 prot
Page 190 and 191:
4.4. FEEDFORWARD PROCESSING of this
Page 192 and 193:
4.4. F^DFORWARD PROCESSING u I M,^/
Page 194 and 195:
4.5. FEEDBACK PROCESSING George and
Page 196 and 197:
4.5. FEEDBACK PROCHSSING Tl.OJ,) n,
Page 198 and 199:
4.5. FEEDBACK PROCESSING lihood fun
Page 200 and 201:
4.5. FEEDBACK PROCESSING 4.5.3 Loop
Page 202 and 203:
4.5. FEEDBACK PROCESSING m .'(S3) 1
Page 204 and 205:
4.5. FEEDBACK PROCESSING Complete b
Page 206 and 207:
4.7. ORIGINAL CONTRIBUTIONS IN THIS
Page 208 and 209:
Chapter 5 Results 5.1 Feedforward p
Page 210 and 211:
5.1. FEEDFORWARD PKOCH.'iSING BwM:1
Page 212 and 213:
5.1. FEEDFORWARD PRIX:ESSING pfcT
Page 214 and 215:
5.1. FEEDFORWARD PROCESSING _ _ _ _
Page 216 and 217:
5.1. FEEDFORWARD PROCHSSING : . •
Page 218 and 219:
5.1. FEEDFORWARD PROCESSING tmageil
Page 220 and 221:
5.1. FEEDFORWARD PROCESSING 5.1.2.1
Page 222 and 223:
5.1, FEEDFORWARD PROCESSING 5.1.2.2
Page 224 and 225:
S.i. FmDFORWARDPROCESSING 8 9 Non-z
Page 226 and 227:
.5./. FEEDFORWARD PROCESSING 5.1.2.
Page 228 and 229:
5.1. FEEDFORWARD PROCESSING Normal
Page 230 and 231:
5.2. FEEDBACK-MEDIATEDILLVSORY CONT
Page 232 and 233:
5.2. FEEDBACK-MEDIATED ILLUSORY CON
Page 234 and 235:
5.2, FEEDSACK-MED1ATED a.LUSORY CON
Page 236 and 237:
Page 238 and 239:
Page 240 and 241:
Page 242 and 243:
Page 244 and 245:
Page 246 and 247:
Page 248 and 249:
5..1, FEEDBACK TO S3: ATTENTION AN/
Page 250 and 251:
Chapter 6 Discussion and conclusion
Page 252 and 253:
6.1, ANALYSIS nh RESULTS Another p;
Page 254 and 255:
6.1. ANALYSIS OF RESULTS different
Page 256 and 257:
6.1. ANALYStS OF RESULTS as this co
Page 258 and 259:
6.1. ANALYSIS OF RESULTS square. Re
Page 260 and 261:
6.1. ANALYSIS OF RESULTS The bclier
Page 262 and 263:
6.1. ANALYSIS Ob RESULTS update met
Page 264 and 265:
6.1. ANALYSIS OF RESULTS ral feedba
Page 266 and 267:
6.1. ANALYSIS OF RESULTS strong rot
Page 268 and 269:
6. J. ANALYSIS OF RESULTS However,
Page 270 and 271:
6.1. ANALYSIS OF RESULTS of overlap
Page 272 and 273:
6.2. COMPARISON WITH EXPERIMENTAL E
Page 274 and 275:
6.2. COMPARISON WITH EXPERIMENTAL E
Page 276 and 277:
6.4. WTUREWURK by Chikkerur et al.
Page 278 and 279:
6.5. CONCLUSIONS AND SUMMARY Ut CON
Page 280 and 281:
6.5. CONCLUSIONS AND SUMMARY OF CON
Page 282 and 283:
Appendix A HMAX as a Hierarchical T
Page 284 and 285:
N^ 9n^ = B- = aN„|t,P*h„j af*.
Page 286 and 287:
Glossary. Xix) Likelihood function,
Page 288 and 289:
List of references. Ahissar, M., Na
Page 290 and 291:
Deneve, S. (2(X)5). Baycsian infere
Page 292 and 293:
Grossl>erg, S., Cisek, P.. Drew, T.
Page 294 and 295:
Karklin. Y- & Lewicki, M, S. (2003)
Page 296 and 297:
Maertens. M., Pollmann. S.. Haiiki;
Page 298 and 299:
Pougei. A., Dayan. P. & Zemel. R. S
Page 300 and 301:
Serre, T., Kretman. G., Kouh, M., C
Page 302:
Weiss, Y. & Adelson, H. H. (1998),
show all

Bernal S D_2010.pdf - University of Plymouth

Create successful ePaper yourself

Delete template?

Save as template?