Bernal S D_2010.pdf - University of Plymouth

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.5./. FEEDFORWARD PROCESSING 5.1.2.3 Categorization as a function of the S2 RF size Figure S.16 shows ihe categorization performance as a function of the S2 RF size, which takes the values 4x4, 8x8, 12x12 and 16x16, The results shown were obtained using the alternative 3-!evel architecture and using values of Kngroup ~ Kc\gn>up — '** ^^^ 'l"^ number of non-zero elements that maximized the performance for each S2 RF size: 1, 4. 8 and 8 respectively. Results are plotted lor the five different lesl dataseis as detailed in Ihe figure legend. c ,9 n o m o
S.i. FEEDFORWARD PnOCESSING 5.1.2.4 Comparison of difTerent models Figure 5.17 compares the categorization pert'onnance of the three versions of the model pro posed, namely the 3-leve! architecture, the 4-level architecture and the allemalive 3-lcvel archi tecture, the HMAX model and an HTM network. 1-or the 4-level architecture only the results for the normal daiasei were calculated, as its poor performance suggested the results on the transformed datasets would be extremely low and dius not worth the computational cost. The HMAX-like model was implemented using Matlab and repticales Ihe model described in Serre et ai. (2007c). i.e. the 3-level HMAX iniplenienlalion. Following the original HMAX implementation, the S2 prototypes are selected at random from the training set, as opposed to employing the minimum-dislanre algorithm implemented in Ihe Bayseian network model (see Section 4.3). The HTM-like results were obtained using the Numenifi Vision Toolkit (George and Hawkins 2009), which allows one to train and test an HTM network. However, only 50 categories are allowed, so 10 categories had to be eliminated from the training and testing datasets. 210
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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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4.1. HMAX AS A BAYESIAN NETWORK S3
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4.1. HMAX AS A BAYESIAN NETWORK Nod
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4.2. ARCHITECTURES 4.2 Architecture
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4,2. ARCHITECTURES S3 C2 1 node Kj,
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4.2. ARCmm^TVRES S3 I C2 s f S2 o o
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4.2. ARCHITECTURES S4 f C3 S3 I C2
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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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• 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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Reynolds, J. H. &, Chelazzi, L. (20
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