Bernal S D_2010.pdf - University of Plymouth

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3.1. THE BAYESIAN BRAIN HYPOTHESIS Intvnal hidden + Ext*rrul vlsJblfl state (phviialiwi, eld tnêne mapplr^ '^Hierarchical World structure Laarned Model Piramererl imemal hidden World Model Observer Model High level cause / Low level ^ features 'JHIcrarchkal Cortical structure Physical states; Light, sound,... Brain senses: Vision, audition,... Figure.^.]: Learned iniemal model in visual corlex rellecis hierarchical causal sirueture of ihe envininmeni which geEieruies the sensory input. The ambiguous inl'iirmaiion providedbysen.sory inputs (e.g. 21) retinal iniagej is only a function of ihe internal state of the world (e.g. ^D ohjecis). The hrain (iihserver) needs to inversely map ihis fuiicli{>n as precisely as possible to generate an accurali; interna! represenialion of the world. T!ie hicrarchicitl organization uf the hrain suggests il has evolved to reflect the inhercnl hierarchicyl struciure of ihe world. 67
XI. THEBAYESIANBRAINHYPfmBSIS posterior probability of the causes given the inputs (mapping from sensations to causes). This can be written as: where P{C\I) represents the posterior probabihty of the causes C given the input /, for example the probability over the different physical causes given a particular retinal image; P{I\C) rep resents the likelihood of the input / given the causes C, for example the probability of a given retinal image having been generated by one or another of the potential different physical causes; P(C) represents the prior probability of the causes C, for example the different physical slates of the world; and P{!] simply represents a normalization factor 3.1.3 Free-energy principle The free-energy principle proposed by Prislon (Friston and Kiebel 2009, Friston lOOfi, Friston et al. 2006, Friston 2010) conceptualizes the brain as an adaptive system which tries to resist a natural tendency to disorder, or entropy. Entropy can also be understood as a measure of unccnainiy or surprise, thus informally, the system needs to avoid surprises to ensure its state remains wiihin physiological bounds. One of the main characteristics of biological systems is ihal they maintain their internal stales within operational bounds, even with constantly changing environments. However, how can a system know if its sensations are surprising? The free energy principle provides a framework to do this as the free energy of a system is an upper bound on surprise. Thus by minimizing free energy, the system is implicitly minimizing surprise. Importantly, free energy can be evaluated because it depends on two probability densities which are available to the system: the recognition density and the conditional or posterior density. The recognition density, P(i?|^). provides a probabilistic representation of the causes. iJ, of a particular stimulus, given a set of internal slates, /i. In the brain these internal states hypolheli- cally correspond to neuronal activity and synaptic weights. The conditional density, P{s. i?|m). provides the joint probabilistic representation of causes, i?, and sensory signals, s. It is based 68
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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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Page 37 and 38: 2.1. OBJECT REC(X}NIT!ON tivity, st
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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
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Page 59 and 60: 2.2. HIGH'lMîm,_WEDBACK 2004), and
Page 61 and 62: 2.2. HIGH-LEVEL FEEDBACK However, t
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Page 65 and 66: 2.3. ILLUSORY AND OCCLUDED CONTOURS
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Page 81 and 82: 2.4. ORIGINAL CONTRIBUTIONS IN THIS
Page 83: 3.1. THE BAYBSIAN BRAIN HYPOTHESIS
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Page 89 and 90: 3.2. EVIDENCE FROM THE BRAIN ity in
Page 91 and 92: 3.3. DEFINITION AND MATHEMATlCACPOR
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Page 95 and 96: XX DEFINITION AND MATHEMATICAL FORM
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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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4.3. LEARNING 4.3.2 S1-C1 CRTs The
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4.3. LEARNING Weight matrix applied
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4.3. LEARNING CI group -1 •B •
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4.3. LEARNING 2. The list of select
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4.3. LEARNING node=«. Therefore, t
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4.3. LEARNING 4.3.4 S2-C2CPTS The w
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4.3. WARNING 60 S3 tealures (object
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4.4. FEEDFORWARD PROCESSING (he sim
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4.4. FEEDFORWARD PROCFSSING Figure
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4.5. FEEDBACK PROCESSING is proport
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4.5. F^mSACK PRCKESSING n^ (U,) i^(
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4.5. FEEDBACK PfiOCESSWG t.(VJ,) ji
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4.5. {REDBACK PROCESSING true vs. r
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4.5. FEEDBACK PROCES.SING 4.5.3.2 D
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4.5. FEEDBACK PROCESSING evidence i
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4.6. SUMMARY OF MODEL APPROXIMATION
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5,1. FEEDFORWARD PROCESSING using t
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5.L FEEDFORWARD PROCESSING Slate =
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5.1. FEEDFORWARD PROCESSING 1^ Onem
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5.1. FEEDFORWARD PROCESSING j • H
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5.1. FEEDFORWARD PROCESSING 5.1.2 O
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5.1. FEEDFORWARD PROCESSING Normal
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5.1. FEEDFORWARD PROCESSING c g ra
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5-1. FEEDFORWARD PROCESSING 4 5 Non
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5.1. FEEDFORWARD PROCESSING 100 8 9
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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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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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268
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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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