General Principles - Andrei Gorea

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Trois types de cônes codent pour une infinité de couleursAbsorption normalisée des 4 « canaux » / filtres rétiniens (3types de cônes & les batonnets)
Learned receptive fieldsSparse representation inthe output of thenetwork.144 pixel valuescontained in the patch.Example image patchused in training.Set of receptive fields that are learnt bymaximizing sparseness in the output of aneural network. The network was trained onapproximately half a million image patches ofnatural scenes. The receptive fields thatemerge from training are spatially localized,oriented, and bandpass similar to corticalsimple cells.Example image patch and its encoding by the sparse coding network.The bar chart directly above the image patch shows the 144 pixel valuescontained in the patch. These input activities are transformed into amuch sparser representation in the output of the network, shown in thebar chart at the top.As the receptive fields are matched to the structures that typically occurin natural scenes, an image can usually be fully represented using asmall number of active units.Field, D.J. & Olshausen, B.A. (2004) Sparse coding of sensory inputs. Current Opinion in Neurobiology, 14, 481–487.
Page 1: • Barlow• Univariance, Populati
Page 4: The relationship between the firing
Page 7: INTERPRETING POPULATION RESPONSEqqF
Page 10: Ohshiro, Angelaki, & DeAngelis, (20
Page 13: Field, D.J. & Olshausen, B.A. (2004
Page 17 and 18: RESPONSENEED OF A NONLINEARITYCONTR
Page 19 and 20: RESPONSENEED OF A NONLINEARITYSelon
Page 21 and 22: RESPONSENEED OF A NONLINEARITY...se
Page 23 and 24: RESPONSENEED OF A NONLINEARITY (Ful
Page 25 and 26: Receptive FieldsExcitationInhibitio
Page 27: Dans un système linéaire, mesurer
Page 30 and 31: The Convolution
Page 32: Receptive Fields & Retinotopy
Page 37 and 38: Multiscaling
Page 39 and 40: FILTRAGE MULTIECHELLEAude Oliva: ht
Page 41 and 42: Receptive Field & Opponency
Page 43: Absorption normalisée des 4 « can
Page 47 and 48: Spatial and spectral relationships
Page 49 and 50: RF in MotionAdelson, E. H. & Bergen
Page 51 and 52: RF in TextureDouble-opponencyFigure
Page 53 and 54: Psychophysics
Page 55 and 56: Freeman, Sagi, Driver (2004), Perce
Page 57 and 58: Figure 3. Horizontal cross-section
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Physiology and flocks
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‘RF’ in swarmingCouzin & Franks
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Cross-Correlation & Binding
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Spatio-temporal correlation:MOTIONN
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Neural crosscorrelationTransition f
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Neural oscillationsEmerging due to
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Rank Order Coding
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RANK ORDER CODINGNeurones can act a
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Perception and Action
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Efferent copy & Proprioception
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Schematic representation of the pri
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Perception is Figure-Ground discrim
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Figure-Fond
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Signal, Noise & Decision
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SLois /ContraintesphysiquesBLACK BO
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Ratcliff & McKoon (2008).The Diffus
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Amodal completion
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Bayes theorem
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General Principles - Andrei Gorea

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