General Principles - Andrei Gorea

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Réponse d’un filtre (s/s ou autre)IntensitéTHE UNIVARIANCE PRINCIPLEIntensité ou « qualité » du StimulusR« Qualité » (l)VR RmaxnIn In RI = stimulus intensityR = responseR max = max resp.N = max slopeS = semi-saturation cst.R s = spontaneous RSUn « canal » ou« filtre » ou« champ récepteur »Un autre
INTERPRETING POPULATION RESPONSEqqFigure 1. Computing the log likelihoodfunction in a feedforward network.At its input (bottom), a stimulus, elicitsn 1, n 2, ..., n Nspikes in the sensoryrepresentation. The response of eachneuron multiplied by the logarithm of itsown tuning curve, log[f i], gives thecontribution of that neuron to the loglikelihood function. Adding thecontribution of individual neurons(shown for two example stimulus valuesin orange and green) gives the overall loglikelihood function, log L(q) for all valuesof q that could have elicited this patternof responses. Here, the orange point atthe peak of the log likelihood functionindicates the most likely stimulus., q iJazayeri, M. & Movshon, J.A. (2006). Optimal representation of sensory information by neural populations. Nature Neuroscience 9, 690-696.
Page 1: • Barlow• Univariance, Populati
Page 4: The relationship between the firing
Page 10: Ohshiro, Angelaki, & DeAngelis, (20
Page 13 and 14: Field, D.J. & Olshausen, B.A. (2004
Page 15 and 16: Learned receptive fieldsSparse repr
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
Page 70 and 71:
Physiology and flocks
Page 72 and 73:
‘RF’ in swarmingCouzin & Franks
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Cross-Correlation & Binding
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Spatio-temporal correlation:MOTIONN
Page 78 and 79:
Neural crosscorrelationTransition f
Page 80 and 81:
Neural oscillationsEmerging due to
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Rank Order Coding
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RANK ORDER CODINGNeurones can act a
Page 86 and 87:
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
Page 100 and 101:
SLois /ContraintesphysiquesBLACK BO
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Ratcliff & McKoon (2008).The Diffus
Page 107 and 108:
Amodal completion
Page 110:
Bayes theorem
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General Principles - Andrei Gorea

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