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Nature Neuroscience dec2005. Independence of luminance and contrast in natural scenes and in the early visual system. Valerio Mante, Robert A Frazor, Vincent Bonin, Wilson S Geisler, and Matteo Carandini. Nature Neuroscience dec2005.

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slide1

Nature Neuroscience dec2005

Independence of luminance and contrast in natural scenes and in the early visual system

Valerio Mante, Robert A Frazor, Vincent Bonin, Wilson S Geisler, and Matteo Carandini

slide2

Nature Neuroscience dec2005

Independence of luminance and contrast in natural scenes and in the early visual system

Valerio Mante, Robert A Frazor, Vincent Bonin, Wilson S Geisler, and Matteo Carandini

  • measured natural statistics of local luminance, contrast
  • modeled changing temporal kernel in cat LGN cells
  • results: luminance independent of contrast kernel is separable, too
  • implications?
slide3

statistics of natural scenes

simulated saccade sequence

movements sampled from measured distributions (uniform gave same results)

weighted local patch

luminance

contrast

slide4

statistics of natural scenes

large dynamic range

little correlation from fixation to fixation

slide8

statistics of natural scenes

  • what causes these distributions?
    • 1/f statistics
    • phase alignment
    • natural scene structure: illumination, reflectance, areas of high-luminance/high-contrast
  • what are the implications for neural coding?
    • large dynamic range requires adaptation
    • expect independent coding of independent quantities
slide9

neural sensitivity to luminance/contrast

linear prediction

luminance: 32→56 cdm

luminance: 56→32 cdm

slide10

neural sensitivity to luminance/contrast

linear prediction

contrast: 31→100%

luminance: 100→31%

slide11

measured response at fixed luminance, contrast

spiking rate varies with temporal frequency, contrast, luminance

slide12

model of neural response

linear filtering by convolution with spatio-temporal kernel

additive noise

thresholding non-linearity

slide14

the spatio-temporal kernel

spatial components

slide15

the spatio-temporal kernel

spatial components

temporal kernel (impulse response)

fitted params:

slide16

fitting the temporal kernel

descriptive model

fit parameters for each luminance/contrast setting

slide17

fitting the temporal kernel

descriptive model

fit parameters for each luminance/contrast setting

slide18

fitting the temporal kernel

descriptive model

fit parameters for each luminance/contrast setting

separable model

model each temporal kernel as a convolution of contrast,luminance, and base kernel (product in the freq domain)

slide19

results - % variance of neural response explained

descriptive

separable

both kernels work equally well

slide20

results - adaptation effects modeled with separable kernel

contrast = 100%

luminance = 84%

contrast = 10%

luminance = 10%

circles: neural response lines: predictions of model

slide21

discussion

  • dynamic range, speed of adaptation
  • stimuli
    • what about other non-linear response properties? (cross-orientation, surround suppresion, etc)
    • separate underlying mechanisms?
    • what about responses to more complex images?
  • relationship to normalization models?
  • what are the neural mechanisms?
  • what are the functional implications?