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

1
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
2
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?

3
statistics of natural scenes
simulated saccade sequence
movements sampled from measured distributions
(uniform gave same results)
weighted local patch
luminance
contrast
4
statistics of natural scenes
large dynamic range little correlation from
fixation to fixation
5
statistics of natural scenes
6
statistics of natural scenes
7
statistics of natural scenes
8
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

9
neural sensitivity to luminance/contrast
linear prediction
luminance 56?32 cdm
luminance 32?56 cdm
10
neural sensitivity to luminance/contrast
linear prediction
luminance 100?31
contrast 31?100
11
measured response at fixed luminance, contrast
spiking rate varies with temporal frequency,
contrast, luminance
12
model of neural response
linear filtering by convolution with
spatio-temporal kernel additive
noise thresholding non-linearity
13
the spatio-temporal kernel
14
the spatio-temporal kernel
spatial components
15
the spatio-temporal kernel
spatial components
temporal kernel (impulse response)
fitted params
16
fitting the temporal kernel
descriptive model
fit parameters for each luminance/contrast setting
17
fitting the temporal kernel
descriptive model
fit parameters for each luminance/contrast setting
18
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)
19
results - variance of neural response explained
separable
descriptive
both kernels work equally well
20
results - adaptation effects modeled with
separable kernel
luminance 84
contrast 100
luminance 10
contrast 10
circles neural response lines predictions of
model
21
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?

22
end