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: 32→56 cdm luminance: 56→32 cdm

10. neural sensitivity to luminance/contrast linear prediction contrast: 31→100% luminance: 100→31%

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 descriptive separable both kernels work equally well

20. results - adaptation effects modeled with separable kernel contrast = 100% luminance = 84% contrast = 10% luminance = 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?

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