1. 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

2. 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 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 luminance contrast weighted local patch movements sampled from measured distributions (uniform gave same results)

4. statistics of natural scenes large dynamic range little correlation from fixation to fixation

5. statistics of natural scenes

8. 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 luminance: 56 → 32 cdmluminance: 32 → 56 cdm linear prediction

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

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. 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. model each temporal kernel as a convolution of contrast, luminance, and base kernel (product in the freq domain) separable model fitting the temporal kernel descriptive model fit parameters for each luminance/contrast setting

19. results - % variance of neural response explained both kernels work equally well separabledescriptive

20. results - adaptation effects modeled with separable kernel circles: neural responselines: predictions of model luminance = 10% luminance = 84% contrast = 10% contrast = 100%

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