1. question:
how are neurons in the primary visual cortex encoding the visual scene?

2. question:
how are neurons in the primary visual cortex encoding the visual scene?
traditional approach:

3. question:
how are neurons in the primary visual cortex encoding the visual scene?
traditional approach:

4. question:
how are neurons in the primary visual cortex encoding the visual scene?
traditional approach:
saturation

5. question:
how are neurons in the primary visual cortex encoding the visual scene?
traditional approach:
saturation
cross-orientation suppr

6. question:
how are neurons in the primary visual cortex encoding the visual scene?
traditional approach:
saturation
cross-orientation suppr
center-surround suppr

7. question:
how are neurons in the primary visual cortex encoding the visual scene?
traditional approach:
saturation
cross-orientation suppr
center-surround suppr
luminance, phase, etc

8. traditional approach:
question:
how are neurons in the primary visual cortex encoding the visual scene?
traditional approach:
saturation
cross-orientation suppr
center-surround suppr
luminance, phase, etc
carandini 2004

9. question:
how are neurons in the primary visual cortex encoding the visual scene?
traditional approach:
saturation
cross-orientation suppr
center-surround suppr
luminance, phase, etc

10. traditional approach:
question:
how are neurons in the primary visual cortex encoding the visual scene?
traditional approach:
saturation
cross-orientation suppr
center-surround suppr
luminance, phase, etc
gratings
natural images

11. 2 important directions:
characterize response of neural populations
use natural stimuli
natural images

12. Coding of Natural Scenes in Primary Visual Cortex
population coding, natural image stimulation:
Coding of Natural Scenes in Primary Visual Cortex
Weliky, Fiser, Hunt, Wagner
Neuron 37: , (2003).

13. the setup:
anesthetized ferrets
multi-electrode cortical surface recorder, ~40 sites
flashed gratings, white squares, nat images

14. model for single cell response
CRF
white squares, reverse correlation
tuning curves
sine wave gratings
phase insensitive!

15. output model for single cell response CRF
white squares, reverse correlation
tuning curves
sine wave gratings
phase insensitive!
model: band-pass filter, localized to CRF
output

16. correlation across all images, all recording sites
neurons
output

23. effect of surround modulation on prediction accuracy
restrict stimuli to CRF, compare to large-field
no effect on site-specific correlation
better predictions of pop response for large-field
both still badly predicted by local models

24. in their words,
“...we found no significant differences between recorded activity on the surface compared to activity recorded with penetrating electrodes in layer 2/3.”
“Although the correlation between local contrast structure and cell responses is modest at the level of individual cortical sites, a very simple population code, derived from activity integrated across cortical sites having retinotopically overlapping receptive fields, represents the local contrast structure of natural scenes very well.”
“...our results demonstrate that by integrating across retino topically neighboring recording sites, a significant degree of linearity is restored to the distributed representation of natural scenes in primary visual cortex.”
“...our study is a restoration of this original classical model claiming that relevant information for coding natural scenes is in the classical receptive field.”

25. problems anesthetized ferrets surface recording
flashed images, not movies
correlation, not percent variance explained
predict “retinotopic map”, not neural activity or stimulus identity
neurons coding “local contrast structure”?
sparseness = efficiency?
sparseness, efficiency measures for multiple cell recordings

26. references/future discussions
Vinje and Gallant (2002)
stimulation of nCRF with nat-vis movies makes firing sparse and efficient
David, Vinje, and Gallant (2004)
phase-sep fourier receptive fields are diff for gratings and nat-vis movies
Felsen, Touryan, and Dan (2005)
quad-pair model doesn’t predict response to naturilistic images
Guo, Robertson, Mahmoodi, and Young (2005)
surround of nat images modulates response; phase important
Smyth, Willmore, Baker, Thompson, Tolhurst (2003)
reverse corr invalid for nat stims; reg-inverse more correct, leads to similar receptive fields for gratings and nat images
Kayser, Salazar, and Koenig (2003)
LFP and spiking show diff activity for broad-band stims, motion important

27. Guo, Robertson, Mahmoodi, Young (2005)

28. David, Vinje, Gallant (2004)

29. David, Vinje, Gallant (2004)