Encoding Visual Scenes in the Primary Visual Cortex: Neuronal Responses and Models

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. 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. 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. population coding, natural image stimulation: Coding of Natural Scenes in Primary Visual Cortex Weliky, Fiser, Hunt, Wagner Neuron 37: 703-718, (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. 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. output correlation across all images, all recording sites neurons

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 predictedby local models

24. in their words, “...we found no significant differences between recorded activity on the surfacecompared 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)