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Neuronal basis of natural textures coding in area V4 of the awake monkey: texture analysis

Neuronal basis of natural textures coding in area V4 of the awake monkey: texture analysis. P.Girard, C. Jouffrais, F. Arcizet, J. Bullier. Insight2+ IST–2000-29688 3D shape and material properties for recognition. Aim of the study (WP3). Coding of material properties

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Neuronal basis of natural textures coding in area V4 of the awake monkey: texture analysis

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  1. Neuronal basis of natural textures codingin area V4 of the awake monkey: texture analysis P.Girard, C. Jouffrais, F. Arcizet, J. Bullier Insight2+ IST–2000-29688 3D shape and material properties for recognition

  2. Aim of the study (WP3) • Coding of material properties • In area V4 of awake macaque monkey • Performing a visual fixation task • Stimuli from the CURET database: • 12 textures + 12 scrambled textures • Frontal viewing direction • 3 illumination directions (22.5, 45 and 67.6 deg.) 72 stimuli

  3. Stimuli Plaster Aluminum foil Sand paper Terrycloth Plaster (zoom) Roof shingle Salt crystals Lettuce leaf Concrete White bread Soleirolia plant Linen

  4. Experimental setup • Control of the experiment and real time analog and digital acquisition: CORTEX (courtesy of NIH) • 5 independent microelectrodes (TREC) • Sorting software: MSD (Alpha-Omega) • Eye monitoring: IScan eye-tracker (120 Hz, 0.2 DVA)

  5. Protocol • Mapping of the Receptive Field (RF) • Hand-moved bars • M-sequences of black and white dots • Recording of response to the 72 stimuli (10 trials per stimulus) • Control: 36 original textures moved 1 deg apart

  6. Recording sites .

  7. Database and statistics • Database: • 167 cells (42 with unshaped stimuli, 98 with shaped stimuli, 27 with new set of textures) • Statistics • ANOVA 3-factors (Texture, Illum. Dir., Type) • Population (Rank analysis, MDS, comparison V4/IT)

  8. Lettuce leaf Plaster (zoom) 100 Spikes/s 0 On Off 0.5s V4 neuron sharply selective to textures 22.5 deg. 45 deg. 67.6 deg.

  9. neuron selective to illumination direction ] ] ] ] ] ] Texture Example of a V4 cell whose discharge is systematically increased for a lighting direction of 67.6 deg.

  10. V4 neuron selective to original and “moved” textures Example of a V4 cell whose selectivity is the same for ‘original’ and ‘moved’ conditions. No response to scrambled sitmuli.

  11. Statistics 3 factors ANOVA (main effect + interaction, P<0.05) shows that: • 82% of the cells are selective to textures • 69% of the cells have a different response to original and random-phase textures • 69% of the cells are selective to lighting direction

  12. 82% selective to texture

  13. Dimension 2 Multidimensional Scaling (MDS) – originals MDS analysis performed on 68 cells. Original textures only, final configuration, 3 dimensions (Alienation:0.108, Stress: 0.099).

  14. Correlations of neuronal responses with first,second,third and fourth order parameters skewness Median luminance Rms contrast kurtosis

  15. Texture analysis • Is there a match between V4 cell population and a set of filters that could be used to classify the textures? • Are there other interesting parameters that characterize the textures and are coded in V4?

  16. Texture analysis: methodology • Sets of 2D GABOR filters (several sizes, spatial frequencies and 8 orientations (0°:22.5:157.5°) • 3 different types of quantification of outputs • - thresholds • -energy • -Spectral histograms

  17. Example of filter and computations (thresholds) Size= 12 pixels, freq: 9.5 c/°, sigma 4 pixels, orientation 0 Size= 12 pixels, freq: 14 c/°, sigma 4 pixels, orientation 0

  18. Example of cluster analysis with filters and neurons

  19. Example of filter and computations (energy)

  20. Cluster analysis based upon energy filters: Size 12 pixels, freq: 2 to 28 c/°, sigma 3 pixels, orientations 0:22.5:157.5° N=56

  21. MDS based upon energy

  22. Spectral Histogram N=29

  23. Spectral Histogram vs ENERGY energy Spectral histogram

  24. MDS over different epochs after the stimulus onset filters: Size 12 pixels, freq: 2 to 28 c/°, sigma 3 pixels, orientations 0:22.5:157.5°

  25. MDS with images (filters/neurons)

  26. New textures

  27. SNR is an important parameter Mean2/std2 (of image, not of filtered image)

  28. Snr : 1 possible dimension N=27 filters: Size 12 pixels, freq: 2 to 28 c/°, sigma 3 pixels, orientations 0:22.5:157.5°

  29. SNR another example

  30. Mds with images of the textures

  31. Luminance?

  32. Conclusions • Coding of material properties in V4 and IT • Is this indeed texture classification or identification? We need expert advice to use better texture characterization (Spatial frequency…) or classification (Varma and Zisserman, Geusebroek and Smeulders) • Do neurons perform such expert classification? Need to use a comparable behavioural task?

  33. Not shown

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