1. “Mimicking cortical responses in the visual cortex” Presentation 27 May 2004 Florie Daniels Lotte Verbunt

2. Introduction Visual system is most important and well-known One cell  interactions between cells Voltage sensitive dyes (Grinvald & Fitzpatrick) Orientation preference

3. Introduction Mapping respons dependent of stimulus in colour 500  m

4. Introduction Spinning pinwheels Goal: Reproducing the previous image and this movie in Mathematica

5. Contents Biological background Modelling of simple cells and hypercolumns of the cortex in Mathematica Clusters: orientation of the hypercolumns in relation to each other Test-images Colourmapping Movies Conclusions and suggestions

6. The optical pathway

7. The primary visual cortex

8. Hypercolumns Processing a single 'pixel' in the visual field Orientation sensitivity Scaling (sizes of receptive fields)

9. Receptive fields (RF) Part of the visual field in which a stimulus will elicit a respons Small RF  high resolution Large RF  blurred picture Size RF = scale (  )

10. The receptive field sensitivity profiles of simple cells First order Gaussian derivative: Second order Gaussian derivative: φ = 0 and φ = π/2

11. The kernels for the hypercolumns 1st order  :1  4 1st order  :4  1 2nd order  :1  4 2nd order  :4  1

12. Clusters

13. Test-image: ramp 1st order  :1  4 2nd order  :1  4 1st order  :4  1 2nd order  :4  1

14. Test-image: line 1st order  :1  4 2nd order  :1  4 1st order  :4  1 2nd order  :4  1

15. Test-image: circle 1st order  :1  4 2nd order  :1  4 1st order  :4  1 2nd order  :4  1

16. Test-image: mr64 1st order  :1  4 2nd order  :1  4 1st order  :4  1 2nd order  :4  1

17. Colourmapping The colourmapping depends on: angle  colour saturation  1 greyvalue  brightness

18. Kernels in colour

19. mr64 in colour

20. Rotating bar (wide) in colour

21. Movie of a rotating line

22. Experiment vs model Which part of cortex???? Rotation  clustering

23. Rotating bar (wide)

24. Rotating bar (narrow)

25. Translating bar (wide)

26. Translating bar (narrow)

27. Conclusions Goal not completely achieved, because of complexity and lack of time σ increasing from the inside to the outside  wide lines σ increasing from the outside to the inside  narrow lines Second order Gaussian kernels are better line detectors than first order Gaussian kernels

28. Suggestions for further research Mathematica: Colour movie Clustering Experimental research: Vary the stimuli Tip: Beware of mistakes in the x and y direction caused by plotting with different plotting commands

29. Questions???