1. Contrast Dependant Center Surround Interactions in Area V4
Kristy A Sundberg, Jude F Mitchell, John H Reynolds

2. Center Surround Receptive Field Organization
RF Center
Surround region

3. Stimulus

4. V4 Example Cells Center alone Firing rate Firing rate % Contrast30 20
Center alone 20
Firing rate
Firing rate 10 10 20 1 10 85 1 10 85
% Contrast
% Contrast

5. V4 Example Cells Response Gain1 10 85 20 30 20
Center alone
Firing rate
Firing rate 10 20
Center + Surround 1 10 85
% Contrast
% Contrast

6. V4 Example Cells Contrast Gain
Firing rate 1 10 85 20 30
% Contrast 1 10 85 20
% Contrast
Center alone
Center + Surround

7. Does response and contrast gain reflect different classes of cells?

8. Does response and contrast gain reflect different classes of cells?
The same neuron can show both patterns.
Contrast Gain
Response gain
Firing rate 1 10 85 20 30
% Contrast 1 10 85 20 30
Center alone
Center alone
Center + 20% Surround 20
Center + 85% Surround
% Contrast

9. Simple divisive normalization model+ -
Add refs

10. + - Simple divisive normalization model
Center stimulus excitation (Ecenter) + -
Center stimulus inhibition (Icenter)

11. + - Simple divisive normalization model
Center stimulus excitation (Ecenter) + -
Center stimulus inhibition (Icenter)
Model response to center stimulus (Ecenter)/(Icenter+A)

12. + - Simple divisive normalization model
surround stimulus excitation (Esurround) + -
surround stimulus inhibition (Isurround)

13. + - Simple divisive normalization model
surround stimulus excitation (Esurround) + -
surround stimulus inhibition (Isurround)
Model response to surround stimulus (Esurround)/(Isurround+A)

14. + - Simple divisive normalization model Model response to both stimuli
(Ecenter + Esurround)/ (Icenter+ Isurround + A)

15. Simple divisive normalization model
(Ecenter)/(Icenter+A) = center stimulus response 10
100
Ecenter Ec
Icenter Ic

16. Simple divisive normalization model
(Ecenter)/(Icenter+A) = center stimulus response 10
100
Ecenter Ec
Response to center stimulus
Icenter Ic

17. Predictions of divisive normalization model
center stimulus response =
(Ecenter)/(Icenter+A)
Center alone
Response 1 10
100
% Contrast

18. Predictions of divisive normalization model
Center + surround stimulus response =
(Ecenter + Esurround)/ (Icenter+ Isurround + A)
Weak Is (low contrast surround)
Center + Surround
Response 1 10
100
% Contrast

19. Predictions of divisive normalization model
Center + surround stimulus response =
(Ecenter + Esurround)/ (Icenter+ Isurround + A)
Weak Is (low contrast surround)
Strong Is (high contrast surround)
Center alone
Response
Center + Surround 1 10
100 1 10
100
% Contrast
% Contrast

20. Predictions of divisive normalization model
Center + surround stimulus response =
(Ecenter + Esurround)/ (Icenter+ Isurround + A) 10 1
100
% Contrast
Response
Strong Is (high contrast surround)
Weak Is (low contrast surround)

21. The same neuron can show both patterns.
Firing rate 1 10 85 20 30
Center + 20% Surround
Center alone 1 10 85 20 30
Center alone 20
Center + 85% Surround
Weak Is (low contrast surround)
Strong Is (high contrast surround)
% Contrast
Response 1 10
100 1 10
100 1 10
100
% Contrast
% Contrast
% Contrast

22. Summary
Simple divisive normalization model can account for both response and contrast gain
Relative strength of surround stimulus inhibitory input determines the pattern of suppression

23. V4 neurons can have peaked contrast response functions20 50
Center alone
Firing rate
Firing rate 10 25 1 10 85 1 10 85
% Contrast
% Contrast

24. Simple divisive normalization model
(Ecenter)/(Icenter+A) = center stimulus response 10
100
Ecenter Ec
Response to center stimulus
Icenter Ic

25. Prediction of divisive normalization model
(Ecenter)/(Icenter+A) = center stimulus response
Center stimulus response
Icenter
Ecenter
% Contrast

26. Prediction of divisive normalization model
(Ecenter)/(Icenter+A) = center stimulus response
% Contrast
Center stimulus response
Ecenter
Icenter
Icenter
Ecenter
% Contrast

27. Conclusions Small gratings induce large surround modulations in V4
Surround suppression shows patterns of both response gain and contrast gain
Simple divisive normalization model can account for both response and contrast gain
Peaked contrast response functions are predicted when V4 inputs have saturating contrast response functions.

28. Thanks Go To
C. Williams
J. Reyes

29. Contrast Dependant Center Surround Interactions in Area V4
Kristy A Sundberg, Jude F Mitchell, John H Reynolds

31. 90 90 90 80 80 80 70 70 70 60 60 60 50 50 50 40 40 40 30 30 30 20 20 20 10 10 10 10 -2 10 -1 10 10 -2 10 -1 10 10 -2 10 -1 10

32. Simple divisive normalization model (Ec+Es)/(Ic+Is+A) = V4 Response
Ec = excitatory input from center stimulus
Ic = Inhibitory input from center stimulus
Es = excitatory input from surround stimulus
Is = inhibitory input from surround stimulus
A = small constant leak term

33. Physiology

34. Simple divisive normalization model
(Ec)/(Ic+A) = V4 center stimulus response Ec
Response to center stimulus Ic

35. Simple divisive normalization model
(Ec)/(Ic+A) = V4 center stimulus response Ec
Response to center stimulus Ic

36. Stimulus

37. Stimulus

38. Stimulus

39. V4 neurons can have peaked contrast response functions20 50
Firing rate
Firing rate 10 25 1 10 85 1 10 85
% Contrast
% Contrast

40. Response Gain Example Center alone Firing rate % Contrast 30 20 10 11 10 85
% Contrast

41. Response Gain Example Center alone Firing rate Center + 85% surround1 10 85 20 30
Center alone
Firing rate
Center + 85% surround
% Contrast

42. Response Gain Example Center alone Firing rate Center + 85% surround1 10 85 20 30
Center alone
Firing rate
Center + 85% surround
% Contrast

43. Response Gain Example Center alone 30 20 Firing rate 10
Center + 85% surround 1 10 85
% Contrast

44. Response Gain Example Center alone 30 20 Firing rate 10
Center + 85% surround 1 10 85
% Contrast

45. Contrast Gain Example Center alone 15 Firing rate 10 5 1 10 851 10 85
% Contrast

46. Contrast Gain Example Center alone 15 Firing rate 10 5
Center + 85% surround 1 10 85
% Contrast

47. Contrast Gain Example Center alone 15 Firing rate 10 5
Center + 85% surround 1 10 85
% Contrast

48. Summary- Part 1
Large surround modulation induced by small grating stimuli
Surround suppression shows patterns of both response gain and contrast gain

49. + - Simple divisive normalization model
Center stimulus excitation (Ecenter) + -
Center stimulus inhibition (Icenter)

50. + - Simple divisive normalization model
Center stimulus excitation (Ecenter) + -
Model response to center stimulus (Ecenter)/(Icenter+A)
Center stimulus inhibition (Icenter)

51. + - Simple divisive normalization model
Center stimulus excitation (Ecenter) + -
Model response to center stimulus (Ecenter)/(Icenter+A)
Center stimulus inhibition (Icenter)

52. surround stimulus excitation (Es) surround stimulus inhibition (Is)
Simple divisive normalization model
surround stimulus excitation (Es) + -
surround stimulus inhibition (Is)

53. surround stimulus excitation (Es) surround stimulus inhibition (Is)
Simple divisive normalization model
surround stimulus excitation (Es)
Model response to surround stimulus (Es)/(Is+A) + -
surround stimulus inhibition (Is)

54. + - Simple divisive normalization model
Model response to both stimuli (Ec+Es)/(Ic+Is+A) + -

55. Predictions of divisive normalization model
(Ec)/(Ic+Is+A) = V4 Response
Weak Is (low contrast surround)
Response 1 10
100
% Contrast

56. The same neuron can show both patterns.30 30
Center alone
Center alone 20 20
Firing rate
Firing rate 10
Center % surround 10
Center + 85% surround 1 10 85 1 10 85
% Contrast
% Contrast
Weak Is (low contrast surround)
Strong Is (high contrast surround)
% Contrast
% Contrast
% Contrast
% Contrast

57. Simple divisive normalization model
(Ec)/(Ic+A) = V4 center stimulus response 10
100 Ec Ec
Response to center stimulus Ic Ic

58. Summary
Simple divisive normalization model predicts peaked contrast response functions when inhibitory input saturates at lower contrasts than excitatory input.