V1 Physiology

1. V1 Physiology

2. Questions Hierarchies of RFs and visual areasIs prediction equal to understanding? Is predicting the mean responses enough?General versus structural models? What should a theory of V1 look like? How is information represented in V1?

3. The cortex

4. Visual Areas in the Nonhuman Primate Felleman & van Essen

5. Visual Areas in the Nonhuman Primate

6. Monkey LGN

7. Monkey LGN

8. Monkey V1 – Laminar organization

9. Monkey V1 – Inputs

10. Monkey V1 – Outputs

11. Monkey V1 – Oculodominance Columns

12. Monkey V1 – Oculodominance Columns

13. Monkey V1 – CO patches (or blobs)

14. Monkey V1 – Orientation Tuning Receptive field

15. Monkey V1 – Orientation Columns

16. Monkey V1 – Orientation Map Orientation map What generates the map? How does it develop? What is the role of experience? What is its functional significance (if any)? How are receptive field properties distributed with respect to the map features (such as pinwheels)? What is the relationship to other maps (retinotopy)?

17. Monkey V1 – The Ice Cube Model Orientation columns

18. LGN cell

19. V1 simple cell

20. V1 complex cell

21. Hierarchy of Receptive Fields Simple cells Concentric on/off Complex cells Hyper-complex Grandmother

22. Simple cells receptive fields

23. Models v0.0

24. Monosynaptic connectivity from thalamus to layer 4 Analysis of monosynaptic connections Alonso, Usrey & Reid (2001)

25. Monosynaptic connectivity from thalamus to layer 4 The “sign rule” of thalamo-cortical connectivity Reid & Alonso (1995) Alonso, Usrey & Reid (2001)

26. Expected response of linear RF to moving gratings

27. Yet F1/F0 distributions are bimodal Skottun et al (1991)

28. There appears to be a continuum of responses Priebe et al, 2004

29. Beware of bounded indices Priebe et al, 2004

30. Laminar distribution of F1/F0 Same in cat (Peterson & Freeman; but see Martinez et al)

31. Standard Models v1.0

32. Stochastic stimuli Conditional Stimulus Distributions P(s) P(s | spike) How are the original and conditional stimulus distributions different?

33. Standard Models v1.1

34. Elaborating the LN model

35. Simple-cell nonlinearities: Saturation Carandini, Heeger & Movshon (1996)

36. Saturation depends on orientation Carandini, Heeger & Movshon (1996)

37. Simple-cell nonlinearities: Masking Carandini, Heeger & Movshon (1996)

38. ‘Non-specific’ gain control can shape tuning selectivity

39. Prediction = Understanding?

40. The linear-nonlinear model

41. Simple cell receptive fields in V1

42. Simple cell receptive fields in V1

43. Simple cell receptive fields in V1

44. Simple cell receptive fields in V1

45. Simple cell receptive fields in V1

46. Why this particular set of filters?

47. Going beyond the modeling of mean responses Why is the cortical state important? Response, Cortical State, Stimulus, • The response to sensory stimulation at any one time is a function of both the recent history of the stimulus and the cortical state. • If the ongoing cortical activity is noise then: • Measure the mean response to sensory stimulus • Measure how the mean response varies with stimulus parameters.

48. The vending machine analogy The ‘vending machine’ analogy Response, Current State, Stimulus, Count up to 75¢ and deliver a coke (a deterministic machine)

49. The vending machine analogy The ‘vending machine’ analogy 50¢ 25¢ 0¢ Count up to 75¢ and deliver a coke (a deterministic machine)

50. The vending machine analogy The ‘vending machine’ analogy Response, Current State, Stimulus, Count up to 75¢ and deliver a coke (a deterministic machine)