1. V1 Physiology

2. Questions Hierarchies of RFs and visual areas Is 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

8. Monkey V1 – Laminar organization

9. Monkey V1 – Inputs

10. Monkey V1 – Outputs

11. Monkey V1 – Oculodominance Columns

13. Monkey V1 – CO patches (or blobs)

14. Receptive field Monkey V1 – Orientation Tuning

15. Monkey V1 – Orientation Columns

16. 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)? Monkey V1 – Orientation Map

17. Orientation columns Monkey V1 – The Ice Cube Model

18. LGN cell

19. V1 simple cell

20. V1 complex cell

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

22. Simple cells receptive fields

23. Models v0.0

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

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

26. Expected response of linear RF to moving gratings

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

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

29. Beware of bounded indices

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

31. Standard Models v1.0

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

33. Standard Models v1.1

34. Elaborating the LN model

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

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

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

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

39. Prediction = Understanding?

40. The linear-nonlinear model

41. Simple cell receptive fields in V1

46. Why this particular set of filters?

47. Why is the cortical state important? Cortical State, Stimulus, Response, 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. Going beyond the modeling of mean responses

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

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

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

51. The ‘vending machine’ analogy The vending machine analogy

52. The ‘vending machine’ analogy The vending machine analogy

53. Arieli et al (1996) Single trial response Single trial prediction Mean response Modeling the Mean Response – Is it sufficient?

54. Supèr et al (2003)

55. Seeking invariants of the population response 8 spikes 17 spikes 3 spikes Vertical grating StimulusResponsePercept There must be some invariant feature in the population responses. Asking about the ‘neural code’ is equivalent to asking what is this invariant (‘best clustering’ approach of Victor et al).

56. Theory of Visual Area X Representation: Area X is about representing natural signals optimally. Estimation/Bayes: Area X is all about estimating the most likely stimulus (motion/contours/etc) given the statistics of natural signals. Processing: Area X is doing some interesting image processing (for example, face detection) Behavior: Area X is about using visual information for visually guided behavior (‘active vision’)

57. Half-Time