1. The visual system V Neuronal codes in the visual system

2. time What‘s the code? Firing rateSpike timing - Synchrony - Timing patterns

3. ’Firing rates are the only code that ALWAYS works’ The codes – firing rate

4. We start with the question Does the brain use rate or precise timing? We turn that into: How noisy are networks? The codes – firing rate Latham & London (submitted)

5. Identical input on every trial t=0 The codes – firing rate Latham & London (submitted)

6. large noise one extra spike on trial 2 small noise t=0 Identical input on every trial Latham & London (submitted)

7. We start with the question Does the brain use rate or precise timing? We turn that into: How noisy are networks? And finally: How many extra postsynaptic spikes are caused by one extra presynaptic spike? The codes – firing rate Latham & London (submitted)

8. Experimental details: in vivo whole cell recordings layer 5 pyramidal cells of rat barrel cortex urethane anesthetic with and without whisker stimulation current injection rather than PSPs Latham & London (submitted)

9. V 100 ms θ Latham & London (submitted)

10. V 100 ms θ extra spike Latham & London (submitted)

11. V 100 ms θ small effect Latham & London (submitted)

12. V 100 ms θ Latham & London (submitted) small effect

13. V 100 ms θ Latham & London (submitted) big effect!!!

14. number of extra spikes caused by just one extra spike =p 1 × number of connections per neuron ≈p 1 × 1000 ≈0.025 × 1000 = 25 Latham & London (submitted)

15. large noise one extra spike on trial 2 small noise t=0 Identical input on every trial Latham & London (submitted)

16. Manipulation of firing rates influences visual perception Salzman et al., (1992)

17. Manipulation of firing rates influences visual perception Salzman et al., (1992)

18. The codes – synchrony ’Perception is about association. Synchrony is too.’

19. The codes – synchrony

22. Center-surround interactions Biederlack et al. (2006)

23. Center-surround interactions Biederlack et al. (2006)

24. The escape of the bullfrog Ishikane et al. (2005)

25. The escape of the bullfrog Ishikane et al. (2005)

26. The codes – precise timing ’If it works, precise timing has incredible coding capacity’

27. 20 ms per stage! 1 spike per neuron! Thorpe & Fabre-Thorpe (2001) The codes – precise timing 20-40 ms 30-50 ms 40-50 ms 50-70 ms 70-90 ms 80-100 ms

28. What can one spike tell us?

30. Theories on spike timing in the cortex Van Rullen & Thorpe (2001)

31. Onset latencies in vision Gollisch & Meister (2008) Fast OFF cell Biphasic OFF cell Time[ms] Time[ms]

32. Onset latencies in vision Gollisch & Meister (2008)

33. From external to internal timing

34. Experimental setup Anaesthesia Primary visual cortex Grating stimuli 16 channels per recording probe Multi- and single unit activity 0.2 mm

36. Raw data Time [ms] Neuron #

37. Raw data Time [ms] Neuron #

38. Raw data Time [ms] Neuron #

39. Raw data Time [ms] Neuron #

40. Preferred firing sequences Preferred relative firing time [ms]

41. Stimulus-dependent changes Relative firing time [ms]

42. Stability Relative firing time [ms] 7 hours

43. Firing sequences and firing rates r total = 0.28 r 2 total = 0.08 Firing rate Firing time

44. Firing sequences and firing rates Time [sec] # of action potentials Relative firing time [ms] Time [sec] r total = 0.01 r 2 total = 0.00

45. Neuronal coding in the real world – what is a response?

46. Responses are multi-dimensional Basole et al. (2003)

47. Information from ‘non-responsive‘ areas Haxby et al. (2001)

48. Natural vision is dynamic Things move. The body moves. Your eyes move. Everything moves. Vision is made to be a dynamic process.

49. ´Lab´ activation Mainen & Sejnowski (1995)

50. ´Natural´ activation Mainen & Sejnowski (1995)

51. Retinal responses to dynamic stimuli Meister & Berry (1999)

52. The fly in the woods Lewen et al. (2001)

53. The fly in the woods Lewen et al. (2001) Time (sec)

54. Sparse responses in natural vision What‘s the code?!

55. Neuronal coding in the real world – what is a signal?

57. Strength and structure of inputs complement each other Synaptic efficacy is boosted by bursting of a single neuron and synchrony of several neurons (Usrey et al.,1998, 2000; Swadlow & Gusev, 2001) Integration time of retinal and LGN cells changes from 1 ms to 100 ms depending on visual circumstances (Berry & Meister 1999, Butts & Stanley, 2007)

58. Rall (1964) Strength and structure of inputs complement each other

59. Rall (1964) Strength and structure of inputs complement each other

60. Rall (1964) Strength and structure of inputs complement each other

61. Rall (1964) Strength and structure of inputs complement each other

62. Rall (1964) Strength and structure of inputs complement each other

63. Rall (1964) Strength and structure of inputs complement each other

64. Rall (1964) Strength and structure of inputs complement each other

65. Euler & Denk (2004) Stiefel & Sejnowski (2007) Strength and structure of inputs complement each other

66. Inputs modulate both rate and timing Kuffler (1953) Increase in stimulus intensity Stimulus onset 50 ms

67. Inputs modulate both rate and timing Fries et al. (2007) Input

68. Inputs modulate both rate and timing Lengyel et al. (2005)Stiefel et al. (2005)

69. Summary V – Neuronal codes in the visual system… are often brought into conceptual competition although in every day vision, they coexist naturally can rarely be tested directly to find out whether they are crucial for perception are diverse and have all proven successful in different visual tasks and circumstances

70. The code is… Everything.