1. Reinagel lectures 2006 Take home message about LGN 1. Lateral geniculate nucleus transmits information from retina to cortex 2. It is not known what computation if any occurs in the LGN 3. For white noise stimuli, responses are precise and reliable 4. PRECISION is the trial to trial jitter in spike TIMING (order 1msec) feed forward inhibition may be the mechanism of precise timing 5. RELIABILITY is the trial to trial variability in spike NUMBER (subpoisson) refractoriness may be the mechanism of reliable spike count 6. BURSTING in the LGN is a distinct biophysical phenomenon, of unknown importance. The *right* question to ask is whether the bursting state is visually primed and whether priming itself encodes information 7. We now have a visually behaving rodent prep to address all these questions Take home message about efficient coding 1. Natural scenes are full of spatial and temporal correlations 2. This suggests WHY center-surround RF's are GOOD: redundancy reduction 2. Test: LGN responses to natural scenes are decorrelated (whitened) 3. More generally: are natural scenes optimal stimuli? is this even the right question? www-biology.ucsd.edu/labs/reinagel/

2. LGN Retina Cortex Ramon y Cajal Hubel 1960 (alert cat) Hubel & Wiesel 1961 (anesthetized) Lateral Geniculate Nucleus

3. spiking inputs intrinsic properties local circuits cortical feedback Gating? Attention? Binding? Prediction testing? Nothing? What happens in the LGN?

4. LGN Retina Cortex Reinagel & Reid 2000

5. LGN response to purely temporal stimuli Luminance Repeat Reinagel & Reid, 2000 Descriptive questions: how precise is the timing? how reliable is the number? are there internal patterns? In each case: visual information? mechanism of encoding? mechanism of decoding?

7. PSTH peaks are milliseconds wide Reinagel & Reid, 2002

8. Reinagel & Reid 2002 Temporal patterns conserved across animals

9. Precision of spike times used (ms) 0.51248163264128 0 50 100 Mutual Information (bits/s) abcde Temporal precision of visual information Theory of Shannon, 1948 Method of Strong et al., 1998 Result of Reinagel & Reid, 2000

10. Mechanisms Underlying Precise Timing Pouille & Scanzian 2001

12. Mean 4 Variance 0 Mean 4 Variance 4 DeterministicPoisson

13. Spike Count: Trial to Trial Variability Variance in Spike # Mean Spike # Random Deterministic (Poisson) = 1= 0 Measure of variability

14. PSTH LGN vs. Poisson Model PSTH

15. 01005001000 0 0.25 0.5 0.75 1 bin size T (msec) Fano Factor LGN Variability << Poisson LGN Poisson

16. Variability increases from retina to cortex Fano Factor at ~ 40 Hz 0 1 RGC LGNV1 Kara, Reinagel & Reid, 2000

17. FF Firing Rate 200 0 0 1 RGC LGN V1 0500 Time (ms) When firing rate is high, variability is low Kara, Reinagel & Reid, 2000

18. Refractoriness Regularizes? PSTH Poisson model Poisson with Refractory Period

19. 0102030405060708090100 probability ISI data Estimating refractoriness from data Method: Berry & Meister 1998 model

20. 05101520253035 0 0.5 1 time since last spike (ms) recovery function Recovery Function absolute and relative refractoriness

21. 05001000 0 100 200 300 400 500 time (ms) firing rate (sp/s) observed free Free Firing Rate

22. Refractory models for all cell types 0 1 2 Fano Factor 0 200 0 Time (ms) V1 LGN RGC Kara, Reinagel & Reid, 2000

23. Variability increases from retina to cortex Fano Factor at ~ 40 Hz 0 1 RGC LGNV1 Kara, Reinagel & Reid, 2000

24. Refractoriness decreases from retina to cortex Recovery Function 0 102030 Time (ms) 0.0 1.0 0.5 V1 RGC Kara, Reinagel & Reid, 2000

25. Summary of Reliability Spike count has sub-Poisson variability High FR  High Reliability Refractoriness completely explains Noise is low, but doubling each synapse - firing rate is decreasing - refractoriness is decreasing

27. Thalamic Bursts (I t ) Jahnsen and Llinas (1984) Hubel and Wiesel (1961)

28. Bursting in the LGN dominate during sleep, when vision is suppressed frequent under anesthesia, when vision is absent almost never seen in alert animals, when vision is happening ERGO Bursts are irrelevant to vision not rhythmic or synchronous in anesthetized animals visual in anesthetized animals synapses prefer bursts do occur in alert animals, and rare signals can be important cool computational ideas ERGO Bursts are crucial to vision

29. -0.6-0.4-0.20 -0.1 0 0.1 0.2 Time before spike (s) Before a burst Before a tonic spike Optimal Guess of Stimulus Visual inputs trigger bursts 0 0.5 1 1.5 Bits/event 0 0.05 0.1 0.15 0.2 Coding Efficiency Burst Tonic Reinagel, Godwin, Sherman & Koch 1999

30. Bursts: Triggering vs. Priming time active inactive LT-Ca ++ channel state AP times Trigger synaptic input * Ca ++ spike observable

31. Bursts in LGN are distinct code words Denning & Reinagel 2005 Alitto, Weyand & Usrey 2005 Lesica & Stanley 2004

32. Summary: Bursting LGN neurons have 2 states Visual inputs trigger responses in both states Visual inputs also control the state BUT All this is under anesthesia What about alert? - Stimulus ensemble matters - Behavioral state may also - Triggering and priming

33. spiking inputs intrinsic properties local circuits cortical feedback What happens in the LGN?

34. Directions Do bursts occur and are they visual in alert animals? Function of cortical feedback to the LGN? Does precision in the LGN matter for perception?

35. Flister, Meier, Conway & Reinagel (unpub) An awake behaving rodent prep for vision Thanks to collaborators at CSHL

36. Bursts in LGN in the awake, behaving rat Flister, Meier & Reinagel (unpub)

37. [break]

38. Center Surround Opponent RFs Kuffler 1958

39. Natural scenes are spatially correlated

40. Spatial correlations in unnatural images

41. Natural Image -40-2002040 0.2 0.4 0.6 0.8 1 distance Correlation 10 0 2 -2 10 0 2 Power spectrum cycles/degree Spatial correlation in natural images (cf. Field 1987; Tadmore & Tolhurst; Ruderman & Bialek; van Hateren)

42. Natural Image -20020 0 0.2 0.4 0.6 0.8 1 Distance (pixels) Correlation 10 -2 10 0 2 -2 10 0 2 4 Power spectrum Spatial frequency -20020 0 0.2 0.4 0.6 0.8 1 10 -2 10 0 2 -2 10 0 2 4 (cf. Barlow 1961)

43. 01234 10 -2 10 10 0 time (s) luminance 01 0 0.2 0.4 0.6 0.8 1 Distance (sec) Correlation 10 10 0 1 2 3 -6 10 -4 10 -2 10 0 2 Power Spectrum Temporal frequency (Hz) Natural temporal stimulus (cf. Dong & Atick 1995; van Hateren 1997)

44. 01234 10 -2 10 10 0 time (s) luminance -2012 0 0.2 0.4 0.6 0.8 1 distance (sec) Correlation 10 10 0 1 3 Power Spectrum 10 -5 10 0 2 Temporal frequency (Hz) (cf. Dan Atick & Reid 1996)

45. + Barlow 1961 Redundancy Reduction Hypothesis + Sensory neurons decorrelate natural inputs to reduce redundancy

46. Dan, Atick & Reid 1996

48. Whitening in the fly Van Hateren 1997

49. Summary: Redundancy Reduction Shannon 1948: Optimal codes lack redundancy Kuffler 1958: Center-surround receptive fields in retina Hubel 1960: Center-surround RFs in LGN Barlow 1961: Center-surround RFs reduce redundancy for natural scenes Dan, Atick & Reid 1996: Responses in LGN are less redundant for natural scenes

50. Bullfrog Auditory Neuron: Natural Stimulus is ‘Optimal’ Rieke, Bodnar & Bialek 1995

51. Cat LGN Neuron: Opposite result Analog LED Stimuli Reinagel & Reid, in prep.

52. 10 0 1 2 3 1 2 3 4 5 temporal frequency (Hz) Power LGN Responses to full field temporal stimuli Natural visual stimulus Random visual stimulus (replication of Dan et al 1996)