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LGN Function and Efficient Coding in Visual Processing

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52 diapositives · 620 Views · Sep 24, 2014

Understand how the Lateral Geniculate Nucleus transmits visual information and explore the efficiency of coding natural scenes. Learn about spike timing precision, spike count reliability, and bursting phenomena. Visit www-biology.ucsd.edu/labs/reinagel/ for more details.

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

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

  4. Retina LGN Cortex Reinagel & Reid 2000

  5. LGN response to purely temporal stimuli Luminance Repeat • 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? Reinagel & Reid, 2000

  6. precision

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

  8. Temporal patterns conserved across animals Reinagel & Reid 2002

  9. Temporal precision of visual information a b c d e 100 Mutual Information (bits/s) 50 0 0.5 1 2 4 8 16 32 64 128 Precision of spike times used (ms) Theory of Shannon, 1948 Method of Strong et al., 1998 Result of Reinagel & Reid, 2000

  10. Mechanisms Underlying Precise Timing Pouille & Scanzian 2001

  11. reliability

  12. Mean 4 Variance 0 Deterministic Poisson Mean 4 Variance 4

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

  14. LGN vs. Poisson Model PSTH PSTH

  15. LGN Variability << Poisson 1 Poisson 0.75 Fano Factor 0.5 0.25 LGN 0 0 100 500 1000 bin size T (msec)

  16. Variability increases from retina to cortex 1 Fano Factor at ~ 40 Hz 0 LGN V1 RGC Kara, Reinagel & Reid, 2000

  17. When firing rate is high, variability is low V1 LGN RGC 200 Firing Rate 0 1 FF 0 0 500 Time (ms) Kara, Reinagel & Reid, 2000

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

  19. Estimating refractoriness from data model Method: Berry & Meister 1998 probability data 0 10 20 30 40 50 60 70 80 90 100 ISI

  20. Recovery Function 1 absolute and relative refractoriness recovery function 0.5 0 0 5 10 15 20 25 30 35 time since last spike (ms)

  21. Free Firing Rate 500 free 400 300 firing rate (sp/s) 200 100 observed 0 0 500 1000 time (ms)

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

  23. Variability increases from retina to cortex 1 Fano Factor at ~ 40 Hz 0 LGN V1 RGC Kara, Reinagel & Reid, 2000

  24. V1 RGC Refractoriness decreasesfrom retina to cortex 1.0 0.5 Recovery Function 0.0 0 10 20 30 Time (ms) 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

  26. bursting

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

  28. Bursting in the LGN • 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 • 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

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

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

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

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

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

  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. Spatial correlation in natural images Natural Image Correlation Power spectrum 1 2 10 0.8 0.6 0 10 0.4 -2 0.2 10 -40 -20 0 20 40 0 2 10 10 distance cycles/degree (cf. Field 1987; Tadmore & Tolhurst; Ruderman & Bialek; van Hateren)

  42. 4 1 10 0.8 2 10 0.6 0.4 0 10 0.2 -2 0 10 -20 0 20 -2 0 2 10 10 10 4 1 10 0.8 2 10 0.6 0.4 0 10 0.2 -2 0 10 -20 0 20 -2 0 2 10 10 10 Natural Image Correlation Power spectrum Spatial frequency Distance (pixels) (cf. Barlow 1961)

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

  44. 2 10 0 10 luminance -1 10 -2 10 0 1 2 3 4 time (s) Correlation Power Spectrum 1 0 0.8 10 0.6 0.4 -5 10 0.2 0 -2 -1 0 1 2 -1 0 1 3 10 10 10 10 distance (sec) 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

  47. 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. A B C 300 7 1 6 250 0.8 5 200 0.6 4 150 Information (bits/s) Efficiency (bits/bit) Information (bits/spk) 3 0.4 100 2 0.2 50 1 0 0 0 white natural white natural white natural Bullfrog Auditory Neuron: Natural Stimulus is ‘Optimal’ Rieke, Bodnar & Bialek 1995