1. What are we trying to explain? Multiple facets of a simple behavior Stephen G. Lisberger Howard Hughes Medical Institute W.M. Keck Center for Integrative Neuroscience Department of Physiology, UCSF

2. What we are trying to explain

3. What does Barry have to do to hit a home run? Sense ball motion and determine speed and direction of motion. Decide whether or not to swing the bat. Program the correct swing to meet the ball just in front of his right foot. Make any corrections as the ball “moves”. All in 400 ms!

4. What we are trying to explain, simplified

5. Methods for studying pursuit eye movement

6. The behavior: pursuit eye movements

7. Smooth pursuit in the time domain

8. Target motion versus image motion

9. Anatomy of pursuit Why are there so many different parts to the pursuit circuit? Perhaps each part of the circuit is doing a different computation. Caudate nucleus

10. The signals recorded in different areas are more similar than different: consider motor cortex (FPA), parietal cortex, and the cerebellum during pursuit Transient and sustained responses Directional tuning Firing related to target speed Similar latencies

11. Neurons in the FPA fire nicely during pursuit

12. Neurons in the cerebellum also fire nicely during pursuit

13. Even neurons in the parietal cortex -- in area MST -- fire nicely during pursuit

14. Neural responses during the behavior don’t distinguish different parts of the pursuit circuit -- now what? It’s especially troubling that all of these areas combine similar blends of signals related to eye velocity, head velocity, and visual motion. The same would be true in the basal ganglia, probably. The same would probably be true for neural activity related to Barry’s home run swing. So, let’s look at the behavior and see if we can use similar signals to support different behavioral features.

15. What are we trying to explain -- the many features of the pursuit behavior. Smooth eye movement persists without image motion. Population decoding for visual-motor transformations (probably done at multiple levels) On-line gain control Target choice in real-world situations Long-term adaptive changes (learning)

16. What are we trying to explain -- the many features of the pursuit behavior. Smooth eye movement persists without image motion. Population decoding for visual-motor transformations (probably done at multiple levels) On-line gain control Target choice in real-world situations Long-term adaptive changes (learning)

17. Natural inputs show that image motion goes away during accurate pursuit. Visual input consists of a big pulse of image motion …followed by a time of very little image motion and big sustained eye motion

18. Continued tracking during image stabilization provides evidence for “eye velocity memory”

19. A simple feedback controller won’t show velocity memory, and requires high internal gain to achieve excellent tracking + g TE+ - I E = g I = g (T – E) = gT – gE E + gE = gT E (1 + g) = gT Gain = E / T = g / (1+g) Gain is 0.5, if g = 1 Gain is 0.9, if g = 9 Gain is 0.99, if g = 99

20. An idea about how to create velocity memory with a positive feedback circuit What discharge would we expect if we recorded from F? Signals related to eye velocity. MST, FPA, and the cerebellum all potentially qualify. But this is a nuts- and-bolts function and we imagine it is supported by cerebellar circuits. Lisberger, Exp. Brain Res. Suppl 6: 501-514, 1982.

21. What are we trying to explain -- the many features of the pursuit behavior. Smooth eye movement persists without image motion. Population decoding for visual-motor transformations (probably done at multiple levels) On-line gain control Target choice in real-world situations Long-term adaptive changes (learning)

22. How do we think about visual-motor transformations in terms of neural circuits? Population code for image motion Preliminary command for eye velocity Motor command Parieto-ponto-cerebellar circuit

23. How do we think about visual-motor transformations in terms of neural circuits? MT Preliminary command for eye velocity Motor command Parieto-ponto-cerebellar circuit

24. What are we trying to explain -- the many features of the pursuit behavior. Smooth eye movement persists without image motion. Population decoding for visual-motor transformations (probably done at multiple levels) On-line gain control Target choice in real-world situations Long-term adaptive changes (learning)

25. So far, we’ve treated pursuit as a negative feedback control system with velocity memory retina Feedforward Gain Next, I’ll show that the feedforward gain of pursuit is variable. Gain control is another feature of pursuit that we need to understand at the level of neural circuits.

26. Use of perturbations to probe the gain of visual-motor transformations for pursuit Research of Joshua Schwartz Target position

27. Use of perturbations to probe the gain of visual-motor transformations for pursuit Research of Joshua Schwartz Target position

28. Use of perturbations to probe the gain of visual-motor transformations for pursuit Research of Joshua Schwartz Target position

29. Use of perturbations to probe the gain of visual-motor transformations for pursuit Research of Joshua Schwartz Target position

30. Perturbations cause large responses during pursuit: the gain is set to “high” During fixation During pursuit

31. Perturbations cause small responses during fixation: the gain is set to “low” During fixation During pursuit

32. Pursuit is a negative feedback control system with a variable feedforward gain retina Variable Gain

33. How do we think about gain control in terms of neural circuits? MT Vector averaging Preliminary command for eye velocity X Motor command Parieto-ponto-cerebellar circuit Site of gain control Gain control circuit ?

34. Stimulation in FPA evokes smooth eye movements

35. Stimulation of FPA enhances the response to a perturbation delivered during fixation

36. Enhancement is in the direction of the perturbation, not in the direction of the evoked eye movement

38. How do we think about gain control in terms of neural circuits? MT Vector averaging Preliminary command for eye velocity Frontal Pursuit Area X Motor command What are the input signals that allow the frontal pursuit area to control the gain of the visual-motor transformation? Parieto-ponto-cerebellar circuit Parieto-frontal circuit Site of gain control

39. How do we think about gain control in terms of neural circuits? MT Vector averaging Preliminary command for eye velocity Frontal Pursuit Area X Motor command Vision needs to be one input signal, since image motion has to turn the pursuit system on and initiate smooth tracking. Parieto-ponto-cerebellar circuit Parieto-frontal circuit Site of gain control

40. How do we think about gain control in terms of neural circuits? MT Vector averaging Preliminary command for eye velocity Frontal Pursuit Area X Motor command Feedback of motor command The frontal pursuit area needs to represent eye velocity as well as image velocity. (Note another positive feedback loop, like the one through the cerebellum) Parieto-ponto-cerebellar circuit Parieto-frontal circuit Site of gain control

41. Now we know why the FPA and cerebellum have very similar outputs during pursuit FPACerebellum

42. Now we know why the FPA and cerebellum have very similar outputs during pursuit FPACerebellum Uses image motion to initiate pursuit by increasing the internal gain of pursuit. Uses eye velocity to keep the internal gain of pursuit high when image motion vanishes because of perfect tracking. Uses image motion to initiate pursuit by driving eye acceleration. Uses eye velocity to keep a moving eye moving when image motion vanishes because of perfect tracking. Same basic signals, same basic positive feedback circuit, same basic goal of maintaining excellent tracking when the sensory input to the system goes away. Operating at very different levels of the motor hierarchy, one at a modulatory level, one at a nuts-and-bolts level.

43. What are we trying to explain -- the many features of the pursuit behavior. Smooth eye movement persists without image motion. Population decoding for visual-motor transformations (probably done at multiple levels) On-line gain control Target choice in real-world situations Long-term adaptive changes (learning)

44. Anatomy of pursuit How does it work when each part is doing the same neural computation? Caudate nucleus

45. Collaborators Anne Churchland Mark Churchland Rich Krauzlis Ed Morris Nicholas Priebe Joshua Schwartz Leeland Stone Masaki Tanaka Albert Fuchs