Lecture 4: Motor Control

1. Lecture 4:Motor Control Prof.dr. Jaap Murre University of Maastricht University of Amsterdam jaap@murre.com http://neuromod.org

2. Overview • The anatomy of the motor system • Population coding • The role of spinal cord, cerebellum, and basal ganglia • This is not in the book, but can be on exam anyway

3. Muscles are activated by alpha motor neurons

4. The stretch reflex reveals some elementary processing in the spinal cord

5. Cortical anatomy of the motor system: lateral view

6. Medial view

7. Schematic overview of the motor system

8. Basic questions regarding motor control can nowadays be answered • How are motor movements represented in the brain? • How are they used in the production of movement? • Which brain areas are involved?

9. How to be precise with noisy components Area 5 neuron during repeated reaching movements: each individual trial gives a rather imprecise signal

10. Population coding • Population coding allows precise representations on the basis of (very) noisy or even damaged components • Population coding is based on the statistics of averages • They rely on coarse-coded neural representations

11. Coarse coding • If a neuron’s representation responds to ‘many’ inputs, this is called coarse coding • The advantage is that more accurate representations can be formed by suitable combination of the coarse representations

12. Why coarse coding works • If we move along a straight line, each time we cross a receptive field boundary one neurons changes its activation: • the representation changes.

13. In primates abundant evidence exists for coarse coding

17. Georgopoulos shows that movement is coded in population vectors

18. Population vectors give accurate movement direction signals

19. Motor cortex sets up the signal, but execution is dependent upon other areas

20. Original plans in motor cortex are sometimes revised ‘on the go’

21. Activation of motor areas is a cascade rather than a sequence

22. Simple movement activations motor cortex and somatosensory cortex

23. More complicated sequences involve other areas SMA = supplementary motor area (part of area 6)

24. Imagined movements remain limited to the supplementary motor area (SMA)

25. Internally and externally generated movements PMC = premotor cortex (also part of area 6)

26. Skilled (Old) versus new motor movements

27. ‘Elastic constraints’ in motor development • The problem of grasping is overdetermined: given an end-location, many possible joint positions solve the problem • In order to make the problem soluble ‘elastic constraints’ are necessary (cf. Mike Jordan) • Muscles (as ‘springs’) are one source of such constraints

28. Spinal cord

29. Coarse maps of limb movements in the frog • Spinal cord of frogs does significant motor processing • Frog can still ‘clean’ itself after severing of cord (dogs can also still scratch themselves) • The data suggest that even at a spinal level coarse coding is used • It is likely that similar types of coding are used in mammals

30. Cats with severed spinal cord could still walk on a treadmill

31. Method followed by Emilio Bizzi Based on the idea of ‘muscles as springs’ by Feldman

32. Limb movements in frog spinal cord are coded with respect to their end-positions

33. The interactions of force fields can be described by vector calculus Fields A and B combined predict field <AB> (see C). When A and B are stimulated the resulting field (see D) corresponds to the theoretical field <AB>

34. Cerebellum

35. Glickstein: it is not ‘completely’ clear what the cerebellum does • Bimanual control • Motor learning? • vestibuloocular reflex • nictitating membrane response • Coordination and integration of movements

36. Global anatomy of cerebellum

37. More detailed anatomy of cerebellum

38. Louis Bolk: midline cerebellar vernis controls bilaterally synchronized movements; cerebellar hemispheres control unilateral movements

40. Synaptic connection Parallel fibers Purkinje cell David Marr (1969): cerebellum is excellent for simple associative learning (conditioning)

41. Hebb-Marr networks • Marr’s views can be combined with those of Hebb to yield associative networks • These networks can store input-output patterns (hetero-associative learning) • The exhibit • pattern completion or content-addressable memory • fault tolerance

42. Willshaw networks • Highly abstracted, early neural network from 1969 • Activations are 0 or 1 • A weight either has the value 0 or 1 • A weight is set to 1 if input and output are 1 • At retrieval the net input is divided by the total number of active nodes in the input pattern

43. Example of a simple heteroassociative memory of the Willshaw type 1 0 0 1 1 0 0 0 1 0 1 1 1 1 0 1 0 0 0 0 1 0 1 1 1 0 1 0 1 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

44. Example of pattern retrieval (1 0 0 1 1 0) 0 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 3 2 2 3 3 2 Sum = 3 Div by 3 = 1 0 0 1 1 0

45. Example of successful pattern completion using a subpattern (1 0 0 1 1 0) 0 0 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 2 2 1 Sum = 2 Div by 2 = 1 0 0 1 1 0

46. Example graceful degradation: small lesions have small effects (1 0 0 1 1 0) 0 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 3 2 1 2 3 1 Sum = 3 Div by 3 = 1 0 0 0 1 0

47. ‘Correspondence views’ on brain structures

48. Computational views on cerebellum

49. Summary • Like vision, motor behavior has a lot of special purpose circuitry • We can understand many aspects of this circuitry in terms of ‘why this representation makes sense’ • For example, coarse grained coding has the advantage of precise control despite noisy components

50. Summary (continued) • Motor behavior is not simply stringing together some basic movements • Motor planning and execution are very much cognitive functions