Motor Systems (Lecture 11)

1. Motor Systems(Lecture 11) Harry R. Erwin, PhD COMM2E University of Sunderland

2. Resources • Nicholls et al. (well-referenced, possibly the best for neuroscientists) • Kandel et al. (medical school textbook) • Shepherd (general textbook) • Johnston and Wu (emphasizes neurophysiology, I don’t have) • Avis Cohen (UMd Bio 708C course notes) • Bower and Beeman, The Book of Genesis, ch 8.

3. Outline • Motor System Architecture • The Muscle • Central Pattern Generators • Motor Systems in the CNS • Biologically-Inspired Motor System Models • Book of Genesis, Chapter 8

4. Motor System Architecture • Hierarchical • Cortex • Cerebellum • Brainstem • Spinal cord • Motoneurons • Muscles

5. Muscles • Muscles are springs and correct for errors. As for any spring, oscillation can be an issue, but the muscle controls it. The muscle thinks! • A muscle is made up of multiple muscle fibers—multinucleate cells in mammals that contain myosin and actin (elastic). These are excitable cells like neurons. • In higher vertebrates, each fiber is innervated by a single motoneuron, but a single motoneuron can innervate many fibers of a single type. Fine motor skills can involve one fiber/one neuron, though more usually about 20. (That makes it interesting that H. erectus had a significantly smaller spinal cord diameter than H. sapiens.)

6. Muscle Fibers • Several types of muscle fibers (the first three listed are important): • SS use oxidative metabolism, are weak, do not appear to fatigue, have a role in maintaining posture. • FR are fatigue-resistant, use both oxidative and non-oxidative enzymes, are stronger, and their motoneurons have intermediate input resistance and rheobase (current threshold for initiating a spike). • FF fatigue rapidly, are non-oxidative (glycolysis), are strongest with low input resistance and high rheobase. • SS are recruited first, followed by FR, and finally FF. • S are slow and have a slow relaxation time.

7. Muscle Contraction Mechanisms • Individual fibers ‘twitch’. Muscle contraction uses multiple muscle fibers twitching in a pattern. This is non-stochastic— action potentials always work. • Myosin and actin are connected by protein bridges. The angles of these bridges define the force that can be exerted, depending on the type of myosin. • Muscles maximize force when stretched. Going beyond that maximum length (‘pulled’), they have no force. • Muscle fibers respond to action potentials (ACh) allowing entry of Ca++. Cramps reflect a calcium deficit.

8. Muscle Efferents • Axon terminals synapse on the fiber. The AP must invade entire fiber. • ACh is the neurotransmitter. Vesicle release leads to diffusion across the junction, where it binds to receptors and is hydrolyzed by AChE. Binding opens cationic (non-specific) channels and the membrane potential collapses.

9. Spindle Systems • Some muscle fibers are stretch receptors, rather than force producers. • Lack contractile elements. • Types include primary and secondary muscle spindles. • Primary spindles report that the fiber they’re monitoring is carrying force. Measure rate of change, allowing you to control velocity. • Secondary spindles measure tension directly. • Not servo control!—the muscle turns on first, control is later.

10. The Spinal Cord • Organized functionally • Characterized by fully coordinated rhythmic movements. • Consists of fused dorsal and ventral roots surrounding a central canal • Dorsal roots are sensory (stretch receptors, pain, touch, joint position) • Ventral roots contain motoneurons • Organized locally into central pattern generators (CPGs) • Ends at the level of the kidneys.

11. Rhythmic Movements • Questions for research: • How do they happen? • What do they mean? • Where do they come from? • Reflex chain? • Sequential pattern of activation? • Reverberatory circuits? • Cutting the spinal cord removes the inhibition of flexion/extension movements (Brown, 1914). • These issues are also present in the CNS, but the two communities do not interact much.

12. Central Pattern Generators (CPGs) • A Central Pattern Generator is a system of neurons that can generate a stereotyped rhythmic movement without sensory afference or somatic feedback. • It can be activated/sustained by a triggering stimulus (either tonic or phasic), but requires no modulation of the input to generate the basic pattern. • Lundberg and Grillner had a nasty argument on whether CPGs are present in the motor system. This is Avis Cohen’s specialty.

13. Research into CPGs • To demonstrate the existence of a CPG: • The stereotyped movement must not be extinguished by the removal of varying sensory afference. • The movement must not be extinguished by the removal of somatic feedback. • Experiments beginning in 1960s produced evidence for CPGs. Russian studies of decorticated cats showed they could maintain walking motion without a cortex. • Hence the cortex turns walking on. • Strength of stimulation controls power, not frequency. • Gait changes are automatic. • Limbs controlled as a whole. • Primitive mammal-like reptiles give insight here. (discuss)

14. CPGs and the Spine • CPG models have been effective in describing how coordinated rhythmic movements might be generated in the spinal column. • Involves interneurons (Renshaw cells) in the spinal cord. However, these can be turned off and the animal still walks. • Most motor actions are indirectly managed using opposing pairs of muscles controlled by a CPG. Motor cortex neurons synapse on the spinal interneurons (and directly on the motoneurons used in delicate finger movements). • The Renshaw cells are driven hard.

15. CPGs in Context • CPGs seem to generate body shape, not force commands. • An acute spinalized curarized deafferented cat still walks. • CPG does not require sensory feedback • CPG does not require descending control • Reflex loops do not operate during locomotion. The spinal cord decides whether you step on something sharp. Corrections and adjustments to ground features are all handled by the CPG.

16. Motor Systems in the CNS • Motor cortex (and related cortices) • Basal ganglia • Cerebellum • Brain-stem nuclei

17. Issues • How are CPGs controlled by the CNS? • Do as I do? • Do as I say? • Do as I suggest? • Is the response • An act? (time-dependent) • A place? (autonomous) • A combination?

18. Hypothesis • Motor commands are suspected of specifying “an image of attainment”. • Passive—posture • Active—an action

19. Mechanisms of Coordination • Preservation of phase relationships • Non-linear • Developmentally tuned • Phase differences are fixed • CNS provides drive • Spine returns periodic signal to the CNS • Mutual entrainment of spine and CNS

20. Motor Cortex • Probably uses dense (vector) rather than sparse coding. • Specifies terminal position of movement in world-centered coordinates. • The spinal cord seems to work in body-centered coordinates. Giszter et al. claim that there are only four degrees of freedom in the spinal cord of frog. • Cerebellum may be responsible for the change of coordinates.

21. Cerebellum • Seems to be a sensory system (Bower) • Receives motoneuron and sensory copies, via separate pathways. • Outputs a periodic inhibitory signal to the spinal cord • Very fast response • Extremely large primary (Purkinje) cells. • Need to be modeled below the ion channel level. 5000 compartments are typical.

22. Basal Ganglia • Play a role in willed (rather than stimulus-triggered) acts. • No convergence in the striatum. Multiple parallel modules. • Convergence at the Globus pallidus (GP). • Feedback loop: • Cortices (PM, SuppM, M, SS) • Putamen (part of the striatum, hard to excite, hence sensitive to synchronization, input from Substantia nigra—Parkinson’s) • GPext (feedback relationship with the SubThalNuc, inhibited by Putamen) • GPinf and SNreticulata (excited by GPext and inhibited by Putamen) • Thalamus (inhibited by GPinf and SNR, excites the prefrontal and premotor cortex).

23. Biologically-Inspired Motor System Models • Two-way street all the way down. The cortex should command a CPG, which then drives the cortex in return, signals the motoneurons, and interacts with sensory afferents. • This is a universal picture, using comparisons at all levels. See Rodney Brooks’ robot models. • Note that if feedback is specific, cell-to-cell-back-to-original cell (as in the inferior colliculus), this supports a back-propagation model for training (not just reinforcement learning!).

24. Back to Central Pattern Generators • The neuronal circuits that support rhythmic muscle contractions are referred to as central pattern generators (CPGs) • These circuits can generate this activity in isolation. • The ability to switch between CPGs relies on feedback from proprioceptors and higher CNS control.

25. Two-Neuron Oscillators

26. The Math of the System • di(t) = i(in modular terms) • i(t) = (i + i(0) ) (mod 2) • When coupled, we get: • d1(t) = 1 + h12(1, 2) • d2(t) = 2 + h21(2, 1) • (t) = 1(t) - 2(t) • This describes the phase lag of oscillator 2 relative to oscillator 1.

27. Calculating • d(t)/dt = d1(t)/dt - d2(t)/dt = (1- 2)+(h12(1, 2) - h21(2, 1)) • Now assume hijis a function of 1-2, and is zero at zero. This is called diffusive coupling. • For the phase lag to remain constant, • d(t)/dt = 0. • If hij is proportional to the sin of the difference, we get different solutions depending on the constant of proportionality, depending on the ratio of 1 - 2 to the sum of the constants of proportionality.

28. Why is this interesting? • It suggests that the typical behaviour of CPGs should move from phase drift to phase-locking and potentially ‘oscillator death’ for large networks.

29. Four Neuron Oscillators • Show similar but more complex behaviour. • Representative to fish swimming (and by implication of tetrapod walking) • Gives some insight into how gaits might be modelled. • Gives us confidence that our models allow us to understand more complex CPGs.