Chapter 9 Motor systems
Motor systems are intrinsically rather more complex than sensory ones: an unfortunate consequence is that we know rather less about them. It may be helpful to begin this introductory chapter by asking why this should be so.
Why studying motor systems is difficult
An obvious way to study the motor system, by analogy with recording from sensory systems, is to stimulate various bits and see what happens. We saw in chapter 1 that there are certain difficulties about doing this, to do with the difficulty of knowing what is an appropriate pattern of stimulation to apply in order to get a response.
A further difficulty in using stimulation to study the motor system is that whereas sensory systems by and large progress in a straightforward way from level to level, a characteristic of motor control is that every action necessarily results in sensory feedback. Lift your hand: there is an immediate influx of sensory activity from the skin, from muscle and joint receptors, from vision and the other special senses as well. This makes the effects of stimulating a particular region of the motor system additionally complex: any movement that may result from it also generates new patterns of afferent activity. Coming up from behind the level at which we are stimulating, this messes things up by altering the pattern of stimulation we are trying to apply.
For both these reasons, electrical stimulation has not proved to be a very helpful way of studying the motor system. A more fruitful approach is an extension of the sensory method. We apply 'real' stimuli to the senses, and trace the resultant patterns of activity as they penetrate deeper and deeper through the levels of the nervous system and emerge triumphantly again at the motor end. In systems as complex as those controlling the human hand, this is not yet technically feasible. But where the number of levels is much smaller, as in more primitive brains like those of insects, or in simpler subsystems of the mammalian brain (like those controlling eye movements, which may have as few as three neuronal levels between input and output) this approach has taught us a great deal.
One general lesson we have learnt is how much more can be discovered about the brain by studying complete systems with an output, compared to purely sensory systems. The visual system is a good example. When people first started exploring it with microelectrodes, at first everything went swimmingly: going systematically from layer to layer into the cortex, investigators such as Hubel and Wiesel found a clear logical progression from ganglion cells to simple cells and to complex cells. People confidently expected this trend to continue, and that after line detectors we would discover square detectors and circle detectors and A-detectors and teacup detectors … But they didn’t; visual neurophysiology began increasingly to lose its way. Why? Because investigating a sensory system without a clear sense of what it is for is like trying to understand a television without knowing that it shows pictures. We may think it is ‘for’ perception; but since we have no idea what perception is (or any other aspect of consciousness) it is hardly surprising if we end up lost. But once we start looking at systems that actually do something tangible, we make progress.
Motor control and feedback
Feedback means using information about results to improve performance, and feedback from the effects of motor responses is fundamentally important in the control of movement. A good way to begin a study of the motor system is to consider just how this sensory information may be used.
No feedback: ballistic control
Of course, one can have a motor system with no feedback at all. A spermatozoon, for example, gets along by a flagellating its flagellum in a way that pays no regard to its orientation: if it is pointing the wrong way, that is just hard luck. Blind behaviour of this kind is not a monopoly of simple organisms: it can often be seen in animals with much more sophisticated motor systems. A classic example is the nest-building behaviour of the brown rat, described by Konrad Lorenz. When a brown rat decides to build a nest, it performs a stereotyped series of actions: it runs out to get nesting material, drags it back to the centre of the nest, sits down and forms it into a sort of circular rampart, pats it down and smoothes it, then runs out to get more material; and so on until the nest is finished. This certainly looks like the intelligent behaviour of an animal that is aware of the consequence of its actions: yet a simple experiment shows it to be nothing of the kind. If it is not given enough to make a nest, the rat still runs out to grab the (non-existent) material, and goes through the motions of dragging it back, forming it into a rampart and patting and smoothing it, even though in reality there is nothing there. Feedback, in other words, was not in fact being used. And of course we ourselves have all had the experience of carrying out some equally skilled and complex series of actions – making tea, for example – and have embarrassingly revealed its stereotyped, robotic nature by for instance absent-mindedly putting coffee instead of tea into the teapot.
Besides, there are many circumstances when our motor systems are forced to act blindly because for one reason or another they are deprived of normal sensory feedback. When I nonchalantly toss some rubbish into a waste bin, once it has left my hand, no amount of sensory feedback about its trajectory is going to enable me to modify its flight. I have clearly had to work out beforehand the precise sequence of motor commands necessary in order to produce the correct pattern of muscular contractions that I need to achieve my goal. Motor acts of this type are called ballistic - a word meaning 'thrown' - and their control can be represented schematically by a diagram like that of Fig. 9.1. Diagrams of this kind are central to understanding control systems, and to understand them we need to get to grips with a certain amount of rather unattractive jargon.
Fig. 9.1 Ballistic
Here we start with the desired result or goal, in this case that we want the rubbish in the bin. This is translated by a controller into an appropriate pattern of commands. These commands produce the actual result through their effect on what engineers call the plant - in this case, the body's muscles. If the controller is functioning properly, then the actual result will equal the desired result: the rubbish will end up in the bin. How well all this works depends on good the controller is: the more it knows about how the plant will behave in response to any particular command, the better it will perform. It needs something like a library of motor programs suitable for different acts, where it can look up the rule needed to translate a particular desire into an appropriate command. A well known example of such a system is the control of ballistic missiles. Here the desired result is the destruction of some particular portion of the globe; computations are then made, based on knowledge of the missile’s characteristics, to determine such parameters as where to point it and how large a thrust is required at take-off: but once it is launched no further action can be taken beyond hoping that the calculations were in fact correct.
Ballistic control is conceptually simple, but it has a fatal defect: it is extremely vulnerable to what systems engineers call noise. Noise is any kind of unpredictable disturbance that makes the actual result different from what the controller expects. If the wind happens to be blowing the wrong way, our ballistic missile may arrive somewhere embarrassing and cause a diplomatic incident. Because the world we live in is never entirely predictable – nothing is certain - a given set of motor commands will never produce quite the same result twice running. A particular pattern of activity in motor nerves will produce different movements of a limb on different occasions, depending on a host of internal factors such as body temperature, fatigue, the amount of energy available, and so on. Even more important in motor systems is the effect of load. When we use our limbs to shift things around, carrying or throwing, a given degree of muscle activity will generate quite different movements, depending whether we are dealing with a lump of rock or a feather. As we shall see, most of the lower levels that control the limbs are devoted to solving this problem of achieving the movements we want despite the noise introduced by unpredictable loads.
Parametric adjustment: feed-forward and feedback
One way of dealing with noise is to have some kind of sensor that monitors the noise before it affects the system, and use this information to adjust the parameters of the controller to allow for it. This kind of modification of the controller parameters to anticipate the effects of noise is called parametric feed-forward (Fig. 9.2). Thus if we measure the speed of the wind before we launch the missile, we can allow for its disturbing effects. Much sensory information is used by the brain in this way, especially in making allowance for the effects of different loads. We shall see later that the neural circuits controlling muscle length use information from force-detectors in the skin and tendons that monitor load in order to make appropriate adjustments of motor commands.
Fig. 9.2 Parametric feedforward
But even this approach is doomed to failure, for in general there are infinitely many things that might cause perturbations, and the brain clearly cannot have a plan for dealing with every one of them in advance. Instead of trying to anticipate absolutely everything that might possibly occur, one solution is to take a more pragmatic approach, with a system that learns from its own mistakes, using not feed-forward but parametric feedback (Fig. 9.3). This introduces two exciting new pieces of jargon: a comparator compares the actual result with the desired result by subtracting one from the other, and it generates an error signal that is used to modify the controller’s parameters. In general, the error signal is a measure of how well the system is coping: if it is zero, the controller is doing a good job, and there is no reason to change its parameters. If the system keeps on making a mess of things, generating persistent error signals, then the commands are gradually adjusted until it gets it right. The advantage of this approach is its flexibility. Rather than requiring stored programs ready in advance for any conceivable kind of action, by starting with rather simple, all-purpose programs, one may refine, through trial and error, what is needed for the tasks that are actually encountered.
Fig. 9.3 Parametric feedback
It goes without saying that this kind of behaviour - using error information from one attempt to improve performance on the next - is highly characteristic of the way in which our motor systems learn to execute complex actions. In playing darts, a novice may at first use pre-existing programs developed perhaps from experience of throwing other objects such as cricket balls, no doubt ultimately from throwing rattles out of his pram. But as he practises, the feedback from each throw, though obviously arriving too late to use immediately, is used to reduce future errors, and in the end he may gradually evolve very accurate programs specifically for dart-throwing. A great deal of the learning of motor skills can usefully be thought of as a parametric feedback of this kind, in which errors are used to modify our stored motor programs.
A specific example, discussed in more detail in Chapter 11, is the vestibulo-ocular reflex. When we move our head, the resultant signals from the semicircular canals are used to drive the eyes by an equal amount in the opposite direction. As a result, they maintain the direction of gaze in space and the retinal image of the outside world remains relatively fixed. This is clearly a ballistic system. Equally clearly, it will go off the rails if the performance of the muscles is degraded through fatigue or disease, or if there is some malfunctioning of the canals. It turns out that the reflex is continually adjusted to ensure that the eye movements really are equal and opposite to the head movement. The error signal in this case comes from neurons that respond to movement of the visual image across the retina; for this retinal slip only occurs when head and eye movement are not matched.
Though parametric feedback and feed-forward can vastly improve the performance of ballistic systems, they are still not ideal. In the first place, the calculations that are needed before the action takes place are in general extremely complex - even throwing rubbish into a bin requires, in effect, the solution of a set of partial differential equations with countless variables - and it is not altogether plausible that the brain could actually have at its disposal a library of such routines so vast as to be able to deal with all the possible motor tasks it might ever encounter during its lifetime. The controller needs to have acquired knowledge about how the plant will behave in response to any kind of command sent to it, and keep this information up to date. So it needs memory as well as intelligence. In addition, parametric feedback only corrects after the event, by which time it may be too late. But there is another approach, much simpler and often better: direct feedback.
Direct feedback: guided control
Here we start as before with a desired result (Fig. 9.4) which we compare at every moment with the actual result. But now the error signal, instead of being used to tweak the parameters, is used directly as the input to the controller. Thus errors immediately generate motor commands, that reduce the difference between the desired and actual result; this is the same kind of negative feedback system that underlies so many of the homeostatic mechanisms of ordinary physiology. Guided missiles are controlled by systems of this type: here the error signal might be something like the angle between the direction in which the missile is pointing and the direction of its target. Whereas a ballistic missile malfunctions disastrously when the wind blows, a guided missile notes the effect of the wind on its relation to the target, and automatically corrects itself. When things go wrong, ballistic systems stay wrong, parametric ones gradually get better, and guided systems readjust immediately.
Fig. 9.4 Direct feedback
In a guided system, instead of
calculating what to do, all you have
to specify is what you want. Like a well-trained servant, the system does
all the rest by itself: such control systems are often called servo systems. Anther advantage is that when errors arise, the computation of
the correcting commands is in general very much simpler than the calculations
needed in a ballistic system. A
familiar example is a domestic central heating system, where the thermostat
acts as the comparator of Fig. 9.4, and generates an error signal which
consists very simply of one of just two possible messages: either that the
actual temperature is above what is wanted, or alternatively that it is below
it – too hot or too cold. The
subsequent computations of the motor commands could hardly be simpler: in the
former case the boiler is switched off, in the latter case it is switched
on. Another, physiological, example
also illustrates this essential simplicity of guided systems. If we instruct a subject to look at a small
light such as A, Fig. 9.6, and then suddenly move it closer to his nose as at
B, we find that his eyes converge smoothly and quite quickly in such a way that
in the end the image of the light still falls exactly on the fovea of each
retina. The velocity of the convergence
movement is high at first, but declines exponentially as the eye gets closer
and closer to its target. This is just
what would be expected of a guided system, in which the eyes are essentially
driven by an error signal. It turns
out that this is indeed what is happening, and disparity between the two
retinal images, sensed by cortical detectors of the kind described in Chapter
7, provides the error signal which generates the convergence. Experiments show that velocity of the eyes
has a very simple relation to the size of this error: it is simply proportional
(fig. 9.5). So as the eyes approach
their goal, this error gets smaller, and the rate of movement correspondingly
declines to zero: hence the time-course of the movement is roughly exponential. Direct feedback is intrinsically a simple
process.
Fig. 9.5 Vergence
The overwhelming advantage of guided systems, however, is not so much their simplicity, but rather the fact that they are almost immune to the effects of noise. For if something unexpected happens that upsets the normal relationship between command and performance, this will be noticed at once (because it will generate error) and the system will instantly generate appropriate commands to achieve the desired result despite the existence of the disturbance. If you leave all the windows in your house open, the thermostat will at once sense the sudden drop in temperature, and the boiler will automatically be switched on until the temperature reaches the desired level once more. Thus the power and elegance of the system is thus that it guarantees to achieve what it has been designed to achieve, even when upset by types of interference that could not have been anticipated by its creator; it is capable of producing results that look intelligent even though - in sharp contrast to the ballistic system with its library of programs for different occasions - it knows very little (only the size of the error) and remembers nothing.
So why are all control systems not of this type? The reason is that it has a weakness. Its proper functioning depends critically on communicating information about the progress of the action rapidly to the comparator. In neural systems, both the sensory receptor processes and the consequent transmission may be rather slow, clearly a serious problem when trying to control fast movements. Delay of this kind 'round the loop' means that instead of responding to the error as it actually is, the system will be responding to the error as it was however many milliseconds ago it takes for the information to find its way back to the brain.
Now the visual system happens to be particularly slow, with reaction times of around 200 milliseconds at best, and this makes visual guidance of actions difficult to achieve. For example, consider a batsman in a game of cricket; one might think that he could use a system like that of Fig. 9.5 to bring his bat up to the ball under visual guidance, using error information about the distance between bat and ball. But is easy to calculate that the existence of this large visual delay makes this physically impossible, because visual information is hopelessly out of date. If the bowler is delivering at 90 mph (40 ms-1) the ball will travel nearly half the length of the pitch in the 200 milliseconds it takes for any visual information about its position to be of use: thus the last useful visual fix on the ball is when it is still 8 metres away. Clearly the bat cannot in any sense be guided on to it.
Internal feedback: efference copy and virtual models
The final type of control system to be considered is at its best in this sort of situation. Though ballistic in the sense that it cannot immediately respond to errors, it has closer affinities with the guided system of Fig. 9.5 than with a simple ballistic one, and uses internal feedback. The notion here is that if it is difficult to obtain feedback about actual results sufficiently quickly for them to be of use during an action, nevertheless it may be possible as the result of experience to predict what the result of a particular motor command is going to be, before the actual result is known. Thus from a general knowledge of the mechanics of one's hand and arm, and information about the kinds of loads that are present, one can form an estimate in advance of what position the limb is going to adopt in response to any particular pattern of motor commands that is sent to it. Since this estimate is formed entirely within the brain, it can be available long before any feedback from the actual movement has found its way back from the periphery.
When things are happening fast, such an estimate - one may call it the predicted result - will at least be better than no information at all. This prediction is derived by sending a copy of the motor commands (an efference copy signal) to a neural model of the mechanical properties of the body, which is used to predict what will happen. So in an internal feedback control system (Fig. 9.6) it is this prediction, rather than the actual result, that is compared with the desired result to produce an estimate of the error. Thus an internal feedback system is a kind of virtual world – in the cricketer's brain, no doubt a virtual cricket pitch. A final, necessary, refinement is that this model must be updated all the time to ensure that it keeps in step with any changes in the actual muscles and bones and the objects in the real world with which we interact. How this is done is that the actual results are continually compared with the predicted results; any errors then represent faults in the model, which are then corrected by parametric feedback. In this way it continually improves the accuracy of its predictions.
Fig. 9.6 Internal feedback
One excellent example of a physiological system that seems to work in this way is the one that controls saccadic eye movements. Saccades are the eye movements we make when we shift the direction of our gaze from one target to another. Large saccades are made with virtually constant velocity - which may be as much as 900 degrees per second - whatever the separation between the targets; thus the duration of a large saccadic movement is a nearly linear function of its size (Fig. 9.7). At first sight, one might think that the eye was simply moving off towards the new target at a constant rate, and that as soon as the visual system senses that the target has been reached, the brakes are applied and the eye comes to rest. But it is easy to calculate - as in the case of the batsman and cricket ball - that this cannot possibly be the true explanation. Because the movement is so extremely rapid, few saccades take more than 100 msec, and most last between 20 and 40; since visual processes in general take considerably longer than this, by the time the brain had recognised that the target had been reached, the eye would have grossly overshot. Thus a simple feedback loop like that of Fig. 9.4 cannot possibly be used. However, there is a feature of the eyes that makes them ideally suited to control by internal feedback. Whilst movement produced by limbs in response to a given commend vary with the load they are experiencing, this is not the case for the eyes, whose load is always constant. This means that the brain can form a very good idea of where the eye is pointing from knowledge of the commands it sends it. Thus the internal model of Fig. 9.6 will work very well, and later we shall see good evidence that efference copy is indeed the means by which we normally sense the position of our eyes; more recent work suggests that the model that uses this information to work out where the eye is pointing is in the superior colliculus. In a saccade, when this calculated eye position is equal to the position requested by the visual system, the drive to the muscles is in effect switched off, and the eye comes to rest on the new target. In the long run, the visual system tells us whether the saccade was in fact successful in landing on the target, and this information is used to tweak the system's parameters.
Fig. 9.7 Saccades
There are several other types of
eye movement apart from saccades. Oddly
enough, between them they exemplify each of the different kinds of motor
control that have been presented here, and they are summarised in the box .
The usefulness of internal models
On the face of it there is perhaps no obvious advantage in using internal feedback rather than ballistic control with parametric feedback. But because the model of the body that is embodied in the former system is essentially a general one, not tied to any particular type of action, it means that experience in carrying out one kind of skilled motor function will benefit the performance of other ones in a rather more direct way than was the case for something like Fig. 9.3. This is particularly true when, as in the case of the eye, the expected result can be computed relatively easily from the motor commands. Learning motor skills then becomes a matter of learning to predict the behaviour of one's own body. Such a system can also cope much better with changes of circumstances, for instance when forced to carry out with one's left hand an action for which you normally use your right. A good analogy is using a map to get around in an unfamiliar town. A sequence of instructions such as 'first right, second left, then right at the garage' is compact but not robust; were one of the streets blocked you would be helpless. A map can record information in such a way that experience gained while carrying out one action can be used to improve other actions: the blocked street can be avoided when making other journeys as well. It also allows rehearsal and planning. A complex manoeuvre – perhaps trying to unlock one's front door while encumbered with groceries – can be tried out in advance within the virtual world before being put into operation.
The hierarchy of control
Evolution by accretion
Another factor that makes both the anatomy and the physiology of motor systems alarmingly complicated is the way in which it has developed in the course of evolution. Whereas the behaviour of the very simplest organisms can be largely described in terms of simple local segmental mechanisms at a peripheral level - as for example the co-ordination of a centipede's legs when it walks - in ascending the evolutionary tree we find more and more domination of the special senses, and as a consequence of this, a corresponding degree of encephalisation: control by higher centres grouped near these sense organs, in the head. It is important to appreciate that by and large this has been a process of accretion. Simpler mechanisms are not in general displaced by more recent ones: they are left essentially intact, but supplemented and controlled from above. They are, after all, carrying out useful functions. Man's walking movements are in essence not so very different from the centipede's, and associated with rather similarly stereotyped sequences of muscle actions mediated by spinal mechanisms of the same general character: such sequences can often be evoked from spinal preparations, animals in which the higher levels of control have been surgically disconnected. It would clearly be foolish for the brain to build its own neural circuits that merely duplicated what the spinal cord was already doing perfectly well, and it is important not to underestimate what the cord is capable of. Classical examples include the spinal dog wagging its tail after defecation, or the wiping reflex in the frog: if a small piece of filter paper is moistened with acid and placed on its back, it will quite accurately use the nearest leg to wipe it off the skin; if that leg is held down, after a short delay another leg is used! It is clear that one should not think of the spinal cord merely as a sort of speaking-tube down which the brain shouts its orders to the muscles: rather, it provides a repertoire of fragments of action, 'party pieces' that can be called on when necessary by the higher levels.
The main difference, in fact, between the spinal cord of a 'higher' and 'lower' animal is that the former in a sense expects to receive more in the way of commands from above; consequently, when isolated from the brain in a spinal preparation, it may appear less responsive. This phenomenon is known as spinal shock: immediately after making the cut that separates cord from brain, spinal reflexes are depressed or absent, because the usual 'permission' from above is not there. But after a period of time the cord becomes more lively, and may in the end actually show a greater degree of responsiveness than before the operation. This period of time depends markedly on the degree of encephalisation: in Man it may take many months; in a dog, days; and in a frog perhaps only a few minutes, reflecting the differing degrees of control normally descending from the brain. In the end, one can never be sure that a spinal animal is really exhibiting all the things that the cord could do if the brain were intact: we always tend to underestimate what the spinal cord is capable of.
The functional characteristics of different hierarchical levels
Experiments of this kind lead naturally to the idea of a hierarchical organisation of the motor system into a series of functional levels (Fig. 9.8), the higher levels having more diverse kinds of sensory information at their disposal and therefore able to plan and anticipate more effectively than the lower.
Fig. 9.8 Levels
Because of their ability to store experience through memory, they can also be more flexible in their responses and learn to conform to the outside world in a way the spinal cord cannot. It follows therefore that as well as being able to stimulate the spinal cord to generate particular patterns of output, these higher levels must also exert a tonic inhibitory influence on lower levels. Brain and cord may well often have conflicting ideas about what is the right thing to do in a particular situation – not flinching from a painful injection, for example - and this conflict needs to be won by the brain. Consequently the effects of lesions in higher levels of the brain are usually two-fold: first a loss of function, particularly of the more flexible and integrated kinds; and secondly the new appearance of abnormal and more primitive modes of response. The latter phenomenon is often described by neurologists as release: the lower centres are released from the restraining influences of the higher, like schoolboys when the teacher is called from their class.
But quite apart from what has happened in evolution, an engineer would recognise that from a purely functional point of view, hierarchies arise inevitably whenever something complicated has to be done, that naturally breaks itself down into relatively repetitive sub-tasks. Thus a well-written computer program will typically consist of simple subroutines that do very small tasks, called by other subroutines which are in turn called by other more global subroutines, and so on. A computer game might have one routine to draw a single dot on the screen, called by another routine that displays a set of dots to form a small pattern, called by another that draws a single object, called by another that displays an entire scene. Clearly at the level at which the programmer is thinking about the general organisation of the game, he does not want to be bothered with the repetitive detail of exactly how each dot in a picture is to be sent to the display.
Such hierarchies are particularly obvious in social organisations that are meant to get things done, most notably in how armies are organised. When Napoleon decided to invade Russia he didn't himself give detailed instructions on how many pairs of boots to buy, or where to dig the latrines: he indicated the overall strategy to be followed – perhaps a little more detailed than Envahissez la Russie – that then percolated down through all the hierarchical layers, getting elaborated as it went, until it eventually reached the soldiers in the firing line. A general has the advantage of integrated information available from a wide variety of sources, which he can use to develop wide-ranging strategies: the men have the advantage of immediate and detailed experience of local conditions, with which they can modify their individual behaviour.
The existence of a hierarchical
organisation carries very important implications for what happens if part of
the system goes wrong. In military
terms, the effects of blowing up a platoon are very different from those of shooting
a general (Fig. 9.9). In the first
case, the defect is obvious, immediate, and limited: very specific jobs no
longer get done: there is a clear correlation between the 'lesion' and the
'symptoms'. In the second case, at
first nothing may appear to be wrong at all: like a headless chicken, the army
still functions. But gradually more
subtle defects may begin to show themselves, such as a lack of long-term
planning or co-ordination. At the same
time, new patterns of activity may start to become apparent as the general's
subordinates begin to put their own ideas into practice without restraint:
symptoms, in other words, of 'release'.
Fig. 9.9 Lesions in hierarchy
These are precisely the kinds of disorders commonly described after damage to different levels of the central nervous system. in polio, for example, the loss of motor neurons causes total paralysis of a particular set of muscles, while others may be unaffected. With lesions at higher levels, we may see loss of some functions and release of others. A classical example is the Babinski sign, or 'up-going big toe'. If the foot of a normal adult is firmly stroked, the immediate response is an involuntary flexion of the foot and toes; but in certain kinds of brain damage, as also in new-born children, the reaction is the exact opposite: the toes curl upwards.·
It is clear in this case that cord and brain have different ideas about what to do when the foot is stroked; in the adult, the brain wins. Another example is the co-ordination of walking movements. A new-born infant is actually able to walk after a fashion (Fig. 9. 10), so long as its weight is supported. But one of the first things that the developing brain does is to suppress this primitive response, many months before it develops its own much more sophisticated patterns of walking, that make better use of integrated sensory information.
Fig. 9.10 Newborn walking
With this notion of hierarchical control in mind, we begin the next chapter by considering what the lowest level of all, the spinal cord, can and cannot do, and the ways in which descending pathways from the brain may control and modify its activity.
References
Rashbass, C. and Westheimer, G. (1961) Disjunctive eye movements. Journal of Physiology 159, 339-360.
Notes
p. Neural networks. A readable account of neural networks is Robert Levine and Diane Drang (1988) Neural networks: the second AI generation. A more technical but comprehensive source is Rojas, R. (1996). Neural Networks: a Systematic Introduction. Berlin: Springer.A stimulating, biologically-oriented but controversial book is G M Edelman (1989) Neural Darwinism: the Theory of Neuronal Group Selection. Oxford: Oxford University Press. Other more recent accounts include Alexander, I., & Martin, H. (1995). An Introduction to Neural Computing. (2nd ed.). (London: Thomson); Callan, R. (1999). The Essence of Neural Networks. (London: Prentice-Hall); Gurney, K. (1997). An Introduction to Neural Networks. (London: UCL Press).
p. Konrad
Lorenz. For examples of
similarly complex but apparently ballistic behaviour, see Lorenz's Studies in Animal and Human Behaviour
(1965; English translation, Robert Martin, 1970) (Cambridge, Mass:
Harvard). This area is intelligently
and succinctly discussed in Marsden, C. D.,
Rothwell, J. C., & Day, B. L. (1984). The use of peripheral feedback in the
control of movement. Trends in
Neuroscience 7 253-257. There is an interesting account of what it
is like to lack motor feedback in Cole,
J. (1991). Pride and a Daily Marathon.
(London: Duckworth).
p. Control
systems. Good books on
specifically biological aspects of control systems that are not too technical
are extremely hard to find. The appendix to R H S Carpenter (1989) Movements of the Eyes (London: Pion) may
be pitched at about the right level. J
H Milsum. (1965). Biological control
systems analysis. (New York: McGraw Hill) and Stark, L. (1968). Neurological Control Systems. (New York:
Plenum) are both excellent but long out of print. A recent account, but expensive, is Khoo, M. C. K. (2000). Physiological
Control Systems: Analysis, Simulation and Estimation. (IEEE Press).
Fairly technical introductory accounts, intended mainly for engineers, are: L. Balmer (1991) Signals and Systems: an Introduction. (New York: Prentice Hall), J J DiStefano, A R Stubberud and J J Williams (1990). Feedback and Control Systems. (New York: McGraw Hill) and Franklin, G. F., Powell, J. D., & Emami-Neini, A. (1994). Feedback Control of Dynamic Systems. (3rd ed.). (Cambridge, Mass: Addison-Wesley).
p. Disparity vergence Two classical papers describing the relation between vergence and disparity: Rashbass, C., & Westheimer, G. (1961). Disjunctive eye movements. Journal of Physiology, 159, 339-360; Westheimer, G., & Mitchell, A. M. (1956). Eye movement responses to convergence stimuli. Archives of Ophthalmology, 55, 848-856.
p. Visual control of batting See for instance Bahill, A. T. and LaRitz, T. (1984) Why can't batters keep their eyes on the ball? American Scientist May-June 249-254, and Lacquaniti, F., Carrozzo, M. and Borghese, N. (1993) The role of vision in tuning anticipatory motor responses of the limbs. In Multisensory Control of Movement, ed. A. Berthoz. (Oxford University Press, Oxford).
p. Internal model for prediction A system of this type is the Smith Predictor, originally developed to control the thickness of the finished product in steel rolling mills: since there was inevitably a certain lag between the steel leaving the rollers and the point where it had cooled enough for the thickness to be meaningful, an ordinary direct feedback system would have lead to unstable oscillations. See Smith, O. J. M. (1959) A controller to overcome dead time. ISA Journal 6, 28-33.
p. Brainstem saccade circuits See for example AF Fuchs, C R Kaneko and C A Scudder (1985) Brainstem control of saccadic eye movements Annual Review of Neuroscience * 307-337, or E L Keller (1992) The Brainstem in RH S Carpenter (ed.) Eye Movements (London: MacMillan).
p. Eye movements General accounts of eye movements include R H S Carpenter (1989) Movements of the Eyes (London: Pion); RH S Carpenter (ed.) (1992) Eye Movements (London: MacMillan); good sources of information on clinical aspects are C Kennard and F C Rose (ed.) Physiological Aspects of Clinical Neuro-Ophthalmology (London: Chapman and Hall), or R J Leigh and D S Zee (1999) The Neurology of Eye Movements (3rd ed.) (Philadelphia: F A Davis). Doucet, P. and Sloep, P. B. (1992) Mathematical Modelling in the Life Sciences. (Ellis Horwood, Chichester) is a more general account of modelling that may also be consulted.
p. Cleverness of the spinal cord The 19th century physiologist Charles Flourens has left a characteristic account of decerebrating a chicken: I removed the two cerebral lobes from a healthy chicken. The animal, thus deprived of its cerebrum, survived ten whole months in a state of perfect health and would in all probability have lived longer if I had not been obliged to leave Paris. I had scarcely removed the brain before the sight of both eyes was suddenly lost; the hearing was also gone and the animal did not give the slightest sign of volition, but kept itself perfectly upright upon its legs, and walked when it was stimulated - or when it was pushed. When thrown into the air, it flew; and swallowed water when it was put into its beak. It seemed entirely to have lost its memory, for when it struck itself against anything, it would not avoid it, but repeat the blow immediately.
p. Armies The analogy is a very old one: in one of his notebooks, Leonardo da Vinci writes: 'The muscles and tendons obey the nerves as soldiers obey their officers; and the nerves obey the brain as the officers obey the general'.
p. The benefits of hierarchies A recent thoughtful discussion, especially in relation to the oculomotor system (where hierarchical organisation is particularly obvious), is Berthoz, A. (2000). The Brain's Sense of Movement. (Cambridge, Mass: Harvard).
p. New-born walking Figure 9.11 was very kindly supplied by Dr N R C Roberton, Department of Paediatrics, Addenbrooke's Hospital, Cambridge. For a dissident view, that the loss of this stepping is due more to changes in body weight in relation to leg strength, see Thelen, E., Fisher, D. M. and Ridley-Johnson, R. (1984) The relationship between physical growth and a newborn reflex. Infant Behavior and Development 7, 479-493.
Neurolab
p. Neural network
Click in the boxes on the left; the input pattern is transformed as it passes from layer to layer, ending up on the right as a count of how many boxes have been checked. This is not a true neural net, in the sense that it learns for itself; it has been programmed to do it. But it shows how a network of simple neural elements (each has a threshold and fires when the number of active inputs exceeds that threshold) can perform quite a complex function.
p. Eye movements
Select a type of eye movement with the buttons at the left, then press sweep. Note that the time- and amplitude scales vary for the different types of movement. Notice that the saccades are stereotyped in form and very fast, but occur with a rather random latency. Their ballistic behaviour is obvious in their response to closely-spaced target movements: compare this with what happens in vergence. In pursuit, the target is tracked by a combination of smooth pursuit (to match the velocity) and saccades (to get the position right). As time goes on, the oculomotor system learns to improve the accuracy of the smooth pursuit, through parametric feedback: consequently, fewer saccades are made.
The demonstration of vestibular, optokinetic and fixation movements are described more fully in Chapter 11, on p. [whatever it is].
p. 11
Control
systems
A demonstration of the different varieties of control system described in the text. Click on the buttons and check-boxes at the left to select the kind of system you want to look at. On the right you can alter some of the characteristics of the control: gain means how sensitive it is, bias is a constant signal added to the output, proportional means that the controller output is simply proportional to the input, proportional + rate means that it is also partly proportional to the rate of change of the input, and proportional + integral means that is partly responsive to accumulated errors. In addition, you can add you own perturbations with the noise slider, and alter the gain of the plant.
Press sweep to see how the actual output y (green) responds to the desired output x (blue), a simple repetitive waveform. See for yourself how with parametric feedback or feedforward, the controller gain and offset adjust themselves automatically to improve performance, and in response to perturbations of the plant, or external noise.
p. Parametric feedback This exhibit shows the functioning of
parametric feedback in the vestibulo-ocular reflex. To understand it, you need to know something about the vestibular
control of eye movements. See the
description in Chapter 11, p. [whatever it is].
People
Pierre Flourens (1794 - 1867) studied medicine at Montpellier, then moved to Paris where he began a series of experiments, in various species, demonstrating the functional effects of removal of various parts of the brain. In this way he was the first to establish the basic function of the medulla, the cerebellum, and the frontal areas.
Josef Babinski (1857 - 1932) was born in Paris, of Polish extraction. He worked with many of the great French neurologists, such as Charcot, particularly on the signs of impairment of the cerebellar and cerebral cortex.
Norbert Wiener (1894 – 1964) was an influential mathematician, teacher and writer, who helped establish the science of control systems, and indeed coined the word ‘cybernetics’ to describe this field of study (a word subsequently fallen into disrepute).
Figure captions
Fig. 9.1 A ballistic control system
Fig. 9.2 A ballistic system with feed-forward, altering the parameters of the controller in response to noise
Fig. 9.3 Parametric feedback: the controller's parameters are modified in response to errors in performance.
Fig. 9.4 A direct feedback system: errors immediately modify the output.
Fig. 9.5 Disparity vergence occurs when a subject looks between two objects at different distances: its time-course (middle) shows a slowing towards the end, as the disparity error approaches zero. Bottom, the vergence velocity is roughly proportional to the disparity error at any moment.
Fig. 9.6 A system using internal feedback; the model of the plant predicts errors, and is adjusted if its predictions are wrong
Fig. 9.7 A saccade is made in looking between two objects at the same distance. Below, the time-course of saccades of different amplitudes, showing the approximately constant velocity of the movements
Fig. 9.8 Schematic representation of hierarchy of levels in the nervous system. Higher levels have more access to information from diverse sources, lower ones have more immediate feedback. On the whole, levels act by controlling those immediately beneath them, rather than by generating movements directly. Upper levels also tend to inhibit lower ones.
Fig. 9.9 Different consequences of lesions at different levels in a hierarchy: localised at lower levels, diffuse at higher, and accompanied by release.
Fig. 9.10 A newborn child walking; this ability will be suppressed by the developing brain, even though adult walking patterns will not appear until a year or so later
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Basic types of
control system
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The existence of parametric or internal feedback obviously makes the interpretation of the results of experimental stimulation or lesions no easy matter. For instance, it is not at all obvious what the effect would be of artificially stimulating the parametric feedback system of Fig. 9.4 at the 'modify' input. Equally, we would anticipate that lesions in such regions as the library of motor programs, or the neural model inside the internal feedback system of Fig. 9.7, would result in complex and subtle effects: not just simple paralysis, but perhaps loss of quality of performance, of the ability to modify responses through experience, and perhaps the appearance of rigidly stereotyped patterns of behaviour not properly adjusted to their objects. Defects of just these kinds are indeed characteristic of many types of clinical derangement of the higher levels of the motor system. The main classes of eye movements (Small moving targets are tracked with a mixture of saccades, to move the eye to the right position, and smooth pursuit, to match the target velocity) Disjunct: |