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Summary Lectures of Neural Basis of Motor Control (NWI-BB080C)
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- Radboud Universiteit Nijmegen (RU)
All lectures of the course Neural Basis of Motor Control, NWI-BB080C
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Voorbeeld 4 van de 39 pagina's
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Neural Basis of Motor ControlLecture 1 – 31 augustus
Motor control for picking up a cup: First you have to locate the cup, this is done via the egocentric
coordination. The next step is to plan the hand movement, this is called endpoint trajectory. Then
there will be the joint trajectory, so determine the intrinsic plan (you will move your joints). At last
there is the execute movement (joint torques), then you make the proper movement.
Picking up a bottle steps:
1. The initial sensing localized the target, determines its coordinates in the egocentric
(body/head centered coordinates). Similarly the position of the hand is sensed using visual
and proprioceptive information
2. Next the trajectory from the hand’s initial position to the final position is planned
3. The hand trajectory is then translated into joint trajectories, which requires a detailed
knowledge of the body mechanics as well as the surrounding space, e.g. obstacles etc. The
process of translating the reaching trajectory of the hand, to the corresponding joint
movements is called inverse kinetics.
4. The execution of the command needs to take into account also the weight of the arm,
friction, required speed, etc. this is referred to as inverse dynamics, which delivers a
temporal sequence of combined muscle forces that translate into torques at the joints/limbs.
Controlling the human body to perform precise movements is a daunting task (and requires a
fearless brain!). There are several concrete challenges that it faces in doing so:
- Maybe most obviously, the human body has very complex mechanics. If you want to write
with a pen, you have to control multiple fingers via a combination of multiple joints of large
limbs to eventually apply sufficient force to the pen to move it by a few mm. Since every joint
has an effect on the subsequent joints, this is a complex task. In addition there is friction and
inertia, the muscles are springy, etc, i.e. a lot of mechanical factors to be taken into account.
- Next, usually we want to perform a particular action in the world, i.e. grab the pen, set it on
the paper. However, there are a myriad of solutions of muscle activation sequences to
achieve this outcome, in particular due to the complexity of the body/skeleton. The brain has
to have rapid mechanisms for choosing a movement involving many muscles, using
additional constraints. This is usually referred to as the complexity of the inverse
model/transform.
- Next, different parts of our body and different senses operate in different coordinate
systems, e.g. information from the eye is in retinal coordinates, while your biceps modifies a
local angle at the elbow (which together with other muscles, then defines coordinates for
reaching points in space). The brain needs to rapidly transform these coordinate systems, in
order to smoothly integrate sensory information with motor actions and vice versa.
- Next, in the real world, speed is often essential, however, faster actions lead to reduced
accuracy, the so called speed-accuracy tradeoff. Hence, the brain has to choose for each
situation the right combination of speed and accuracy for the task.
- Lastly, as you will already know from other courses: brain activity itself ca
There are challenges for the brain to perform precise movements:
- The complexity of the motor system
- Many solutions dilemma
- Coordinate systems and transformations
- Speed-Accuracy tradeoff
- Variability of neural activity
,Speed accuracy trade off:
If you do it really fast then the accuracy will be high, but if the speed is low then the accuracy will be
low.
- Accuracy varies with the speed of movement, not duration
Fitt’s law on speed-accuracy tradeoff → when the W is narrower (the goal region is smaller) this will
make the test harder. And A is with the distance, when you want to do is fast then A will make it
harder when the distance is bigger.
- Log2 (2A/W)= difficulty
o A = bigger values make it harder, movement duration becomes longer
o W= smaller values make it harder, movement duration becomes longer
Consider the simple task of moving from a starting position to the target line. Subjects are instructed
to perform the movement within a particular time (140, 170, or 200 ms).The combination of distance
and movement time leads to a particular speed, see x-axis on the right. As one can see, it is not the
duration of the movement that determines the accuracy, but instead the speed of the movement:
- Slower movements lead to higher accuracy (lower s.d.)
- Faster movements lead to lower accuracy (higher s.d.)
Coordinate systems:
The same point in space in different coordinate systems:
Each system may occur naturally, but they need to be translated in the brain.
For a given movement how to determine the brain’s coordinate system? → just look at the
trajectories.
Hand movement are optimized in Cartesian space, and joints have to adapt accordingly.
Concepts of motor control:
You will need a devise motor plan to go from a desired movement to the actual movement. You will
need sensory information and improve accuracy. Then you will need to simulate the movement (so
see where your hand will going to be) (forward model), align with other motor plans. There is the so
called efference copy, this is a copy of the motor command, this is already represented in your brain.
Then the next level is to simulate the sensory input (forward sensory model), this is to sensitive to
unpredicted changes, this depends to the efference copy.
Feedforward control strategy:
,Fast → needs to know system mechanics
Inflexible → only accurate if nothing unexpected happens
Do normal reaching movements use feedforward or feedback control? It uses feedforward, because
feedback is much slower.
In neural system, both strategies are combined:
- First a large feedforward movement
- Followed by feedback-based corrective movements
The part of the motor control that does not include sensory feedback is referred to as feedforward
control. Feedforward control starts from the desired, future state of the body, and then uses a
feedforward controller to plan the movement. Note, that the feedforward controller - a bit
counterintuitively - contains the inverse model! Inverse here refers to finding the right muscle
activations to achieve the desired state of the joints. This is compatible with feedforward, which
refers to being independent of sensory information gathered during the movement (note that it is of
course based on sensory information before the movement!).The feedforward controller then sends
the motor command to the muscle for execution. This strategy has positive and negative aspects:
- On the positive side, feedforward control is fast, since the motor command is directly
executed and does not have to wait for sensory information to be collected and processed.
- On the negative side, it is rather inflexible, i.e. once executed, the command is not modified
by new information, e.g. imagine you would control your arm just feedforward, and would
not react to an obstacle that is in the way of moving your arm. There are some movements
that are mostly controlled feedforward, e.g. eye movements, where speed is more important
than feedback (and there are rarely obstacles in the way of turning your eye).
Reaching towards a target is performed by detailed planning using the feedforward model. Hence
the executed movement depends on the distance to the target, - with faster and longer movements
made for more distant targets. In the case of a pure feedback system, one would have expected a
sequence of smaller movements, initially independent of distance. After each small movement, a
sensory measurement would indicate the distance to the target, and further movements would be
made to reach the target. As suggested, the feedback strategy would take longer, although it might
be more accurate in the end.
Flexible → can deal with internal and external imperfections
Slower → needs to wait for sensory feedback
If sensory feedback is used during the moment, one speaks about feedback control. In the example
here, e.g. the length of the muscles is measured with muscle spindles, which is then processed and
compared with the intended state of the limbs. This creates a new movement to make (‘residual
error’), which is then translated again by the feedforward controller into a corrective movement. This
, is for example the case between eye-movements: visual feedback is used to perform corrective
saccades, but only between individual saccades. This strategy also has positive and negative aspects:
- On the positive side, feedback control is more flexible, since a movement can be dynamically
corrected in a changing environment.
- On the negative side, it is comparatively slow, since the update of the movement has to wait
for the sensory input to arrive, and an updated motor command to be generated.
Grip force is adjusted feedforward and using feedback.
Efference copy= internal copy of the motor command
Efferent= away from the CNS, i.e. a motor command
If the grip force goes below the load force, you will slip the object.
If you not only delay, but also randomize the timing of the self-tickling, would this be less tickling or
more tickling? → it will be more tickling.
So far we have considered the movements as rather isolated events, however, movements are
usually dynamics sequences, where the endpoint of one, is the starting point of the next. Even single
movements can be considered to be composed of a sequence of motor execution and control events.
The sequence of these events starts with some initial estimate (1, upper image). The blue circle
indicates the internal uncertainty about the location of the hand. The Forward model (here termed
motor/dynamic, since it produces the actual command) produces a motor command, which drives
the muscles, but is also sent as an efference copy to the internal state prediction/simulation (2).The
Forward sensory model uses the efference copy together with knowledge about the state of the
environment to predict the expected sensory feedback. A difference is then computed between the
actual sensory feedback and the predicted sensory feedback. The difference (‘error’) is then
weighted with a Gain factor and used to inform the predicted state about the difference between the
actual and expected relation between hand and environment. This can be used to update the state
estimate (3, purple). This new estimate becomes the starting point for a loop of the same process,
until the error has been reduced to near 0, and hence, the state estimate is in line with the intended
action.
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