Neural Basis of Motor Control (BB080C) Summary of all lectures
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Course
Neural Basis Of Motor Control (BB080C)
Institution
Radboud Universiteit Nijmegen (RU)
Summary of all lectures of the course Neural Basis of Motor Control (BB080C) given in the 3rd year of the bachelor biology. Great for people who struggle with big chunks of text. Lots of pictures and bullet points
Lecture 1 Challenges
Motor Control for Picking up a
Cup
The hand trajectory is translated into
joint trajectories, which requires a
detailed knowledge of the body
mechanics as well as the surrounding
space. The process of translating the
reaching trajectory of the hand, to the
corresponding joint movements (‘joint
trajectories’) is called ‘inverse kinetics’.
Challenges for the Brain to
perform Precise Movements →
Speed Accuracy Tradeoff
Accuracy varies with the speed of
movement, not duration.
When task difficulty increases, motor
control takes longer to maintain accuracy.
(Fitt’s Law on Speed-Accuracy Tradeoff)
1
,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?
(Hand) movement are optimized in Cartesian space, and joints have to adapt accordingly
Reaching Errors are Uncorrelated, meaning that both direction and length of the motor
movement are independently controlled. If they were controlled together, one would expect
that an error in one, would lead to an error in the other, leading to correlation.
Concepts of
Motor Control
2
,Feedforward Control Strategy
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.
Fast but inflexible
Do normal reaching movements use feedforward or feedback control?
In neural systems, both strategies are combined:
● first a large feedforward movement
● followed by feedback-based corrective movements
Feedback Control Strategy
Sensory feedback is used
during the moment.
Flexible but slow.
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.
Grip Force is adjusted feedforward and using
Feedback
Feedforward and feedback information are combined in
different phases of a task.
Feedforward to start in lifting it with an expected weight.
If/when the weight differs and adjustment is made to be
able to lift the object.
3
, Using the Efference Copy to simulate the Movement
Another very powerful concept of
motor control is to not only send
the motor command to the
muscles, but make it available to
the brain (potentially in a modified
format). It can be considered an
internal simulation/prediction of the
motor command, which is referred
to as the forward model. It can be
useful in multiple ways.
Since it makes a prediction of what
should happen, comparing the
predicted movement to the
actually sensed movements allows
to make a comparison between
the intended and the actual movement, which is the basis for making a corrective movement.
This requires sensory feedback, but is not identical to it.
Prediction of Own Movement Accelerates Motor Control
● If the movement is external, then the grip
force is much bigger than the load force and
lags it, due to the internal delays in the
motor system. Note, how large it is,
maintaining a safety margin in order to
maintain the grip.
● If the movement is created internally, then
the grip force is not delayed and matched
rather precisely in size. This is possible,
because the grip force can be co-regulated
with the load force, which is controlled by
(and via the efference copy, represented in)
the neural activity of the motor system.
Integration of Prediction and Feedback
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.
4
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