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Computational
Neurobiology of Reaching and Pointing A Foundation for Motor
Learning Reza Shadmehr and Steven P. Wise MIT Press, 544 pages, 165 illustrations |
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Web resources: muscle models, limb stiffness, kinematics, dynamics, and control policies.
Chapter 1: Introduction
Overview:
Understanding reaching and pointing movements depends on knowledge of physics,
biology, mathematics, robotics, and computer science. Physics plays a
fundamental role because reaching and pointing require your central nervous
system (CNS) to solve difficult mechanical problems: it must learn to control a
limb that consists of linked segments, which interact with each other as well
as with external objects as they accelerate in a gravitational field.
1-1 Why motor learning?
1-2 Why now?
1-3 Why a theoretical study?
1-4 Why a computational
theory?
1-5 Why vertebrates, why
primates, and why a two-joint arm?
Chapter 2: Our Moving History: The Evolution of the
Vertebrate CNS
Overview:
The vertebrate CNS originated approximately 500–550 million years ago, in
surprisingly recognizable form. In large part, what it did for those animals
then, it does for you and other vertebrates today. A major role of the early
vertebrate CNS involved the guidance of swimming based on receptors that
accumulate information from a relatively large distance, mainly those for
vision and olfaction. The original vertebrate motor system later adapted into
the one that controls your reaching and pointing movements.
2-1 Birth of the motor
system
2-2 Components of the motor
system
2-3 A brief history of the
motor system
2-4 First steps: inventing
the vertebrate brain
2-5 More recent steps:
cerebellum and motor cortex
2-6 Summary
Chapter 3: Burdens of History: Control Problems that
Reach from the Past
Overview:
The evolutionary history of the CNS accounts for many of the problems that the
motor system must overcome in order to control reaching and pointing movements.
In learning to control such movements, the CNS must generate force slowly with
spring-like actuators (muscles) that act against a skeleton. It also must
analyze inputs from sensory transducers that provide feedback, but only after a
relatively long delay.
3-1 Limbs
3-2 Muscles
3-3 Nerves
Chapter 4: What Motor Learning Is, What Motor Learning
Does
Overview:
Your evolutionary history has given you a motor system that learns, and motor
learning plays a fundamental role in reaching and pointing movements. Motor
learning takes many forms, including: (1) learning over generations that
becomes encoded in the genome, is epigenetically expressed as instincts and
reflexes, and contributes to learned (conditioned) reflexes; (2) learning new
skills to augment your inherited motor repertoire, and adapting those skills to
maintain performance at a given level; and (3) learning what movements to make
and when to make them. Motor learning allows you and other animals to achieve
appetitive goals and avoid harm. Reaching extends the range of goals available,
and pointing has special importance for communication.
4-1 Motor learning undefined
4-2 Motor learning over
generations: Links to instincts and reflexes
4-3 Learning new skills and
maintaining performance
4-4 Making decisions
adaptively
4-5 Summary
Chapter 5: What Does the Motor Learning I: Spinal Cord
and Brainstem
Overview:
All levels of your CNS contribute to motor learning, including those lowest in
its hierarchy: the spinal cord and brainstem. One highly specialized part of
the brainstem, the cerebellum, plays a particularly important role in learning
to reach and point, among other aspects of motor learning.
5-1 Spinal cord
5-2 Hindbrain
5-3 Cerebellum
5-4 Red nucleus
5-5 Superior colliculus
Chapter 6: What Does the Motor Learning II: Forebrain
Overview:
The forebrain comprises the diencephalon and telencephalon. The basal ganglia
play an important, but enigmatic, role in motor control and learning, including
reaching and pointing movements. The thalamus acts as a key node in recurrent,
distributed modules—often known as “loops”—which integrate the cerebral cortex
into subcortical motor-control systems. Like other advanced mammals, your
cerebral cortex makes up most of your CNS, and your neocortex makes up most of
your brain. Two large parts of it, the motor cortex and the posterior parietal
cortex (PPC), make important contributions to reaching and pointing.
6-1 Basal ganglia
6-2 Thalamus
6-3 Cortical organization I:
General considerations
6-4 Cortical organization
II: Cortical fields for reaching and pointing
Chapter 7: What Generates Force and Feedback
Overview:
Muscles convert chemical energy into force and act like an integrated system of
springs, dampers, and force generators. This chapter describes the relationship
between linear forces, as produced by muscles, and torques generated in a
two-joint arm. Muscle fibers not only generate force, but also give rise to
feedback signals that convey information about forces and muscle lengths to the
CNS.
7-1 Biological versus mechanical actuators
7-2 Muscle mechanisms
7-3 Motor units
7-4 A muscle model
7-5 Converting force to
torque
7-6 Muscle afferents
7-7 Muscle afferents in
action
Chapter 8: What Maintains Limb Stability
Overview:
Pairs of muscles act against each other. This antagonist architecture produces
an equilibrium point—a balance of forces—which helps stabilize the limb. The
passive, spring-like properties of your limb promote its stability, but your
CNS also uses reflexes to stabilize the limb. These mechanisms maintain your
hand at a reach target or in a given direction of pointing.
8-1 Equilibrium points from
antagonist muscle activity
8-2 Restoring torques from
length–tension properties
8-3 Stiffness from
coactivation
8-4 Reaching without
feedback in monkeys
8-5 Equilibrium points from
artificial stimulation
8-6 Rapid movements from sequential
muscles activation
8-7 Passive properties
produce stability
8-8 Reflexes produce
stability
8-9 Reaching without
feedback in people
8-10 Passive properties and
reflexes combined
Part II. Computing Locations and Displacements
Chapter 9: Computing End-Effector Location I: Theory
Overview:
Collectively, your hand and other things controlled by it are end effectors. In
order to control a reaching movement, the CNS computes the difference between
the location of a target and the current location of the end effector. This
chapter considers the problem of computing end-effector location from sensors
that measure muscle lengths or joint angles, a computation called forward
kinematics.
9-1 Reaching and pointing
requires sensory feedback
9-2 Kinematics and dynamics
9-3 Degrees of freedom and
coordinate frames
9-4 End effectors and
adaptive mapping
9-5 Predicting the location
of an end effector in visual coordinates
9-6 Predicting end-effector
location with proprioception: Virtual robotics
9-7 Predicting end-effector
location with proprioception: Computations
Chapter 10: Computing End-Effector Location II:
Experiment
Overview:
The CNS computes an estimate of limb configuration through an alignment of
information from various sensory modalities, including proprioception and
vision. This computation appears to rely on neurons in which discharge varies
monotonically and approximately linearly with location of the end effector in
the workspace.
10-1 Role of proprioceptive signals in end-effector
localization
10-2 Introduction to frontal
and parietal neurophysiology
10-3 Encoding of limb
configuration in the CNS
10-4 Errors in reaching due to
lesions of the PPC
Chapter 11: Computing Target Location
Overview:
In order to control a reaching or pointing movement, your CNS computes the
difference between the location of a target and the current location of the end
effector. In computing target location, your CNS combines information about the
location of the target on the retina with information about eye and head orientation.
Neurons in the PPC encode this information in a multiplicative way.
11-1 Computing target and
end-effector locations in a common frame
11-2 Computing target location
in a vision-based frame
11-3 Combining retinal location
with eye orientation through gain fields
Chapter 12: Computing Difference Vectors I:
Fixation-Centered Coordinates
Overview:
In order to control a reaching or pointing movement your CNS compares an
estimate of end-effector location to an estimate of target location. Neural
networks subtract these estimates to represent a difference vector for the end
effector. The difference vector represents a movement plan for reaching the
target. For reaching and pointing movements in primates, the CNS represents
both targets and end effectors in a visual coordinate frame, with the fovea as
its origin, termed fixation-centered.
12-1 Planning reaching and pointing with difference
vectors
12-2 Shoulder-centered versus
fixation-centered coordinates
12-3 Planning in
fixation-centered coordinates: experiment
12-4 Planning in
fixation-centered coordinates: theory
12-5 Localizing an end-effector
in fixation-centered coordinates
12-6 Encoding end-effector
location in fixation-centered coordinates
12-7 Issues concerning
fixation-centered coordinates
Chapter 13: Computing Difference Vectors II: Parietal
and Frontal Cortex
Overview:
The difference vector represents a high-level plan for movement, which
specifies a displacement of an end effector from its current location to the
target’s location. However, several question remain about the nature of this
plan. Does it correspond to a movement that your CNS will make with the hand,
with the eye, or with some other part of your body? Does it reflect a movement
the CNS might make or definitely will make? And what parts of the CNS play the
most direct role in formulating this plan? This chapter presents some evidence
that areas in the PPC, acting in close concert with the frontal motor areas,
participate in computing the motor plan.
13-1 Computing a movement plan
13-2 Planning potential movements but not executing them