The Computational Neurobiology of Reaching and Pointing A Foundation for Motor Learning

by Shadmehr, Reza; Wise, Steven P.

• ISBN13: 9780262195089
• ISBN10: 0262195089
• Format: Hardcover
• Copyright: 2004-10-28
• Publisher: Bradford Books

Summary

Neuroscience involves the study of the nervous system, and its topics range from genetics to inferential reasoning. At its heart, however, lies a search for understanding how the environment affects the nervous system and how the nervous system, in turn, empowers us to interact with and alter our environment. This empowerment requires motor learning. The Computational Neurobiology of Reaching and Pointingaddresses the neural mechanisms of one important form of motor learning. The authors integrate material from the computational, behavioral, and neural sciences of motor control that is not available in any other single source. The result is a unified, comprehensive model of reaching and pointing. The book is intended to be used as a text by graduate students in both neuroscience and bioengineering and as a reference source by experts in neuroscience, robotics, and other disciplines. The book begins with an overview of the evolution, anatomy, and physiology of the motor system, including the mechanisms for generating force and maintaining limb stability. The sections that follow, "Computing Locations and Displacements," "Skills, Adaptations, and Trajectories," and "Predictions, Decisions, and Flexibility," present a theory of sensorially guided reaching and pointing that evolves organically based on computational principles rather than a traditional structure-by-structure approach. The book also includes five appendixes that provide brief refreshers on fundamentals of biology, mathematics, physics, and neurophysiology, as well as a glossary of relevant terms. The authors have also made supplemental materials available on the Internet. These web documents provide source code for simulations, step-by-step derivations of certain mathematical formulations, and expanded explanations of some concepts.

Author Biography

Reza Shadmehr is Professor of Bioengineering and Professor of Neuroscience at the Johns Hopkins University School of Medicine. He is the coauthor of The Computational Neurobiology of Reaching and Pointing and Biological Learning and Control (both published by the MIT Press).

Steven P. Wise is a Research Biologist in the Senior Biomedical Research Service at the National Institute of Mental Health.

Table of Contents

Preface	xv
Introduction	1	(6)
Why Motor Learning?	1	(1)
Why Now?	2	(1)
Why a Theoretical Study?	3	(1)
Why a Computational Theory?	3	(1)
Why Vertebrates, Why Primates, and Why a Two-Joint Arm?	4	(3)
I Evolution, Anatomy, and Physiology	7	(134)
Our Moving History: The Evolution of the Vertebrate CNS	9	(18)
Birth of the Motor System	9	(1)
Components of the Motor System	10	(2)
A Brief History of the Motor System	12	(3)
First Steps: Inventing the Vertebrate Brain	15	(6)
More Recent Steps: Cerebellum and Motor Cortex	21	(2)
Summary	23	(4)
Burdens of History: Control Problems That Reach from the Past	27	(12)
Limbs	28	(4)
Muscles	32	(5)
Nerves	37	(2)
What Motor Learning Is, What Motor Learning Does	39	(22)
Motor Learning Undefined	39	(2)
Motor Learning over Generations: Links to Instincts and Reflexes	41	(5)
Learning New Skills and Maintaining Performance	46	(5)
Making Decisions Adaptively	51	(7)
Summary	58	(3)
What Does the Motor Learning I: Spinal Cord and Brainstem	61	(14)
Spinal Cord	61	(4)
Hindbrain	65	(3)
Cerebellum	68	(3)
Red Nucleus	71	(2)
Superior Colliculus	73	(2)
What Does the Motor Learning II: Forebrain	75	(18)
Basal Ganglia	75	(5)
Thalamus	80	(1)
Cortical Organization I: General Considerations	81	(4)
Cortical Organization II: Cortical Fields for Reaching and Pointing	85	(8)
What Generates Force and Feedback	93	(26)
Biological Versus Mechanical Actuators	93	(1)
Muscle Mechanisms	94	(4)
Motor Units	98	(1)
A Muscle Model	99	(3)
Converting Force to Torque	102	(6)
Muscle Afferents	108	(4)
Muscle Afferents in Action	112	(7)
What Maintains Limb Stability	119	(22)
Equilibrium Points from Antagonist Muscle Activity	120	(1)
Restoring Torques from Length-Tension Properties	121	(2)
Stiffness from Muscle Coactivation	123	(1)
Reaching Without Feedback in Monkeys	123	(3)
Equilibrium Points from Artificial Stimulation	126	(1)
Rapid Movements from Sequential Muscle Activation	127	(2)
Passive Properties Produce Stability	129	(2)
Reflexes Produce Stability	131	(4)
Reaching Without Feedback in Humans	135	(1)
Passive Properties and Reflexes Combined	136	(5)
II Computing Locations and Displacements	141	(130)
Computing End-Effector Location I: Theory	143	(16)
Reaching and Pointing Require Sensory Feedback	143	(1)
Kinematics and Dynamics	144	(1)
Degrees of Freedom and Coordinate Frames	144	(2)
End Effectors and Adaptive Mapping	146	(1)
Predicting the Location of an End Effector in Visual Coordinates	147	(1)
Predicting End-Effector Location with Proprioception: Virtual Robotics	148	(3)
Predicting End-Effector Location with Proprioception: Computations	151	(8)
Computing End-Effector Location II: Experiment	159	(20)
Role of Proprioceptive Signals in End-Effector Localization	159	(3)
Introduction to Frontal and Parietal Neurophysiology	162	(3)
Encoding of Limb Configuration in the CNS	165	(10)
Errors in Reaching due to Lesions of the PPC	175	(4)
Computing Target Location	179	(26)
Computing Target and End-Effector Locations in a Common Frame	180	(3)
Computing Target Location in a Vision-Based Frame	183	(5)
Combining Retinal Location with Eye Orientation Through Gain Fields	188	(17)
Computing Difference Vectors I: Fixation-Centered Coordinates	205	(24)
Planning Reaching and Pointing with Difference Vectors	205	(4)
Shoulder-Centered Versus Fixation-Centered Coordinates	209	(3)
Planning in Fixation-Centered Coordinates: Experiment	212	(4)
Planning in Fixation-Centered Coordinates: Theory	216	(5)
Localizing an End Effector in Fixation-Centered Coordinates	221	(1)
Encoding End-Effector Location in Fixation-Centered Coordinates	222	(3)
Issues Concerning Fixation-Centered Coordinates	225	(4)
Computing Difference Vectors II: Parietal and Frontal Cortex	229	(16)
Computing a Movement Plan	229	(8)
Planning Potential Movements but Not Executing Them	237	(4)
Planning the Next Movement in a Sequence	241	(4)
Planning Displacements and Forces	245	(26)
Representing the Difference Vector in the Motor Areas of the Frontal Lobe	247	(14)
Population Vectors, Force Coding, and Coordinate Frames in M1	261	(10)
III Skills, Adaptation, and Trajectories	271	(106)
Aligning Vision and Proprioception I: Adaptation and Context	273	(22)
Newts Cannot Adapt to Rotation of Their Eyes	275	(1)
Primates Adapt to Rotation of the Visual Field	276	(3)
Prism Adaptation Requires Modification of Both Location and Displacement Maps	279	(1)
Long-term Memories and Learning to Switch on Context	280	(4)
Prism Adaptation in Virtual Robotics	284	(2)
Consequences of Planning in Vision-Based Coordinates	286	(2)
Moving an End Effector Attached to the Hand	288	(1)
Internal Models of Kinematics	289	(2)
Estimate of Limb Location Is Influenced by the Likelihood of the Sensed Variables	291	(4)
Aligning Vision and Proprioception II: Mechanisms and Generalization	295	(24)
Neural Systems Involved in Adapting Alignments Between Proprioception and Vision	295	(8)
Generalization of Adaptation to Altered Visual Feedback	303	(16)
Remapping, Predictive Updating, and Autopilot Control	319	(22)
Remapping Target Location	319	(6)
Predictive Remapping of Target and End-Effector Location with Efference Copy	325	(6)
Remapping End-Effector Location	331	(10)
Planning to Reach or Point I: Smoothness in Visual Coordinates	341	(12)
Regularity in Reaching and Pointing	343	(7)
Description of Trajectory Smoothness: Minimum Jerk	350	(3)
Planning to Reach or Point II: A Next-State Planner	353	(24)
The Problem of Planning	353	(1)
Transforming a Displacement Vector into a Trajectory	354	(3)
The Next-State Planner	357	(7)
Minimizing the Effects of Signal-Dependent Noise	364	(2)
Online Correction of Self-Generated and Imposed Errors in Huntington's Disease	366	(5)
Transforming Plans into Trajectories: The Problem of Redundancy	371	(6)
IV Predictions, Decisions, and Flexibility	377	(148)
Predicting Force I: Internal Models of Dynamics	379	(24)
Internal Models of Dynamics	380	(11)
Correlates of Adapting to Altered Dynamics	391	(12)
Predicting Force II: Representation and Generalization	403	(32)
The Coordinate System of the Internal Model of Dynamics	403	(7)
Computing an Internal Model with a Population Code	410	(6)
Estimating Generalization Functions from Trial-to-Trial Changes in Movement	416	(16)
A Not-So-Invariant Desired Trajectory	432	(3)
Predicting Force III: Consolidating a Motor Skill	435	(12)
Consolidation	435	(6)
A Role for Time and Sleep in Consolidation of Motor Memories	441	(6)
Predicting Inputs and Correcting Errors I: Filtering and Teaching	447	(26)
Cancellation of Predicted Signals by Adaptive Filtering	449	(5)
Predicting and Responding to a Stimulus	454	(8)
Similar Learning Mechanisms in Basal Ganglia and Cerebellum	462	(2)
A Training Signal for the Basal Ganglia	464	(4)
Why Does Huntington's Disease Result in Disorders in Reaching?	468	(5)
Predicting Inputs and Correcting Errors II: Learning from Reflexes	473	(22)
Climbing Fibers Encode a Signal That Represents Motor Error	475	(5)
Predictively Correcting Motor Commands	480	(15)
Deciding Flexibly on Goals, Actions, and Sequences	495	(30)
Deciding on a Target	496	(7)
Choosing Among Multiple Potential Targets of Movement	503	(1)
Deciding on Multiple Movements	504	(1)
Action Selection Based on Estimates of State	505	(8)
Moving to Places Other Than a Stimulus: Standard Mapping vs. Nonstandard Mapping	513	(6)
Summary	519	(6)
V Glossary and Appendixes	525	(24)
Glossary	527	(6)
Appendix A Biology Refresher	533	(4)
Appendix B Anatomy Refresher	537	(2)
Appendix C Mathematics Refresher	539	(4)
Appendix D Physics Refresher	543	(4)
Appendix E Neurophysiology Refresher	547	(2)
Index	549