Description
Book SynopsisA multi-disciplinary look at the current state of knowledge regarding motor control and movementfrom molecular biology to robotics
The last two decades have seen a dramatic increase in the number of sophisticated tools and methodologies for exploring motor control and movement. Multi-unit recordings, molecular neurogenetics, computer simulation, and new scientific approaches for studying how muscles and body anatomy transform motor neuron activity into movement have helped revolutionize the field.Neurobiology of Motor Controlbrings together contributions from an interdisciplinary group of experts to provide a review of the current state of knowledge about the initiation and execution of movement, as well as the latest methods and tools for investigating them.
The book ranges from the findings of basic scientists studying model organisms such as mollusks andDrosophila, to biomedical researchers investigating vertebrate motor production to neuroengi
Table of Contents
List of Contributors xiii
About the Cover xvii
1 Introduction 1
Ansgar Büschges and Scott L. Hooper
References 5
2 Electrophysiological Recording Techniques 7
Scott L. Hooper and Joachim Schmidt
2.1 Introduction 7
2.2 Terminology 8
2.3 Intracellular and Patch Clamp Recording 9
2.3.1 Recording Electrodes 9
2.3.2 Current-Clamp:Measuring Transmembrane Potential 12
2.3.3 Voltage Clamp: Measuring Transmembrane Current 15
2.3.3.1 Voltage Clamp with Transmembrane Potential as Reference 15
2.3.3.2 Voltage Clamp with Preparation (Bath) Ground as Reference 16
2.4 Extracellular Recording and Stimulation 17
2.5 A Brief History of Electrophysiological Recording 21
2.6 Concepts Important to Understanding Neuron Recording Techniques 27
2.6.1 Membrane Properties 27
2.6.2 Intracellular Recording 29
2.6.3 Extracellular Recording 32
2.6.3.1 Intracellular Action Potential Shape 33
2.6.3.2 Axon Embedded in Uniform, Infinite Volume Conductor 33
2.6.3.3 Variations in Extracellular Action Potential Shape Induced by Non-Uniform, Non-Infinite Volume Conductors 42
2.6.3.4 Bipolar Recording 44
2.6.3.5 Extracellular Action Potential Summary 46
Acknowledgements 47
References 47
3 Multi-Unit Recording 55
Arthur Leblois and Christophe Pouzat
3.1 Introduction 55
3.2 Chapter Organization and Expository Choices 56
3.3 Hardware 57
3.4 Spike Sorting Methods 60
Endnotes 69
References 70
4 The “New Math” of Neuroscience: Genetic Tools for Accessing and Electively Manipulating Neurons 75
Andreas Schoofs,Michael J. Pankratz, and Martyn Goulding
4.1 Introduction 75
4.2 Restricting Gene Expression to Specific Neurons 76
4.2.1 Promoter Bashing, Enhancer Trapping: Binary Systems for Targeted Gene Expression 77
4.2.2 Intersectional Strategies 81
4.2.3 Temporally Inducible Systems 82
4.3 Tracing, Manipulating, and Monitoring Neurons 84
4.3.1 Tracing Neuronal Projections and Connections with Fluorescent Reporters 84
4.3.2 Viral Tracers for Mapping Neural Connections 85
4.3.3 Manipulating Neuronal Function 87
4.3.4 Monitoring Neuronal Activity 90
4.4 Case Studies 92
4.5 Future Perspective 98
References 98
5 Computer Simulation—Power and Peril 107
Astrid A. Prinz and Scott L. Hooper
5.1 Introduction 107
5.2 Why Model? 107
5.3 Modeling Approaches 110
5.4 Model Optimization and Validation 118
5.5 Beyond Purely ComputationalModels 120
5.6 Fundamental Concepts and Frequently Used Models in Motor Control 121
5.6.1 How to Predict the Future 121
5.6.2 Neuron Models 123
5.6.3 Synapse Models 127
5.6.4 Muscle Models 128
5.6.5 Biomechanical Models 128
5.7 The Future 129
Acknowledgements 130
References 130
6 Evolution of Motor Systems 135
Paul S. Katz and Melina E. Hale
6.1 Introduction 135
6.2 Phylogenetics 136
6.3 Homology and Homoplasy 138
6.4 Levels of Biological Organization 139
6.5 Homologous Neurons 139
6.6 Deep Homology 142
6.7 Homoplasy 145
6.8 Convergence in Central Pattern Generators 150
6.9 Evolutionary Loss 152
6.10 Evolution of Novel Motor Behaviors 152
6.11 Three Scenarios for the Evolution of Novel Behavior 154
6.11.1 Generalist Neural Circuitry 154
6.11.2 Rewired Circuitry 157
6.11.3 Functional Rewiring with Neuromodulation 159
6.12 Motor System Evolvability 161
6.13 Neuron Duplication and Parcellation 162
6.14 Divergence of Neural Circuitry 164
6.15 Summary and Conclusions 165
Acknowledgements 165
References 165
7 Motor Pattern Selection 177
7.1 Introduction to Motor Pattern Selection in Vertebrates and Invertebrates 178
Hans-Joachim Pflüger and Sten Grillner
References 179
7.2 Selection of Action—A Vertebrate Perspective 181
Sten Grillner and Brita Robertson
7.2.1 Introduction 181
7.2.2 Control of Locomotory Outputs 182
7.2.3 The Organization and Role of the Basal Ganglia 184
7.2.4 ConceptualModel of the Organization Underlying Selection of Behavior 187
7.2.5 The Organization of Motor Control From Cortex (Pallium in Lower Vertebrates) 189
7.2.6 The Relative Role of Different Forebrain Structures for Selection of Behavior 189
Acknowledgements 190
References 191
7.3 Motor Pattern Selection and Initiation in Invertebrates with an Emphasis on Insects 195
Hans-Joachim Pflüger
7.3.1 Introduction 195
7.3.2 Organization Principles of Relevant Sensory Systems 196
7.3.3 Movement-Generating Neural Networks in Invertebrates 196
7.3.4 Motor Pattern Selection in Invertebrates 197
7.3.4.1 Probabilistic “Selection”: Intrinsically Variable CPGs in Mollusk Feeding 197
7.3.4.2 Selection via CPG Coordination 198
7.3.4.3 Selection by Neuromodulators or Neurohormones 198
7.3.4.4 Selection by Command Neurons Not in the Brain 201
7.3.4.5 The Brain is Crucial in the Motor Selection Process 202
7.3.5 Two Case Studies 207
7.3.6 Concluding Remarks on Invertebrates 213
7.3.7 Are There Common Themes between Motor Pattern Selection in
Invertebrates and Vertebrates? 213
References 216
8 Neural Networks for the Generation of RhythmicMotor Behaviors 225
Ronald M. Harris-Warrick and Jan-Marino Ramirez
8.1 Introduction 225
8.2 Concept of the Central Pattern Generator 225
8.3 Overall Organization of Rhythmic Motor Networks 227
8.4 Identification of CPG Neurons and Synapses: The “Wiring Diagram” 234
8.5 Cellular PropertiesThat Shape Network Output: Building Blocks for Network Operation 238
8.6 Combined Neural Mechanisms for Rhythmogenesis 240
8.7 Ionic Currents Shaping CPG Network Neuron Intrinsic Firing Properties 241
8.7.1 Role of Outward Currents in Regulating Pacemaker and Network Activity 241
8.7.2 Role of Inward Currents in the Generation of Pacemaker and Network Activity 243
8.7.3 Interaction of Inward and Outward Currents in the Generation of Pacemaker Activity 245
8.7.4 Homeostatic Plasticity and the Balance between Different Ion Channel Types 245
8.7.5 Rapid Changes in Extracellular Ion Concentrations during Rhythmic Network Function 246
8.8 Role of Network Synaptic Properties in Organizing Rhythmic Behaviors 246
8.9 Variable Output from Motor Networks 249
8.10 Conclusions 252
Acknowledgements 253
References 253
9 Sensory Feedback in the Control of Posture and Locomotion 263
Donald H. Edwards and Boris I. Prilutsky
9.1 Introduction 263
9.2 History and Background of Feedback Control 264
9.3 Classical Control Theory 264
9.4 Nervous System Implementation in the Control of Posture and Limb Movements 267
9.5 Organization and Function in Arthropods 274
9.5.1 Locomotory System Gross Anatomy 274
9.5.2 Proprioceptors and Exteroceptors 274
9.5.3 Arthropod Nervous Systems 275
9.5.4 Postures and Movement Commands 275
9.5.5 Sensory Feedback in the Maintenance of Posture 275
9.5.6 Sensory Feedback in Movement andWalking 276
9.6 Organization and Function in Vertebrates 282
9.6.1 Sensory Feedback in the Maintenance of Posture 282
9.6.2 Sensory Feedback and its Integration with Motor Commands in
Movement 285
9.7 Conclusions 293
Acknowledgements 294
Endnote 294
References 294
10 Coordination of Rhythmic Movements 305
Jean-Patrick Le Gal, Réjean Dubuc, and Carmen Smarandache-Wellmann
10.1 Introduction 305
10.2 Overview of Invertebrate CPGs 306
10.2.1 Stomatogastric Nervous System: Feeding Circuits in Decapod Crustacea 308
10.2.2 Leech Locomotion 315
10.2.3 Crayfish Swimmeret System 317
10.2.4 Insect Locomotion 319
10.2.5 MultipleMechanisms Mediate Coordination in Invertebrate Systems 321
10.3 Overview of Vertebrate CPGs 321
10.3.1 General Characteristic of Vertebrate CPGs 322
10.3.1.1 Locomotor CPGs 322
10.3.1.2 Respiratory CPGs 323
10.3.1.3 Feeding CPGs 324
10.3.2 CPG Interactions within One Motor Function 324
10.3.2.1 Unit Generators in Limbless Swimming Vertebrates 324
10.3.2.2 Unit Generators in Mammalian Limbs 325
10.3.3 CPGs Interactions for Different Motor Functions 327
10.3.3.1 Coordination of Respiration and Swallowing 327
10.3.3.2 Coordination of Locomotion and Respiration 328
10.4 Conclusion 331
References 332
11 Prehensile Movements 341
Till Bockemühl
11.1 Introduction: Prehension as Goal-Directed Behavior 341
11.2 The Redundancy Problem in Motor Control 343
11.3 Redundancy Occurs on Multiple Levels of the Motor System 346
11.4 Overcoming the Redundancy Problem 349
11.4.1 InvariantMovement Features 350
11.4.2 Increasing the Number of Task Conditions 352
11.4.3 Reducing the Number of DOFs 357
References 361
12 Muscle, Biomechanics, and Implications for Neural Control 365
Lena H. Ting and Hillel J. Chiel
12.1 Introduction 365
12.2 Behavioral Context Determines How Motorneuron Activity Is Transformed into Muscle Force and Power 366
12.2.1 The Neuromuscular Transform Is History-Dependent 367
12.2.1.1 Motorneurons Are Subject to Neuromodulation and History-Dependence That Can Significantly Alter Their Output 368
12.2.1.2 Presynaptic Neurotransmitter Release at the Neuromuscular Junction Is History-Dependent 368
12.2.1.3 Post-SynapticMuscle Excitation Is History-Dependent and Subject to Modulation 368
12.2.1.4 Contractile Dynamics of Cross-Bridge Interactions Are History Dependent 369
12.2.1.5 The Molecular Motors of Muscles Give Rise to the Functional and History-Dependent Properties of Muscle Force Generation 369
12.2.2 Muscle Power Depends on Behavioral Context 371
12.2.3 Muscle Specialization Reflects Behavioral Repertoire 373
12.3 Organismal Structures Transform Muscle Force into Behavior 374
12.3.1 Effects of Muscle Force Depend on the Properties of the Body and the Environment 375
12.3.1.1 The Relative Importance of Inertial, Viscous, and Spring-Like Forces Affect the Role of Muscle Force 375
12.3.1.2 Muscle Function Depends on Behavioral Context and Environmental Forces 377
12.3.1.3 Biomechanical Affordances and Constraints of Body Structures Affect Muscle Functions 377
12.3.2 Muscles Are Multi-Functional 381
12.3.3 Specialization of Biomechanical Structures Reflect Behavioral Repertoire 385
12.4 Biomechanics Defines Meaningful Patterns of Neural Activity 387
12.4.1 Organismal Structures Are Multi-Functional 389
12.4.2 Many Functionally-Equivalent Solutions Exist for Sensorimotor Tasks 392
12.4.3 Structure and Variability in Motor Patterns Reflect Biomechanics 394
12.4.4 Specialization of Neuromechanical Systems Reflect Behavioral Repertoire 399
12.5 Conclusions 401
Acknowledgements 402
References 402
13 Plasticity and Learning in Motor Control Networks 417
John Simmers and Keith T. Sillar
13.1 Introduction 417
13.2 Homeostatic Motor Network Assembly 418
13.3 Short-Term Motor Learning Conferred by Sodium Pumps 420
13.3.1 Swimming CPG Network Plasticity in Xenopus Frog Tadpoles 420
13.3.2 Comparative Aspects of Na+ Pump Contribution to Neural Network Function 425
13.4 CPG Network Plasticity and Motor Learning Conferred by Operant Conditioning 426
13.5 Discussion and Conclusions 432
References 436
14 Bio-inspired Robot Locomotion 443
Thomas Buschmann and Barry Trimmer
14.1 Introduction 443
14.2 Mechanical Engineering Background and a Biological Example 444
14.3 Legged Robots with Skeletal Structures 446
14.3.1 Mechanism Design, Sensing, and Actuation 446
14.3.2 Basic Dynamics of Legged Locomotion 447
14.3.3 Trajectory-OrientedWalking Control 448
14.3.4 Limit CycleWalkers 450
14.3.5 CPG-Based Control and Step-Phase Control 451
14.4 Soft Robots 452
14.4.1 Limitations and Advantages of Soft Materials 452
14.4.2 The Challenges 453
14.4.2.1 Actuators 453
14.4.2.2 Sensors 455
14.4.2.3 Control of Soft Robots 456
14.4.3 Bioinspired Locomotion in Soft Robots 459
14.5 Conclusion and Outlook 463
References 463
Index 473
