Neurobiology of motor control : fundamental concepts and new directions

Content: List of Contributors xiii About the Cover xvii 1 Introduction 1Ansgar Buschges and Scott L. Hooper References 5 2 Electrophysiological Recording Techniques 7Scott 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 55Arthur 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 75Andreas 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 107Astrid 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 135Paul 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 178Hans-Joachim Pfluger 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 195Hans-Joachim Pfluger 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 225Ronald 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 263Donald 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 305Jean-Patrick Le Gal, Rejean 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 341Till Bockemuhl 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 365Lena 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 417John 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 443Thomas 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