Neural Engineering

Name: Neural Engineering
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Inhalt

Intro
Neural Engineering
Preface
Contents
Chapter 1: Introduction to Neurophysiology
1 Overview of Neurons, Synapses, Neuronal Circuits, and Central Nervous System Anatomy
1.1 Temporal and Spatial Facilitation
1.2 Special Neural Circuits
1.3 Reflexes
1.4 Reflex Time
2 Sensory Systems
2.1 Properties of a Particular Stimulus
2.2 Functional Organization of a Receptor
2.3 The Relative Distributions of Receptors within the Human Body
2.4 Sensory Input into Motor Systems
3 Somatovisceral Sensibility
3.1 Processing in the Central Nervous System
3.2 Basic Anatomy of the Somatosensory System
3.2.1 Specific Pathways
3.2.2 Nonspecific Pathways
3.3 Somatosensory Projection Areas in the Cortex
3.4 Mechanoreception
4 General Anatomic and Functional Features of the Motor System
4.1 Motor Control Hierarchy for Voluntary Movements
4.2 Spinal Cord
4.3 Brainstem Components
4.4 Cerebellum
4.5 Motor Cortex
4.6 Efferent Connections from the Motor Cortex
4.7 Basal Ganglia and Thalamus
5 Maintenance of Upright Posture and Sense of Equilibrium
5.1 Sense of Equilibrium
5.1.1 Macula Organs
5.1.2 Semicircular Canals
5.1.3 Central Vestibular System
5.1.4 Vestibular Reflexes
6 Complex Integrative Functions of the Motor System
6.1 The Complex Motor Function of Speech
6.2 Motoneuron Recruitment
7 Pathophysiology of the Motor System
7.1 Disorders of the Spinal Cord
7.2 Disruption of Functions Within the Brainstem
7.3 Disturbances Within the Cerebellum
7.4 Disorders Within the Basal Ganglia
7.5 Impairment Within the Motor Cortex
8 The Autonomic Nervous System
8.1 Sympathetic
8.2 Parasympathetic
8.3 Neurotransmitters in the ANS
8.4 The Adrenal Medulla
8.5 Central Organization of the ANS
9 The Hypothalamus and Homeostasis
10 Regulation of Body Temperature: Thermoregulation
10.1 Core Temperature
10.2 Cutaneous Thermoreception
10.3 Central Thermoregulation
11 The Limbic System and the Ascending Reticular Activating System
11.1 Function of the Various Portions of the Reticular Activating System
11.2 Brain Waves
11.3 Sleep
11.4 Mechanisms of Sleep
12 Pain
12.1 Intensity of Pain (Quantity)
13 Vision
13.1 Functional Anatomy
13.2 The Visual Focusing System
13.3 Visual Receptor Cells
13.4 The Receptor Transduction Process
13.5 Eye Movements
14 Sound and Hearing
14.1 Functional Anatomy
14.2 Auditory Sensations
14.3 The Central Auditory System
15 Taste and Smell
Additional Reading
Chapter 2: Brain-Computer Interfaces
1 Introduction
2 BCI Definition and Structure
2.1 What is a BCI?
2.2 Alternative or Related Terms
2.3 The Components of a BCI
2.4 The Unique Challenge of BCI Research and Development
2.5 BCI Operation Depends on the Interaction of Two Adaptive Controllers
2.6 Choosing Signals and Brain Areas for BCIs
3 Signal Acquisition
3.1 Invasive Techniques
3.1.1 Intracortical
3.1.2 Cortical surface
3.2 Noninvasive Techniques
3.2.1 EEG
3.2.2 MEG
3.2.3 fMRI
3.2.4 NIRS
3.3 Neural Signals Used by BCIs
3.3.1 Sensorimotor Rhythms
3.3.2 Slow cortical potentials
3.3.3 The P300 event-related potential
3.3.4 Event-related potentials
3.3.5 Spikes and local field potentials
4 Signal Processing
4.1 Feature Extraction
4.1.1 Artifact/noise removal and signal enhancement
4.1.2 Feature Extraction Methods
4.1.3 Feature Selection and Dimensionality Reduction
4.2 Feature Translation
4.2.1 Continuous feature translation
4.2.2 Discrete feature translation
5 Major BCI Applications
5.1 Replacing Lost Communication
5.2 Replacing Lost Motor Function and Promoting Neuroplasticity to Improve Defective Function
5.3 Supplementing Normal Function
6 Examples of EEG-Based BCI Systems
6.1 General-Purpose Software Platform for BCI Research
6.2 BCIs Based on Sensorimotor Rhythms
6.3 BCIs Based on P300
6.4 BCIs Based on Visual Evoked Potentials
6.5 BCIs Based on Auditory Evoked Potentials
6.6 Attention-Based BCI
7 BCI Performance Assessment
7.1 User Performance Assessment
7.2 System Performance Assessment
7.3 BCI Training
7.3.1 Cognitive tasks
7.3.2 Operant conditioning
7.3.3 Factors that affect training
8 Future Expectations and Critical Needs
8.1 Expectations
8.2 Signal Acquisition
8.3 Clinical Validation and Dissemination
8.4 Reliability
9 Conclusion
References
Chapter 3: Neurorobotics: Opening Novel Lines of Communication Between Populations of Single Neurons and External Devices
1 Introduction
2 Directly Interfacing with Populations of Single Neurons
2.1 Representation of Information in the Brain by Populations of Neurons
2.2 Coding Strategies of Ensembles of Single Neurons
2.3 Decoding the Neural Signal
3 Neurorobotic Control
3.1 Feasibility of Neurorobotic Control
3.2 Development of Neural Population Functions and Implementation in Real Time
3.2.1 Early Approaches to Decoding Movement Trajectories
3.2.2 Linear Vs. Nonlinear Approaches
3.2.3 Closing the Loop
3.3 Neurorobotic Control After Injury
3.3.1 Role of Plasticity
3.3.2 Implications for Spinal Cord Injury
3.3.3 Closed Loop Devices for Stimulating
3.3.4 Neurorobotic Control as a Therapeutic Device
4 Hardware Requirements for Neurorobotic Control
4.1 Biophysics of the Neuron-Electrode Interface
4.2 Signal Conditioning
4.3 Discriminating Action Potentials
4.4 Packaging and Telemetry
5 New Directions and Challenges for a Neurorobotic Control
References
Chapter 4: Decoding Algorithms for Brain-Machine Interfaces
1 The Role and Setting of Motor Brain-Machine Interfaces
1.1 Brain-Machine Interfaces: A Case of Neural Decoding
1.2 Developing and Deploying BMI Decoding Algorithms
2 A Review of Decoding Approaches
3 Neural Signals for Motor Brain-Machine Interfaces
4 Input-Output Modeling: Motor Decoding from Neural Signals
4.1 System Architecture, Performance Criteria, and Adaptation
4.2 Achieving Generalization Without Overfitting
4.3 Experimental Dataset for Method Comparisons: Natural Reaching Task
5 Applying Linear Modeling to BMIs
5.1 Wiener Filter for BMIs: Multiple-input and Multiple-output
5.2 Least Mean Squares
6 Nonlinear Discriminative Models
6.1 Artificial Neural Network
7 Generative Models
7.1 Population Vector Algorithm-Linear Gaussian Generative Models
7.2 Maximum Likelihood Point Process
7.3 Bayesian Models
7.4 Closed-Loop Comparison of Generative Models
8 Conclusion
Appendix A. Review of Closed-Loop Experiments
References
Chapter 5: EEG Signal Processing: Theory and Applications
1 Introduction: EEG Generalities
1.1 Traditional EEG Bands
1.2 Paroxysmal Discharges and EEG Shapes
1.3 Survey of EEG Applications
2 Time Domain Representation and Methods
2.1 The Teager-Kaiser Energy Algorithm: Theory
3 Frequency Domain Methods
3.1 Nonparametric Spectral Methods
3.2 Parametric (Modeling) Methods
3.2.1 Diagnostic Power of the Autoregressive Method Is Used as a Dominant Frequency Method to Calculate Normalized Separation
3.3 Parametric Methods of Signal Processing: The MUSIC Algorithm
3.4 Wavelets
3.4.1 The Wavelet Transform: Variable Time and Frequency Resolution. The Continuous Wavelet Transform
3.4.2 The Discrete Wavelet Transform
3.4.3 Application of Wavelets and Entropy: The Definition of IQ-Information Quantity
4 An Application of EEG: Detecting Brain Injury After Cardiac Arrest
4.1 Experimental Methods for Hypoxic-Asphyxic Cardiac Arrest: The Use of Normalized Separation
4.2 Detecting and Counting Bursts
4.3 EEG and Entropy: A Novel Approach to Brain Injury Monitoring
5 Clinical Applications: Adult Hypoxia-Asphyxia Due to Cardiac Arrest
6 Conclusion
References
Chapter 6: Neural Modeling
1 Why Build Neural Models?
2 Basic Properties of Excitable Membranes
2.1 Membrane Properties
2.2 Equivalent Circuit Representation
2.2.1 Membrane Capacitance
2.2.2 Membrane Conductance
2.2.3 Normalized Units for the Passive Membrane
2.2.4 Passive Membrane Representation
3 Excitability
3.1 Electric Potentials
3.2 Resting Potential
3.3 Voltage-Gated Conductances
3.4 The Hodgkin-Huxley Model: Action Potentials in the Squid Giant Axon
3.4.1 Voltage Clamp and Space Clamp
3.4.2 Ionic Conductances
3.4.3 Model of the Potassium and Sodium Conductance
3.4.4 Potassium and Sodium Currents
3.4.5 Complete Hodgkin-Huxley Model
3.4.6 Normalized Units in the Hodgkin-Huxley Model
3.5 Behavior of the Hodgkin-Huxley Model
3.5.1 Action Potentials and Threshold
3.5.2 Refractory Period
3.6 Assumptions of the Model
4 Propagating Activity
5 Diversity in Channels and Electrical Activity
5.1 Bursting
5.2 Subthreshold Oscillations
5.3 After-Hyperpolarizations and After-Depolarizations
5.4 Spike-Frequency Adaptation
5.5 Bistability
5.6 Post-inhibitory Rebound Spiking
6 Nonlinear Dendritic Processing
6.1 Dendritic Channel Expression
6.2 Dendritic Excitability
7 Simple-Neural Models
7.1 Integrate-and-Fire Model
7.2 Behavior of the Leaky Integrate-and-Fire Model
7.3 Modified Integrate-and-Fire Models
7.3.1 Resonate-and-Fire Models
7.3.2 Quadratic Integrate-and-Fire Models
7.3.3 Complexity in Simple Models
8 Similar Phenotypes Arising from Disparate Mechanisms
9 Synapse Models
10 Short-Term Synaptic Plasticity
11 Beyond Single Neurons
11.1 Feed-Forward Networks
11.2 Persistent Activity
12 Neural Modeling in Medicine
13 Modeling Resources
References
Chapter 7: Neural Modelling: Neural Information Processing and Selected Applications
1 Introduction
2 Neural Information Processing: From Neurons to Neural Ensembles and the Cerebral Cortex
2.1 Neural Information Processing By a Single Neuron
2.2 Neural Information Processing By a Neural Ensemble
2.3 Neural Information Processing By the Cerebral Cortex
2.3.1 Plasticity
2.3.2 Emergence
2.3.3 Small-World Architecture
2.3.4 Modularity
3 Multi-scale Modelling of the Neural System
3.1 Point Process Model
3.2 Haken-Kelso-Bunz Model
3.3 Framework of a Multi-scale Dynamical Model
3.4 Summary
4 Models of the Auditory Periphery
4.1 Biophysics of the Auditory Periphery
4.2 Rate-Intensity Function
4.3 Model Description
4.4 Adaptability and Impairments
4.5 Other Considerations
4.6 Summary
5 Models of Visual Attention
5.1 Bottom-Up and Top-Down Approaches for Sensory Information Processing
5.2 Model Description: Feed-forward, Feedback, and Local Connectivity
5.3 Modes of Operation, Simulation Results, and Future Directions
5.4 Summary
6 Models of Autonomic Nervous Control for Blood Pressure Homeostasis
6.1 Neural Control on the Cardiovascular System to Maintain Homeostasis
6.2 Model Description: Linear and Non-linear Models of the Baroreflex
6.2.1 Models of the afferent baroreceptor activity
6.2.2 Models of the Autonomous Nervous Activity
6.2.3 Models of the Efferent Nerve Activity
6.3 Summary
7 Conclusions
References
Chapter 8: Bidomain Modeling of Neural Tissue
1 Introduction
2 Background
3 Single Neuron Models
3.1 Hodgkin-Huxley Model
3.2 Other Neural Models
4 Cable Formulation
5 Compartmental Neural Models
6 Bidomain Models
6.1 Application in Neural Tissue
6.2 Bidomain Derivation
6.3 Boundary Conditions
6.4 Influence of Tissue Properties
6.5 Simulating Electrical Propagation in a Nerve Axon
References
Chapter 9: Transcranial Magnetic Stimulation
1 Introduction/Overview
2 Physics of TMS
2.1 Induced Magnetic Field
2.2 Quasi-Static Assumptions
2.3 Boundary Conditions and Effects
2.4 MEG Reciprocity
3 Design Considerations
3.1 Stimulator Circuitry and Design
3.2 TMS Coil Design
3.3 Other Factors to Consider: Mechanical Forces, Overheating, and Safety
4 Neuronal response to TMS
4.1 Factors Involved in Neuronal Activation
4.2 In Vitro Studies
4.3 Long-Term Effects Observed with TMS
5 Clinical and Research Applications of TMS
5.1 Uses of TMS in Therapy
5.2 Uses for the Treatment of Psychiatric Disorders
5.3 Movement Disabilities and Neurorehabilitation
5.4 The Use of TMS in Cognitive Studies
5.5 Use of TMS for Understanding Mechanisms of Neural Interaction
5.6 Other Uses
6 Conclusions
References
Chapter 10: Managing Neurological Disorders Using Neuromodulation
1 The Burden of Neurological Disorders
2 Traditional Treatment Approaches
3 The Field of Neuromodulation
4 Neural Circuitry and Neural Networks in Neurological Disorders
5 Electrical Neuromodulation
6 Deep Brain Stimulation-Principles and Procedure
7 Brain and Spinal Cord Electrical Neuromodulation for Different Neurological Disorders
7.1 Deep Brain Stimulation for Movement Disorders
7.2 Deep Brain Stimulation for Neurobehavioral Disorders
8 Electrical Neuromodulation for Epilepsy
9 Electrical Neuromodulation for Pain
10 Chemical Neuromodulation
11 Biological Neuromodulation
12 Conclusion
References
Chapter 11: Functional Magnetic Resonance Imaging
1 Principles of MRI
2 Principles of Functional MRI
3 fMRI Experiment Design
4 fMRI Data Analysis
4.1 Preprocessing
4.1.1 Motion Correction
4.1.2 Slice Timing Correction
4.1.3 Functional-Structural Co-registration
4.1.4 Normalization
4.1.5 Temporal Filtering
4.1.6 Spatial Filtering
4.2 Statistical Tests
4.2.1 t-Test
4.2.2 Correlation Analysis
4.2.3 Regression Analysis: The General Linear Model
5 Biophysical Modeling of the fMRI Signal
6 Spatial and Temporal Resolutions
7 Signal and Noise Considerations
8 Combining fMRI and EEG for Human Brain Mapping
9 Summary
References
Chapter 12: Electrophysiological Mapping and Neuroimaging
1 Introduction
1.1 Generation and Measurement of EEG and MEG
1.2 Spatial and Temporal Resolution of EEG and MEG
2 Electrophysiological Mapping
2.1 EEG Mapping
2.2 MEG Mapping
2.3 Surface Laplacian Mapping
3 EEG/MEG Forward Problem: Volume Source and Conductor Models
3.1 Source Models
3.2 Volume Conductor Models
3.3 Forward Solutions
3.3.1 Forward Solutions in Infinite Homogeneous Medium
3.3.2 Forward Solution in a Realistic Geometry Piecewise Homogeneous Model
4 EEG/MEG Inverse Problem: Source Imaging
4.1 Dipole Source Localization
4.1.1 Equivalent Current Dipole Models
4.1.2 Dipole Source Localization Methods
4.2 Cortical Potential Imaging
4.3 Cortical Current Density Imaging
4.3.1 Cortical Current Density Source Model
4.3.2 Linear Inverse Filters
General Inverse
Tikhonov Regularization
Truncated SVD
4.3.3 Regularization Parameters
L-Curve Method
Statistical Methods
4.3.4 Interpretation of Linear Inverse in Bayesian Theory
4.4 Volume Current Density Imaging
4.4.1 Challenges of the 3D Source Imaging
4.4.2 Inverse Techniques in Volume Current Density Imaging
4.4.3 Nonlinear Inverse Techniques
4.5 Multimodal Source Imaging Integrating Electromagnetic and Hemodynamic Imaging
5 Discussions
References
Chapter 13: Exploring Functional and Causal Connectivity in the Brain
1 Introduction
2 Basics of Functional and Causal Connectivity Analysis
2.1 Stochastic Processes and Their Characterization
2.2 Granger Causality
3 Numerical and Experimental Examples
4 Brain Causal Mapping from Electrophysiological Measurements in Humans
4.1 Analysis of Directed Cortical Interactions
4.2 Connectivity Analysis from Electrocorticogram
4.3 Connectivity Analysis from E/MEG Source Imaging
5 Software Packages for Functional and Causal Connectivity Analysis
6 Concluding Remarks
References
Chapter 14: Retinal Bioengineering
1 Introduction
2 The Neural Structure and Function of the Retina
2.1 Photoreceptors
2.2 Retinal Circuits
2.3 Receptive Fields
2.4 Eccentricity and Acuity
3 Vasculature of the Retina
4 Major Retinal Diseases
4.1 Retinitis Pigmentosa
4.2 Macular Degeneration
4.3 Glaucoma
4.4 Diabetic Retinopathy
4.5 Vascular Occlusive Disease
4.6 Retinal Detachment
5 Engineering Contributions to Understanding Retinal Physiology and Pathophysiology
5.1 Photoreceptor Models
5.1.1 Input-output Analysis of Rod Responses
5.1.2 Biochemically Based Analysis of Rod Responses
5.1.3 Responses to Steps of Light
5.1.4 Diagnostic Value of a-wave
5.2 Postreceptor ERG Analyses
5.2.1 b-wave Analyses
5.2.2 Multifocal ERG
5.3 Ganglion Cell Models
5.3.1 Systems Analysis
5.3.2 X and Y Cells in Cat
5.3.3 Difference of Gaussians Model of the Receptive Field
5.3.4 Gaussian Center-Surround Models
5.3.5 More Complex Ganglion Cell Models
5.3.6 Multielectrode Recordings
5.3.7 W Cells
6 Engineering and the Retinal Microenvironment
6.1 Oxygen
6.2 Ion Distribution
6.2.1 H+Distribution and Production
6.2.2 Retinal Extracellular Volume
6.2.3 Net Changes in Ion Distribution with Light
6.3 Relating Photoreceptor Function to Metabolism
7 Opportunities
References
Chapter 15: Retinal Prosthesis
1 Introduction
1.1 Basic Anatomy of the Eye and Retina
2 Visual Acuity
3 Acuity Vs. Eccentricity
4 How Visual Acuity Is Measured
4.1 Eye Disease
5 Retinal Prosthesis
6 Clinical Studies of Experimental Implants
7 Engineering Aspects of Visual Prostheses
7.1 Camera
7.2 Image Processing
8 Retinal Stimulating Electrodes
9 Conclusion
References
Chapter 16: Neural Interfacing with the Peripheral Nervous System: A FINE Approach
1 Neurotechnology for Interfacing with the Peripheral Nervous System
1.1 Interface Systems with Peripheral Nerves
1.2 Intrafascicular Nerve Electrodes
1.3 Flat Interface Nerve Electrode
2 Selective Recording of Peripheral Neural Activity
2.1 Detection Algorithms
2.2 Blind Source Separation
2.3 Classic Inverse Problem Solutions (IP)
2.4 Spatial Filtering or Beamforming
2.4.1 Beamforming Algorithm Mapping
2.4.2 Beamforming Filter Matrix
2.4.3 Source Localization
2.4.4 Source-Based Filter Generation
2.5 Signal Separation in Computer Models
2.5.1 Localization of Sources
2.5.2 Recovery of Two Active Sources
2.5.3 Effect of Multiple Active Fascicles
2.6 Recovery of Neural Signals from a Rabbit Sciatic Nerve
2.6.1 Recovery of Low-Frequency Evoked Activity
2.6.2 Effect of Contact Density and Position on Signal Recovery
2.6.3 Recovery of Pseudo-spontaneous Signals
3 Control of Multifasciculated Nerves with Selective Stimulation
3.1 Controller Design
3.2 Controller with Separated Static and Dynamic Properties
3.3 Multi-contact Electrode Control of Rabbit Ankle Joint
3.4 In Silico Control of Human Ankle
4 Conclusions
References
Chapter 17: Seizure Prediction
1 Introduction
2 Processes Underlying the Electroencephalogram
3 Electrographic Seizure Activity
4 Time Series Analysis and Application in EEG
4.1 Linear Methods
4.2 Surrogate time series
4.3 Nonlinear Methods
4.3.1 Lyapunov Exponent
4.3.2 Kolmogorov Entropy
4.3.3 Attractor Dimension
4.4 Multichannel Based Methods
5 Retrospective Evaluation
6 Future Prospects
Appendix 1: C Function to Calculate Maximum Likelihood Kolmogorov Entropy
Appendix 2: Matlab scripts to create Figures 17.2 and 17.5
References
Chapter 18: Reverse Engineering the Brain: A Hippocampal Cognitive Prosthesis for Repair and Enhancement of Memory Function
1 Introduction
2 Methods
2.1 Multi-input, Multi-output Nonlinear Dynamic Model of Neural Population Activities
2.1.1 Model Configuration
2.1.2 Laguerre Expansion
2.1.3 Coefficients Estimation
2.1.4 Statistical Model Selection of the MIMO Model
2.1.5 Kernel Reconstruction
2.1.6 Kernel Interpretation
2.1.7 Model Validation
2.1.8 Model Prediction
2.1.9 Estimation and Statistical Validation of Universal Models
2.2 Experimental Methods
2.2.1 Subjects and Training
2.2.2 Behavioral Apparatus and Training Procedure
2.2.3 Multi-neuron Recording of Hippocampal Ensembles
2.2.4 MIMO-Generated Ensemble Stimulation
2.3 VLSI Implementation of the MIMO Model
3 Results
3.1 Nonlinear Dynamic MIMO Models of the Hippocampal CA3-CA1 Pathway
3.2 Statistical Summary of Modeling Results
3.3 Universal Model for Multiple Long-Term Memories
3.4 The Hippocampal MIMO Model as a Cognitive Prosthesis
3.5 Use of the Hippocampal MIMO Model to Facilitate Normal Performance
3.6 Hardware Implementation
3.6.1 Device Organization
Major Functional Units
Analog Front-End
Low-Noise Amplifier
Analog-to-Digital Converter
Spike Detection and Sorting
Input Band-Pass Filters
Matched-Filter Sorter
Multiple-Input/Multiple-Output Model Computation
Stimulation Outputs
3.6.2 Physical Layout
Analog Circuit Design
Digital Circuit Design
Layout
4 Discussion
4.1 Spatio-Temporal Coding of Information in the Nervous System
4.2 Strength of the Nonlinear Multi-input, Multi-output Modeling Methodology
4.3 ``Corrective´´ Effects of the Biomimetic Hippocampal Model as a Prosthesis
4.4 Specificity of Information Included in the Hippocampal Spatio-Temporal Electrical Stimulation
4.5 Further Development of Cognitive Prostheses
References
Chapter 19: Neural Tissue Engineering
1 Introduction
1.1 The Nervous System
1.1.1 Cells of The CNS
1.1.2 Schwann Cells of the PNS
2 The Need for Neural Tissue Engineering
2.1 Nature of Deficits and Pathologies
3 Engineering Endogenous Repair
3.1 Engineering CNS Repair
3.2 Engineering PNS Repair
4 Biochemical Repair
4.1 Neurotrophic Factor Therapy
4.2 Gene Therapy
5 Cellular Replacement Therapy
5.1 Genetically Engineered Cells
5.2 Stem Cell Based Therapies
6 Facilitating Rehabilitation
6.1 Electrical Stimulation of Neuromuscular Tissue
7 Conclusions
References
Index

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