NEURAL ENGINEERING

Preface
Contents
Contributors
1 Introduction to Neurophysiology
	1.1 Overview of Neurons, Synapses, Neuronal Circuits, and Central Nervous System Anatomy
		1.1.1 Temporal and Spatial Facilitation
		1.1.2 Special Neural Circuits
		1.1.3 Reflexes
		1.1.4 Reflex Time
	1.2 Sensory Systems
		1.2.1 Properties of a Particular Stimulus
		1.2.2 Functional Organization of a Receptor
		1.2.3 The Relative Distributions of Receptors Within the Human Body
		1.2.4 Sensory Input into Motor Systems
	1.3 Somatovisceral Sensibility
		1.3.1 Processing in the Central Nervous System
		1.3.2 Basic Anatomy of the Somatosensory System
			1.3.2.1 Specific Pathways
			1.3.2.2 Nonspecific Pathways
		1.3.3 Somatosensory Projection Areas in the Cortex
		1.3.4 Mechanoreception
	1.4 General Anatomic and Functional Features of the Motor System
		1.4.1 Motor Control Hierarchy for Voluntary Movements
		1.4.2 Spinal Cord
		1.4.3 Brain Stem Components
		1.4.4 Cerebellum
		1.4.5 Motor Cortex
		1.4.6 Efferent Connections from the Motor Cortex
		1.4.7 Basal Ganglia and Thalamus
	1.5 Maintenance of Upright Posture and Sense of Equilibrium
		1.5.1 Sense of Equilibrium
			1.5.1.1 Macular Organs
			1.5.1.2 Semicircular Canals
			1.5.1.3 Central Vestibular System
			1.5.1.4 Vestibular Reflexes
	1.6 Complex Integrative Functions of the Motor System
		1.6.1 The Complex Motor Function of Speech
		1.6.2 Motor Neuron Recruitment
	1.7 Pathophysiology of the Motor System
		1.7.1 Disorders of the Spinal Cord
		1.7.2 Disruption of Functions Within the Brain Stem
		1.7.3 Disturbances Within the Cerebellum
		1.7.4 Disorders Within the Basal Ganglia
		1.7.5 Impairment Within the Motor Cortex
	1.8 The Autonomic Nervous System
		1.8.1 Sympathetic System
		1.8.2 Parasympathetic System
		1.8.3 Neurotransmitters in the ANS
		1.8.4 The Adrenal Medulla
		1.8.5 Central Organization of the ANS
	1.9 The Hypothalamus and Homeostasis
	1.10 Regulation of Body Temperature: Thermoregulation
		1.10.1 Core Temperature
		1.10.2 Cutaneous Thermoreception
		1.10.3 Central Thermoregulation
	1.11 The Limbic and the Ascending Reticular Activating Systems
		1.11.1 Function of the Various Portions of the Reticular Activating System
		1.11.2 Brain Waves
		1.11.3 Sleep
		1.11.4 Mechanisms of Sleep
	1.12 Pain
		1.12.1 Intensity of Pain (Quantity)
	1.13 Vision
		1.13.1 Functional Anatomy
		1.13.2 The Visual Focusing System
		1.13.3 Visual Receptor Cells
		1.13.4 The Receptor Transduction Process
		1.13.5 Eye Movements
	1.14 Sound and Hearing
		1.14.1 Functional Anatomy
		1.14.2 Auditory Sensations
		1.14.3 The Central Auditory System
	1.15 Taste and Smell
	Homework
	References/Additional Sources
2 Biopotential Measurements and Electrodes
	2.1 Introduction
	2.2 Electrodes for Neural Interfaces
		2.2.1 Electrode Properties and Modeling
			2.2.1.1 Electrode-Electrolyte Double-Layer Interface
			2.2.1.2 Impedance
			2.2.1.3 Half-Cell Potential
			2.2.1.4 Noise
			2.2.1.5 Water Window and Current Transfer Capacity
		2.2.2 Volume Conduction for Electrical Recording and Stimulation
	2.3 Circuit Techniques for Neural Interfaces
		2.3.1 Analog Front-Ends
		2.3.2 Intracellular Recording and Clamping Circuits
	2.4 Design Considerations and Performance Metrics
		2.4.1 Power Consumption
		2.4.2 Bandwidth
		2.4.3 Input Dynamic Range
		2.4.4 Cross-Talk
		2.4.5 Noise
			2.4.5.1 Front-End Amplifier Noise Model
			2.4.5.2 Net Noise Contributions
		2.4.6 Interference and Common-Mode Rejection
			2.4.6.1 Differential Sensing Circuit Techniques to Mitigate Common-Mode Interference
			2.4.6.2 Input Impedance-Boosting Techniques
			2.4.6.3 Active Grounding: Driven Right Leg
	2.5 Survey of Neural Engineering Applications
		2.5.1 Electrodes and Instrumentation
			2.5.1.1 Scale and Invasiveness
			2.5.1.2 Temporal, Spatial, and Spectral Resolution
			2.5.1.3 Experiment Model
			2.5.1.4 In-Ear Placement
		2.5.2 Minimally Invasive Electrocorticography
		2.5.3 Neurotechnologies for Penetrating Electrodes
	Homework
	References
3 EEG Signal Processing: Theory and Applications
	3.1 Introduction: EEG Generalities
		3.1.1 Traditional EEG Bands
		3.1.2 Paroxysmal Discharges and EEG Shapes
		3.1.3 Survey of EEG Applications
	3.2 Time-Domain Representation and Methods
		3.2.1 The Teager-Kaiser Energy Algorithm: Theory
	3.3 Frequency Domain Methods
		3.3.1 Nonparametric Spectral Methods
		3.3.2 Parametric (Modeling) Methods
			3.3.2.1 Diagnostic Power of the Autoregressive Method Is Used as a Dominant Frequency Method to Calculate Normalized Separation
		3.3.3 Parametric Methods of Signal Processing: The MUSIC Algorithm
		3.3.4 Wavelets
			3.3.4.1 The Wavelet Transform: Variable Time and Frequency Resolution. The Continuous Wavelet Transform (CWT)
			3.3.4.2 The Discrete Wavelet Transform
			3.3.4.3 Application of Wavelets and Entropy: The Definition of IQ—Information Quantity
	3.4 An Application of EEG: Detecting Brain Injury After Cardiac Arrest
		3.4.1 Experimental Methods for Hypoxic-Asphyxic Cardiac Arrest and the Use of Normalized Separation
		3.4.2 Detecting and Counting Bursts
		3.4.3 EEG and Entropy: A Novel Approach to Brain Injury Monitoring
		3.4.4 Enhancing Recovery from Cardiac Arrest: The Use of Orexin
	3.5 Conclusion
	Homework
	References
4 Brain–Computer Interfaces
	4.1 Introduction
	4.2 BCI Definition and Structure
		4.2.1 What Is a BCI?
		4.2.2 Alternative or Related Terms
		4.2.3 The Components of a BCI
		4.2.4 The Unique Challenge of BCI Research and Development
		4.2.5 BCI Operation Depends on the Interaction of Two Adaptive Controllers and the User Interface
		4.2.6 Choosing Signals and Brain Areas for BCIs
	4.3 Signal Acquisition
		4.3.1 Invasive Techniques
			4.3.1.1 Intracortical
			4.3.1.2 Cortical Surface
		4.3.2 Noninvasive Techniques
			4.3.2.1 EEG
			4.3.2.2 MEG
			4.3.2.3 fMRI
			4.3.2.4 NIRS
		4.3.3 Neural Signals Used by BCIs
			4.3.3.1 Sensorimotor Rhythms
			4.3.3.2 Slow Cortical Potentials
			4.3.3.3 The P300 Event-Related Potential
			4.3.3.4 Event-Related Potentials
			4.3.3.5 Spikes and Local Field Potentials
	4.4 Signal Processing
		4.4.1 Feature Extraction
			4.4.1.1 Artifact/Noise Removal and Signal Enhancement
			4.4.1.2 Feature Extraction Methods
			4.4.1.3 Feature Selection and Dimensionality Reduction
		4.4.2 Feature Translation
			4.4.2.1 Continuous Feature Translation
			4.4.2.2 Discrete Feature Translation
	4.5 Major BCI Applications
		4.5.1 Replacing Lost Communication
		4.5.2 Replacing Lost Motor Function and Promoting Neuroplasticity to Improve Defective Function
		4.5.3 Supplementing Normal Function
		4.5.4 Augmenting/Virtualizing Reality with BCI
		4.5.5 Providing Neurofeedback
	4.6 Examples of EEG-Based BCI Systems
		4.6.1 General-Purpose Software Platforms for BCI Research
		4.6.2 BCIs Based on Sensorimotor Rhythms
		4.6.3 BCIs Based on P300
		4.6.4 BCIs Based on Visual Evoked Potentials
		4.6.5 BCIs Based on Auditory Evoked Potentials
		4.6.6 Hybrid BCI
		4.6.7 Attention-Based BCI
		4.6.8 BCIs for Brain-to-Brain Communications and Interactions
	4.7 BCI Performance Assessment and Training
		4.7.1 User Performance Assessment
		4.7.2 System Performance Assessment
	4.8 Future Perspectives
		4.8.1 Expectations
		4.8.2 Signal Acquisition and Processing
		4.8.3 Clinical and Practical Validation
		4.8.4 BCI Training
		4.8.5 Recognition of BCI Efficiency and Inefficiency
		4.8.6 Reciprocal Learning Between the Machine and the Brain
	4.9 Conclusion
	Homework
	References
5 Intracortical Brain–Machine Interfaces
	5.1 What Is a Brain–Machine Interface?
		5.1.1 History of Intracortical BMIs
		5.1.2 Components of an Intracortical BMI
	5.2 Choosing the Input for iBMIs
		5.2.1 Neural Signal Recordings
		5.2.2 Multielectrode Arrays
		5.2.3 Motor Neurophysiology
	5.3 Intracortical Spelling Devices
		5.3.1 Classification Decoders
	5.4 Intracortical Control of Continuous Effectors
		5.4.1 Population Vector Algorithm
		5.4.2 Optimal Linear Estimator
		5.4.3 Kalman Filter
	5.5 Reanimating Paralyzed Limbs
		5.5.1 Functional Electrical Stimulation
		5.5.2 FES Systems
		5.5.3 Brain-Controlled FES
		5.5.4 Challenges for FES
	5.6 The Future of iBMIs
		5.6.1 Restoring Somatosensory Feedback
		5.6.2 Building Better Electrodes
		5.6.3 iBMIs for Basic Science
	Homework
	References
6 Deep Brain Stimulation: Emerging Technologies and Applications
	6.1 Introduction
	6.2 State of the Art in DBS Instrumentation
	6.3 Current Understanding of the Therapeutic Mechanisms of DBS
	6.4 Novel Temporal Patterns of Stimulation as a Therapeutic Innovation
	6.5 Innovations in DBS Electrode Design
	6.6 Imaging and Computational Tools for Personalized DBS
	6.7 Development of Closed-Loop DBS Systems
	6.8 Emerging Indications
	6.9 Nonsurgical Approaches for Deep Brain Stimulation
		6.9.1 Focused Ultrasound
		6.9.2 Temporal Interference
	6.10 Discussion
	Homework
	References
7 Transcranial Magnetic Stimulation: Principles and Applications
	7.1 Introduction
	7.2 Devices
		7.2.1 Pulse Generators
			7.2.1.1 Circuit Topology
			7.2.1.2 Energy Efficiency and Repetitive TMS
		7.2.2 Coils
			7.2.2.1 Coil Heating
			7.2.2.2 Coil Forces
		7.2.3 Device Safety
			7.2.3.1 Interaction with Other Devices
	7.3 Physics
		7.3.1 Magnetic Field
		7.3.2 Induced Electric Field
		7.3.3 Electric Field Models
			7.3.3.1 Reciprocity to Magnetoencephalography
			7.3.3.2 Fundamental Limitations of Induced Electric Field
	7.4 Biophysics
		7.4.1 Neuronal Membrane Depolarization in Response to Electric Field
		7.4.2 Neural Activation Models
	7.5 Measuring Responses to Stimulation
		7.5.1 Characterization of Behavior, Cognition, or Emotional State
		7.5.2 Electrophysiological and Imaging Methods
	7.6 Stimulation Paradigms and Applications
		7.6.1 Single Pulses
		7.6.2 Paired Pulses
		7.6.3 Pulse Trains
		7.6.4 Clinical Applications
		7.6.5 Research Applications
	7.7 Conclusions
	Homework
	References
8 Transcranial Electrical Stimulation
	8.1 Basics of tES Devices and Dose
	8.2 General Design Aspects of tES Electrodes
	8.3 tES Electrodes: Sponge Electrode
	8.4 tES Electrodes: Self-Adhesive Electrode
	8.5 tES Electrodes: High-Definition Electrode (HD Electrode)
	8.6 Electrode Resistance
	8.7 Current Control, Voltage Limits
	8.8 Indications for tES Use
	8.9 Current Flow Modeling Informs Device/Electrode Design and Setup
	8.10 tES Biophysics/Mechanisms
	8.11 Tolerability of tES Devices
	Homework
	References
9 Optogenetics: Novel Brain Interface Technology That Originates in Bioprospecting
	9.1 Introduction
	9.2 Tools of Optogenetics
		9.2.1 Opsins for Excitation and Inhibition
		9.2.2 Mechanisms of Gene Delivery
		9.2.3 Target Species for Optogenetic Experiments
	9.3 Mechanisms of Light Delivery
		9.3.1 Light-Tissue Interaction
		9.3.2 Light-Guiding Systems
		9.3.3 Spatial Light Modulators
		9.3.4 Biological Sources for Light
	9.4 Hybrid Platforms
		9.4.1 Optogenetic Neural Probes
		9.4.2 Two-Photon Optogenetic Stimulation
		9.4.3 Optogenetic Stimulation and Coherence Tomography
		9.4.4 Optogenetic Functional Magnetic Resonance Imaging (ofMRI)
	9.5 Optogenetic Stimulation for Therapy
	Homework
	References
10 Selective Chronic Recording in the Peripheral Nervous System
	Abbreviations
	10.1 Introduction
	10.2 Movement Intent Recovery
	10.3 High SNR Amplification of Neural Signals
	10.4 EMG Interference and Rejection
		10.4.1 Increasing SNR with Open Bandwidth
	10.5 Separation Algorithm Derivation
	10.6 Chronic Nerve Recordings
	10.7 Computer Model of Neural Recording Properties of a FINE
		10.7.1 Finite Element Model
		10.7.2 NEURON Model
		10.7.3 Single Fiber Action Potential Through FINE Simulation
		10.7.4 Generating Representative Spectra of ENG
	10.8 Summary and Conclusions
	Homework
	References
11 Functional Magnetic Resonance Imaging
	11.1 Introduction
	11.2 Magnetic Resonance Imaging
	11.3 Blood Oxygenation Level–Dependent Contrast
	11.4 BOLD Response to Neural Activity
	11.5 Hemodynamic Response Function
	11.6 Event-Related and Block Design
	11.7 BOLD Time Series Analysis
	11.8 Task fMRI for Functional Mapping
	11.9 Resting State fMRI
	11.10 Naturalistic Paradigm
	11.11 Summary
	Homework
	References
12 Photoacoustic Tomography of Neural Systems
	12.1 Introduction to Photoacoustic Tomography
	12.2 Photoacoustic Generation and Propagation
		12.2.1 Initial Photoacoustic Pressure
		12.2.2 General Photoacoustic Equation
		12.2.3 General Forward Solution
	12.3 Photoacoustic Detection and Image Reconstruction
		12.3.1 Photoacoustic Detection
		12.3.2 General Image Reconstruction
	12.4 Implementations of Photoacoustic Tomography
		12.4.1 Photoacoustic Computed Tomography
		12.4.2 Photoacoustic Microscopy
	12.5 Photoacoustic Tomography for Neural Imaging
		12.5.1 Photoacoustic Tomography of the Brain
			12.5.1.1 PAM of the Brain Vasculature at Single-Cell Resolution
			12.5.1.2 Label-Free Histology-Like PAM of the Mouse Brain and Peripheral Nerves
			12.5.1.3 Label-Free PACT of the Mouse Brain Structures
			12.5.1.4 Spectral PAT for Neuroimaging
		12.5.2 Photoacoustic Tomography of Neural Activities
			12.5.2.1 Monitoring Brain Hemodynamic Responses at Multiple Scales
			12.5.2.2 Mapping Large-Scale Neural Activities
			12.5.2.3 Imaging Brain Diseases at the Whole Brain Level
			12.5.2.4 Imaging Brain Glucose Metabolism
			12.5.2.5 Visualizing Neural Activities Using Voltage-/Calcium- Sensitive Indicators
			12.5.2.6 Outlook
	Homework
	References
13 Electrophysiological Mapping and Source Imaging
	13.1 Introduction
		13.1.1 Generation and Measurement of EEG and MEG
		13.1.2 Spatial and Temporal Resolution of EEG and MEG
	13.2 Electrophysiological Mapping
		13.2.1 EEG Mapping
		13.2.2 MEG Mapping
		13.2.3 Surface Laplacian Mapping
		13.2.4 Multivariate Pattern Analysis of EEG and MEG Signals
	13.3 EEG/MEG Forward Modeling
		13.3.1 Source Models
		13.3.2 Volume Conductor Models
		13.3.3 Forward Solutions
	13.4 EEG/MEG Source Imaging
		13.4.1 Dipole Source Localization
		13.4.2 Cortical Potential Imaging
		13.4.3 Cortical Current Density Source Imaging
			13.4.3.1 Cortical Current Density Source Model
			13.4.3.2 Linear Inverse Filters
			13.4.3.3 Regularization Parameters
			13.4.3.4 Interpretation of Linear Inverse in Bayesian Theory
		13.4.4 Volume Current Density Source Imaging
			13.4.4.1 Challenges of the 3D Source Imaging
			13.4.4.2 Inverse Estimation Techniques in Volume Current Density Imaging
			13.4.4.3 Nonlinear Inverse Techniques
		13.4.5 Multimodal Source Imaging Integrating Electromagnetic and Hemodynamic Imaging
	13.5 Getting Started with Electrophysiological Imaging and Data Processing
	13.6 Discussions
	Homework
	References
14 Exploring Functional and Causal Connectivityin the Brain
	14.1 Introduction
	14.2 Basics of Functional and Causal Connectivity Analysis
		14.2.1 Stochastic Processes and Their Characterization
		14.2.2 Granger Causality
	14.3 Numerical and Experimental Examples
	14.4 Brain Causal Mapping from Electrophysiological Measurements in Humans
		14.4.1 Analysis of Directed Cortical Interactions
		14.4.2 Connectivity Analysis from Electrocorticogram
		14.4.3 Connectivity Analysis from E/MEG Source Imaging
	14.5 Software Packages for Functional and Causal Connectivity Analysis
	14.6 Concluding Remarks
	Homework
	References
15 Deep Learning Models with Applications to Brain Image Analysis
	15.1 Background
	15.2 Image Processing and Concept of Deep Learning
		15.2.1 Brain Image Pre-processing
		15.2.2 Fundamentals About Neural Network Models
	15.3 Convolutional Neural Networks
		15.3.1 CNN Fundamentals
		15.3.2 CNN Variants
		15.3.3 Residual Learning Based on CNN
		15.3.4 Fully Convolutional Networks and U-Net
		15.3.5 Combination of CNNs
		15.3.6 CNN Applications to Brain Image Classification and Segmentation
		15.3.7 Brain Image Classification
		15.3.8 Brain Image Segmentation
	15.4 Recurrent Neural Networks
		15.4.1 Recurrent Neural Networks (RNNs): Basic Model
		15.4.2 Long Short-Term Memory (LSTM) Model
		15.4.3 RNN Applications to Time Series Data Analysis
	15.5 Auto-encoder
		15.5.1 AE Applications to Feature Learning in Brain Image Analysis
		15.5.2 Brain Image Classification
		15.5.3 Brain Image Registration
	15.6 Generative Adversarial Networks
		15.6.1 Principle of GAN
		15.6.2 GAN Variants
		15.6.3 Pix2Pix GANs
		15.6.4 CycleGAN
		15.6.5 GAN Applications to Brain Image Analysis
		15.6.6 Brain Image Synthesis
		15.6.7 Brain Image Augmentation
	15.7 Discussion
	15.8 Conclusion
	Homework
	References
16 Neural Modeling
	16.1 Why Build Neural Models?
	16.2 Basic Properties of Excitable Membranes
		16.2.1 Membrane Properties
		16.2.2 Equivalent Circuit Representation
			16.2.2.1 Membrane Capacitance
			16.2.2.2 Membrane Conductance
			16.2.2.3 Normalized Units for the Passive Membrane
			16.2.2.4 Passive Membrane Representation
	16.3 Excitability
		16.3.1 Electric Potentials
		16.3.2 Resting Potential
		16.3.3 Voltage-Gated Conductances
		16.3.4 The Hodgkin-Huxley Model: Action Potentials in the Squid Giant Axon
			16.3.4.1 Voltage Clamp and Space Clamp
			16.3.4.2 Ionic Conductances
			16.3.4.3 Model of the Potassium and Sodium Conductance
			16.3.4.4 Potassium and Sodium Currents
			16.3.4.5 Complete Hodgkin-Huxley Model
			16.3.4.6 Normalized Units in the Hodgkin-Huxley Model
		16.3.5 Behavior of theHodgkin- Huxley Model
			16.3.5.1 Action Potentials and Threshold
			16.3.5.2 Refractory Period
		16.3.6 Assumptions of the Model
	16.4 Propagating Activity
	16.5 Diversity in Channels and Electrical Activity
		16.5.1 Bursting
		16.5.2 Subthreshold Oscillations
		16.5.3 After-Hyperpolarizations and After-Depolarizations
		16.5.4 Spike-Frequency Adaptation
		16.5.5 Bistability
		16.5.6 Post-Inhibitory Rebound Spiking
	16.6 Nonlinear Dendritic Processing
		16.6.1 Dendritic Channel Expression
		16.6.2 Dendritic Excitability
	16.7 Simple Neural Models
		16.7.1 Integrate-and-Fire Model
		16.7.2 Behavior of the Leaky Integrate-and-Fire Model
		16.7.3 Modified Integrate-and-Fire Models
			16.7.3.1 Resonate-and-Fire Models
			16.7.3.2 Quadratic Integrate-and-Fire Models
			16.7.3.3 Complexity in Simple Models
	16.8 Generalized Linear Model
		16.8.1 Linear-Nonlinear Poisson Model
		16.8.2 Generalized Linear Model with Spike History Dynamics
	16.9 Similar Phenotypes Arising from Disparate Mechanisms
	16.10 Synapse Models
	16.11 Short-Term Synaptic Plasticity
	16.12 Beyond Single Neurons
		16.12.1 Feed-Forward Networks
		16.12.2 Persistent Activity
	16.13 Neural Modeling in Medicine
	16.14 Modeling Resources
	Homework
	References
17 Linear Dynamics and Control of Brain Networks
	17.1 Emergence in the Structure and Function of Complex Systems
	17.2 Quantitative Dynamical Models of Neural Systems and Interactions
		17.2.1 Spatial and Temporal Considerations
		17.2.2 Dynamical Model Approximations
		17.2.3 Incorporating Exogenous Control
		17.2.4 Model Linearization
	17.3 Theory of Linear Systems
		17.3.1 Impulse Response
		17.3.2 Control Response
		17.3.3 Linear Relation Between the Convolution and Control Input
		17.3.4 Controllability
		17.3.5 Minimum Energy Control
	17.4 Mapping Network Architecture to Control Properties
		17.4.1 Neuronal Control in Model Organisms
		17.4.2 State Transitions in the Human Brain
	17.5 Methodological Considerations and Limitations
		17.5.1 Dimensionality and Numerical Stability
		17.5.2 Model Validation and Experimental Data
		17.5.3 Assumption of Linearity
	17.6 Open Frontiers
		17.6.1 Theory and Statistics
		17.6.2 Context, Computations, and Information Processing
		17.6.3 Disease and Intervention
	Homework
	References
18 Deciphering the Neuronal Population Code
	18.1 Introduction
	18.2 Extracting Information from Single Neurons
	18.3 Correlation in Pairs of Neurons
	18.4 Synchrony in Pairs of Neurons
	18.5 Beyond Pairwise Correlation
		18.5.1 Relating Neurons to Behavior
	18.6 Developing Hypotheses About the Structure and Function of Neuronal Population Activity
	18.7 Conclusion
	Homework
	References
19 Machine Intelligence-Based Epileptic Seizure Forecasting
	19.1 Introduction
	19.2 Feature Extraction
		19.2.1 Rhythms of the Brain
		19.2.2 Wavelet Phase Coherence
		19.2.3 Cross-Frequency Coupling
		19.2.4 Model Performance
	19.3 Seizure Detection and Forecasting
		19.3.1 Linear Methods
		19.3.2 Tree-Based Methods
		19.3.3 Deep Neural Networks
		19.3.4 Improving Model Performance
	19.4 Other Applications of Machine Intelligence with EEG
		19.4.1 Prediction of Antiepileptic Drug Treatment Outcomes
	19.5 Current Challenges and Future Directions
	Homework
		Conceptual Questions
		Practical Analysis Questions
	References
20 Retinal Prosthesis
	20.1 Introduction
	20.2 Basic Anatomy of the Eye and Retina
		20.2.1 Eye Disease
		20.2.2 Retinal Prosthesis
		20.2.3 Clinical Studies
	20.3 Retinal Prostheses Research
		20.3.1 Camera
		20.3.2 Image Processing
		20.3.3 Retinal Stimulating Electrode Arrays
	20.4 Conclusion
	Homework
	References
21 Retinal Bioengineering
	21.1 Introduction
	21.2 The Neural Structure and Function of the Retina
		21.2.1 Photoreceptors
		21.2.2 Retinal Circuits
		21.2.3 Receptive Fields
		21.2.4 Eccentricity and Acuity
	21.3 Vasculature of the Retina
	21.4 Major Retinal Diseases
		21.4.1 Retinitis Pigmentosa
		21.4.2 Macular Degeneration
		21.4.3 Glaucoma
		21.4.4 Diabetic Retinopathy
		21.4.5 Vascular Occlusive Disease
		21.4.6 Retinal Detachment
	21.5 Engineering Contributions to Understanding Retinal Physiology and Pathophysiology
		21.5.1 Photoreceptor Models
			21.5.1.1 Input-Output Analysis of Rod Responses
			21.5.1.2 Biochemically Based Analysis of Rod Responses
			21.5.1.3 Responses to Steps of Light
			21.5.1.4 Diagnostic Value of a-Wave
		21.5.2 Post-Receptor ERG Analyses
			21.5.2.1 B-Wave Analyses
			21.5.2.2 Computing ERGs from Specific Retinal Areas
		21.5.3 Ganglion Cell Models
			21.5.3.1 Systems Analysis
			21.5.3.2 X and Y Cells in Cat
			21.5.3.3 Difference of Gaussians Model of the Receptive Field
			21.5.3.4 Gaussian Center-Surround Models
			21.5.3.5 More Complex Retinal Ganglion Cell Models
			21.5.3.6 Multielectrode Recordings
			21.5.3.7 Other Types of Retinal Ganglion Cells
		21.5.4 Retinal Connectivity Models
	21.6 Engineering and the Retinal Microenvironment
		21.6.1 Oxygen
		21.6.2 Ion Distribution
			21.6.2.1 H+ Distribution and Production
			21.6.2.2 Retinal Extracellular Volume
			21.6.2.3 Net Changes in Ion Distribution with Light
	21.7 Opportunities
	Homework
	References
22 Neural Tissue Engineering
	22.1 Introduction
		22.1.1 Tissues of the Nervous System
			22.1.1.1 Cells and Tissues of the Brain
			22.1.1.2 Tissue and Cells of the Spinal Cord
			22.1.1.3 Tissue and Cells of the Peripheral Nervous System
		22.1.2 Targets of Tissue Engineering Approaches in the Nervous System
			22.1.2.1 Nervous System Injuries
			22.1.2.2 Degenerative Disease of the Nervous System
			22.1.2.3 Neural Device Integration
	22.2 Tissue Engineering Technologies
		22.2.1 Material Infrastructure for Regeneration
			22.2.1.1 Nerve Conduits for PNS Repair
			22.2.1.2 Implantable Hydrogels for CNS Regeneration
		22.2.2 Neurotrophic Factors
		22.2.3 Cellular Engineering Approaches
			22.2.3.1 Stem Cells
			22.2.3.2 Genetic Engineering
			22.2.3.3 Gene Therapy
		22.2.4 Immunomodulation
		22.2.5 Electrical Stimulation for Repair and Regeneration
	22.3 Conclusion
	Homework
	References
Index