
Neural Engineering
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Content
- 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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