Neural Dynamics of Adaptive Sensory-Motor Control

Name: Neural Dynamics of Adaptive Sensory-Motor Control | Ballistic Eye Movements
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Ballistic Eye Movements

Elsevier (Publisher)

Published on 10. October 2011

335 pages

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978-0-08-086684-0 (ISBN)

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Front Cover
Neural Dynamics of Adaptive Sensory-Motor Control: Ballistic Eye Movements
Copyright Page
Table of Contents
CHAPTER 1. MULTIPLE LEARNING PROBLEMS ARE SOLVED BY SENSORY-MOTOR SYSTEMS
1.1. Introduction: Brain Designs are Adaptive Designs
1.2. Eye Movements as a Model Sensory-Motor System
1.3. Intermodality Circular Reactions: Learning Gated by Comparison of Target, Position with Present Position
1.4. Learning a Target Position Map
1.5. From Multimodal Target Map to Unimodal Motor Map
1.6. Vector Maps from Comparisons of Target Position Maps and Present Position Maps
1.7. Automatic Compensation for Present Position: Code Compression
1.8. Outflow vs. Inflow in the Registration of Present Position
1.9. Corollary Discharges and Calibration of Muscle Plant Contractions
1.10. Outflow-Inflow Pattern Matches and Linearization of Muscle Responses: Automatic Gain Control
1.11. Motor Vectors Calibrated by visual Error Signals
1.12. Postural Stability: Separate Calibration of Muscle Length and Tension
1.13. Planned vs. Reactive Movements: The Rear View Mirror Problem
1.14. Attentional Gating
1.15. Intermodality Interactions in a Head Coordinate Frame
1.16. Head Coordinate Maps Encode Predictive Saccades
1.17. The Relationship between Macrotheory and Microtheory
CHAPTER 2. PARALLEL PROCESSING OF MOVEMENT AND ERROR SIGNALS
2.1. Sensory-Motor Coordinates: Hemifield Gradients
2.2. Choice of Fixation Light: Network Competition
2.3. Correcting Fixation Errors: Competition Precedes Storage in Sensory Short Term Memory
2.4. Parallel Processing of Movement and Error Signals
2.5. Why Does A Saccade Generator Exist?
2.6. Competitive Choice and Storage in Short Term Memory
CHAPTER 3. SACCADIC LEARNING USING VISUAL ERROR SIGNALS: SELF-MOTION VS. WORLD-MOTION AND CEREBELLAR DYNAMICS
3.1. Compensation for Initial Position in the Movement Signal
3.2. Explicit vs. Implicit Knowledge of Initial Position
3.3. Characterization of Correctable Errors
3.4. Self-Movement vs. World-Movement: Ballistic vs. Continuous Movement
3.5. A Universal Adaptive Gain Control Mechanism: Saccades, VOR, Posture, and Muscle Gain
3.6. Compatibility of Design Hypotheses
3.7. Different Coordinates for Unconditioned and Conditioned Movement Systems
3.8. Correcting Undershoot, Overshoot, and Skewed Saccadic Errors
3.9. Curvature Distorting Contact Lens vs. Inverting Contact Lens
3.10. Equal Access to Error Signals: Separate Anatomies for Unconditioned Movements and Conditioned Gain Changes
3.11. Anatomical Interpretation of the Adaptive Gain Stage: The Cerebellum
3.12. Superposition of Sampling Map and Error Signal Map: Logarithms and Bidirectional Parallel Fibers
3.13. Fractured Somatotopy and/or Bilateral Cerebellar Organization
3.14. More Constraints on Cerebellar Learning
3.15. Dual Action, Incremental Learning, and Error Signal Attenuation
3.16. Numerical Studies of Adaptive Foveation due to Cerebellar Gain Changes: Learned Compensation for System Nonlinearities
3.17. Shared Processing Load and Recovery from Lesions
3.18. Models of Saccadic Error Correction
3.19. Dynamic Coasting
3.20. Outflow-Inflow Comparisons: A Large Movement as a Series of Small Movement Segments
3.21. Mismatch due to Plant Nonlinearity or to Dynamic Coasting?
3.22. Adaptive Control of Dynamic Coasting
CHAPTER 4. COMPARING TARGET POSITION WITH PRESENT POSITION: NEURAL VECTORS
4.1. Reconciling Visually Reactive and Intentional Computations
4.2. Experimental Evidence for Vector Inputs to the Superior Colliculus
4.3. Adaptive Inhibitory Efference Copy in Motor Control
4.4. Multistage Elaboration of a Vector Map
4.5. Attention Modulation in Parietal Cortex and Inhibitory Gating of SC Signals: The Delay in Vector Subtraction
4.6. Stages in the Adaptive Neural Computation of a Vector Difference
4.7. Modulators of Head-to-Muscle Coordinate Learning
4.8. Mathematical Design of the Head-Muscle Interface
4.9. Muscle Linearization and Retinotopic Recoding
4.10. Saccade Staircases and Automatic Compensation for Present Position
4.11. Corrective Saccades in the Dark: An Outflow Interpretation
CHAPTER 5. ADAPTIVE LINEARIZATION OF THE MUSCLE PLANT
5.1. Fast Corrective Saccades vs. Slow Muscle Linearization
5.2. Muscle Linearization Network
5.3. Cerebellar Direct Response Cells
5.4. Adaptation To Strabismus Surgery
5.5. Error Correction with and without Adaptive Gain Changes
5.6. Matching within the Outflow-Inflow Interface
5.7. An Explanation of the Steinbach and Smith Data
5.8. A Role for Golgi Tendon Organs in Muscle Linearization
5.9. Dynamic Linearization: Adaptive Sampling during Saccades
5.10. An Agonist-Antagonist Ratio Scale
5.11. Sampling from a Spatial Map of Outflow Position
CHAPTER 6. SPATIAL MAPS OF MOTOR PATTERNS
6.1. The General Problem: Transforming Pattern Intensities into Map Positions
6.2. Antagonistic Positional Gradients, Contrast Enhancement, and Coincidence Detectors
6.3. Position-Threshold-Slope Shift, Maps
6.4. Self-organizing Spatial Maps
6.5. Activity-Dependent Map Formation
6.6. Coding of Movement Length and Direction
6.7. Normalization of Total PTS Shift Map
CHAPTER 7. SACCADE GENERATOR AND SACCADE RESET
7.1. Saccade Generator
7.2. Converting an Intensity Code into a Duration Code
7.3. Summation of Retinotopic and Initial Eye Position Signals Signals to the Saccade Generator
7.4. The Eye Position Update Network
7.5. Two Types of Initial Position Compensation: Eye Position Update and Muscle Linearization
7.6. Saccade Staircases
7.7. Circuit Design of the Eye Position Update Network
7.8. A Saccade Generator Circuit
7.9. Computer Simulations of a Saccade Generator Model
7.10. Comparison of Computer Simulations with Neural Data
CHAPTER 8. POSTURAL STABILITY AND LENGTH- TENSION REGULATION
8.1. Separate Postural and Movement Systems
8.2. Tension Equalization Network
8.3. Design of the Tension Equalization Network
8.4. Adaptive Step Gain and Pulse Gain: Correcting Post-Saccadic Drift
8.5. Relationship to the Vestibulo-Ocular Reflex
8.6. Cerebellar Functional Heterogeneity
CHAPTER 9. SACCADIC RHYTHM AND PREDICTIVE MOVEMENT SEQUENCES
9.1. Rhythmic Choices among Multiple Movement Sources
9.2. Distinguishing Correct Predictive Saccades from Incorrect Individual Saccades
9.3. The Temporal Control of Predictive Saccades
9.4. Storage of Temporal Order Information
9.5. Design of a Predictive Command Network
9.6. Saccade Generation by Predictive Commands
9.7. Two Types of Output Gates: Target-Driven Gates and Saccade-Driven Gates
9.8. Parietal Light-Sensitive and Saccade Neurons
9.9. Switching between Movement and Postural Eye Position Maps: Frontal Eye Field Control
9.10. Direct Computation of Predictive Difference Vectors from Stored Retinotopic Positions?
CHAPTER 10. FORMATION OF AN INVARIANT TARGET POSITION MAP
10.1. Invariant Self-Regulating Multimodal Maps
10.2. Prewired Positional Gradients: The Mean Value Property
10.3. Self-organizing Target Position Maps: Multimodal Sampling of a Unimodal Eye Position Map
10.4. Double Duty by Sampling Maps and their Neural Interpretation
10.5. Associative Learning at Autoreceptive Synaptic Knobs
10.6. Multimodal Learning of Invariant Self-Regulating Spatial Maps
10.7. Multimodal Learning of an Invariant Self-Regulating Target Position Map
10.8. Associative Pattern Learning
CHAPTER 11. VISUALLY REACTIVE, MULTIMODAL, INTENTIONAL, AND PREDICTIVE MOVEMENTS: A SYNTHESIS
11.1. Avoiding Infinite Regress: Planned Movements Share Reactive Movement Parameters
11.2. Learning and Competition from a Vector-Based Map to a Light-Based Map
11.3. Associative Pattern Learning and Competitive Choice: Non-Hebbian Learning Rule
11.4. Light Intensity, Motion, Attentional, and Multimodal Interactions within the Parietal Cortex
11.5. Nonspecific and Specific Attentional Mechanisms
11.6. Multiple Retinotopic Maps
11.7. Interactions between Superior Colliculus, Visual Cortex and Parietal Cortex
11.8. Multiple Target Position Maps within Parietal Cortex and Frontal Eye Fields
11.9. Learning Multiply-Activated Target Position Maps
11.10. Multiple Parietal and Frontal Eye Field Vector Systems
11.11. Learning Neural Vectors and Adaptive Gains in a Predictive Movement System
11.12. Frontal Eye Field Control of Voluntary Saccadic Eye Movements and Posture: Cell Types
11.13. Coupled Vector and Adaptive Gain Learning
11.14. Gating of Learning, Movement, and Posture
11.15. When Saccade Choice May Fail: Saccadic Averaging and Partial Vector Compensation
CHAPTER 12. ARE THERE UNIVERSAL PRINCIPLES OF SENSORY-MOTOR CONTROL?
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