Dynamic Neural Field Theory for Motion Perception

Dynamic Neural Field Theory for Motion Perception by Martin A. Giese.

Dynamic Neural Field Theory for Motion Perception provides a new theoretical framework that permits a systematic analysis of the dynamic properties of motion perception. This framework uses dynamic neural fields as a key mathematical concept. The author demonstrates how neural fields can be applied...

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Bibliographic Details
Main Author: Giese, Martin A. (Author)
Corporate Author: SpringerLink (Online service)
Format: eBook
Language:English
Published: New York, NY : Springer US : Imprint: Springer, 1999.
Edition:1st ed. 1999.
Series:The Springer International Series in Engineering and Computer Science, 469
Springer eBook Collection.
Subjects:
Statistical physics.
Dynamical systems.
Optical data processing.
Neurosciences.
Neuropsychology.
Electronic resources (E-books)
Online Access:Click to view e-book
Holy Cross Note:Loaded electronically.
Electronic access restricted to members of the Holy Cross Community.
Table of Contents:
  • 1 Introduction
  • I Basic Concepts
  • 2 Visual perception of motion
  • 3 Basic principles of the dynamic approach
  • 4 Dynamic neural fields
  • II Model for Motion Perception
  • 5 Dynamic neural field model for motion perception
  • 6 Necessity of the concepts: Model for the motion quartet
  • 7 Sufficiency of the concepts: Field model for 2D-motion perception
  • 8 Relationships: neural fields and computational algorithms
  • 9 Identification of field models from neurophysiological data
  • III Other Applications of Neural Fields
  • 10 Neural field model for the motor planning of eye movements
  • 11 Technical applications of neural fields
  • 12 Discussion
  • Appendices
  • A Appendix of chapter 3
  • A.1 Relationship: Eye-Position and Relative Phase Dynamics
  • B Appendix of chapter 6
  • B.1 Geometry Dependence of Feed-Forward Input
  • B.2 Stochastic Bistable Dynamics
  • B.3 Parameters of the Model for the Motion Quartet
  • C Appendix of chapter 7
  • C.1 Properties of the Interaction Function
  • C.2 One-Dimensional Neural Field Model for Motion Direction
  • C.3 Parameters of the Neural Field Model
  • D Appendix of chapter 8
  • D.1 Proof of Theorem 4
  • D.2 Proof of Lemma 1
  • D.3 Proof of Theorem 5
  • E Appendix of chapter 9
  • E.2 Least Squares Estimation of Kernel Functions
  • E.3 Equivalent Feed-Forward System for a Linear Threshold
  • F Appendix of chapter 11
  • F. 1 Transformation between Robot and World Coordinates
  • F.2 Transformations between the Perceptive Spaces
  • F.3 Learning of the Parameters of the Approximation Dynamics
  • List of Symbols.

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