Models of Neural Networks IV Early Vision and Attention

Models of Neural Networks IV Early Vision and Attention / edited by J. Leo van Hemmen, Jack D. Cowan, Eytan Domany.

Close this book for a moment and look around you. You scan the scene by directing your attention, and gaze, at certain specific objects. Despite the background, you discern them. The process is partially intentional and partially preattentive. How all this can be done is described in the fourth volu...

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Bibliographic Details
Corporate Author: SpringerLink (Online service)
Other Authors: van Hemmen, J. Leo (Editor), Cowan, Jack D. (Editor), Domany, Eytan (Editor)
Format: eBook
Language:English
Published: New York, NY : Springer New York : Imprint: Springer, 2002.
Edition:1st ed. 2002.
Series:Physics of Neural Networks,
Springer eBook Collection.
Subjects:
Artificial intelligence.
Computational complexity.
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 Neurons, Networks, and Cognition: An Introduction to Neural Modeling
  • 1.1 Introduction
  • 1.2 Neurons
  • 1.3 Local Cortical Network Dynamics
  • 1.4 Collective Computation: Associative Memory
  • 1.5 Concluding Remarks
  • 1.6 Acknowledgments
  • 1.7 References
  • 2 The Gaze Control System
  • 2.1 Introduction
  • 2.2 The Gaze Control System in One and Two Dimensions
  • 2.3 New Aspects for Eye Rotations in 3D
  • 2.4 Mathematics of 3D Rotational Kinematics
  • 2.5 Donders’ Law and Listing’s Law
  • 2.6 Head-free Saccadic Gaze Shifts in 3D
  • 2.7 Conclusion
  • 2.8 References
  • 3 Integrating Anatomy and Physiology of the Primary Visual Pathway: From LGN to Cortex
  • 3.1 Introduction
  • 3.2 The LGN
  • 3.3 Models of the LGN
  • 3.4 The Visual Cortex
  • 3.5 Models of the Visual Cortex
  • 3.6 References
  • 4 Neural Principles of Preattentive Scene Segmentation: Hints from Cortical Recordings, Related Models, and Perception
  • 4.1 Introduction
  • 4.2 Properties of Synchronized Fast Cortical Oscillations (FCOs)
  • 4.3 Coding Contour Continuity
  • 4.4 Coding Region Continuity
  • 4.5 Coding the Separation of Adjacent Regions
  • 4.6 Spatially Restricted Synchronization Among FCOs
  • 4.7 Additional Properties of FCOs
  • 4.8 Stimulus-Locked Scene Segmentation
  • 4.9 Early Labeling of Visual Objects by FCO- or Rate-Coherence?
  • 4.10 Appendix
  • 4.11 References
  • 5 Figure-Ground Segregation and Brightness Perception at Illusory Contours: A Neuronal Model
  • 5.1 Introduction
  • 5.2 Methods
  • 5.3 Results
  • 5.4 Discussion
  • 5.5 Acknowledgment
  • 5.6 References
  • 6 Controlling the Focus of Visual Selective Attention
  • 6.1 Introduction
  • 6.2 A Computational Model of The Dorsal Pathway
  • 6.3 Simulation Results
  • 6.4 Discussion
  • 6.5 References
  • 7 Activity-Gating Attentional Networks
  • 7.1 Introduction
  • 7.2 Activity-Gating Networks
  • 7.3 Results
  • 7.4 Discussion
  • 7.5 References
  • 8 Timing and Counting Precision in the Blowfly Visual System
  • 8.1 Introduction
  • 8.2 Signal, Noise and Information Transmission in a Modulated Poisson Process
  • 8.3 The Early Stages of Fly Vision
  • 8.4 Coding in a Blowfly Motion Sensitive Neuron
  • 8.5 Discussion and Conclusions
  • 8.6 References
  • 9 Paradigms for Computing with Spiking Neurons
  • 9.1 Introduction
  • 9.2 A Formal Computational Model for a Network of Spiking Neurons
  • 9.3 McCulloch-Pitts Neurons versus Spiking Neurons
  • 9.4 Computing with Temporal Patterns
  • 9.5 Computing with a Space-Rate Code
  • 9.6 Analog Computation on Time Series in a Space-Rate Code
  • 9.7 Computing with Firing Rates
  • 9.8 Firing Rates and Temporal Correlations
  • 9.9 Networks of Spiking Neurons for Storing and Retrieving Information
  • 9.10 Computing on Spike Trains
  • 9.11 Conclusions
  • 9.12 References.

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