Models of Neural Networks IV: Early Vision and Attention

423 Pages·2002·19.619 MB·English

by  J. A. Hertz (auth.), J. Leo van Hemmen, Jack D. Cowan, Eytan Domany (eds.)

Physics of Neural Networks
Series Editors:
J.D. Cowan E.Domany J.L. van Hemmen
Advisory Board:
l A. Hertz
U. Hopfield
Springer Science+Business Media, LLC
M. Kawato
TJ. Sejnowski
D. Sheninglon
Physics of Neural Networks
Models ofN eural Networks
E. Domany, J.L. van Hemmen, K. Schulten (Eds.)
Models ofN eural Networks II: Temporal Aspects of Coding and Information
Processing in Biological Systems
E. Domany, IL. van Hemmen, K. Schulten (Eds.)
Models ofN eural Networks III: Association, Generalization, and Representation
E. Domany, IL. van Hemmen, K. Schulten (Eds.)
Models ofN eural Networks IV: Early Vision and Attention
IL. van Hemmen, ID. Cowan, E. Domany (Eds.)
Neural Networks: An Introduction
B. Muller, J. Reinhart
J. Leo van Hemmen Jack D. Cowan
Eytan Domany (Eds.)
Models of
Neural Networks IV
Early Vision and Attention
With 139 Figures
, Springer
Series and Volume Editors:
J. Leo van Hemmen Eytan Domany
Institut fUr Theoretische Physik Depanment of Electronics
Technische UniversiUiI MUnchen Weizmann Institute of Science
D·85747 Garehing bei MUnchen 76100 Rehovot
Gennany Israel
[email protected] [email protected]
Jack D. Cowan
Oepanment of Mathematics
University of Chicago
Chicago, IL 60637
USA
[email protected]
Library of Congress Cataloging-in.Publication Data
Models of neural networks IV I 1.L. van Hemmen, 1.D. Cowan, E. Domany,
editors.
p. cm. - (Physics of neural networks)
Includes bibliographieal rd erenees and index.
1. Neural networks (Computer sdenee) - Mathematieal model!.
1. Cowan, lO. (1ack D.). II. Domany, E. (Eytan).
m.
Hemmen, I.L van (Jan Leonard). IV. Series.
QA76.87.M59 2001
006.3-de20 95-14288
Prinled on acid-free paper.
o
2002 Springer Science+Business Media New YOl:K
Originally publishcd by Springer-Verlag New Yorl<, Inc in 2002
Softcover reprint of the hardcover lst edition 2002
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ISBN 978-1-4419-2875-7 ISBN 978-0-387-21703-1 (eBook) SPIN 10774300
DOI 10.1007/978-0-387-21703-1
Preface
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
volume of Models of Neural Networks devoted to Early Vision and Atten
tion that you are holding in your hands. Early vision comprises the first
stages of visual information processing. It is as such a scientific challenge
whose clarification calls for a penetrating review. Here you see the result.
The Heraeus Foundation (Hanau) is to be thanked for its support during
the initial phase of this project.
John Hertz, who has extensive experience in both computational and ex
perimental neuroscience, provides in "Neurons, Networks, and Cognition"
a theoretical introduction to neural modeling. John Van Opstal explains in
"The Gaze Control System" how the eye's gaze control is performed and
presents a novel theoretical description incorporating recent experimental
results. We then turn to the relay stations thereafter, the lateral genicu
late nucleus (LGN) and the primary visual cortex. Their anatomy, phys
iology, functional relations, and ensuing response properties are carefully
analyzed by Klaus Funke et al. in "Integrating Anatomy and Physiology
of the Primary Visual Pathway: From LGN to Cortex", one of the most
comprehensive reviews that is available at the moment.
How do we discern patterns? That is to say, how do we perform scene
segmentation? It has been shown that this process is partially preattentive
and, so to speak, done on the spot in the primary visual cortex. Reinhard
Eckhorn explains the underlying "Neural Principles of Preattentive Scene
Segmentation" while Esther Peterhans et al. analyze a neuronal model of
"Figure-Ground Segregation and Brightness Perception at Illusory Con
tours" .
Scene segmentation can also be performed by a feedback process that is
called 'attention'. A glance suffices to convince every beholder that the eye
catches megabytes of data. Through attention we reduce this data flood by
singling out specific objects. Ernst Niebur et al. indicate how this can be
done by "Controlling the Focus of Visual Selective Attention" while Julian
Eggert and Leo van Hemmen elucidate the feedback mechanism proper in
"Activity-Gating Attentional Networks".
Ever tried to smash a busy buzzing fly against the wall? Then you know
how good it is in avoiding you. That is to say, you realize that also insects
such as flies may perform highly efficient visual-information processing. In
vi Preface
their essay "Timing and Counting Precision in the Blowfly Visual System"
Rob de Ruyter van Steveninck and Bill Bialek explain how this is done in
early vision and show what key role is played by spikes. Finally, Wolfgang
Maass approaches "Paradigms for Computing with Spiking Neurons" from
the point of view of a computer scientist who is concerned with biological
information processing. Enjoy!
The Editors
Contents
Preface v
Contributors xiii
1 Neurons, Networks, and Cognition: An Introduction to
Neural Modeling 1
J. A. Hertz
1.1 Introduction............... 1
1.1.1 A few neuroanatomical facts . 2
1.1.2 A few neurophysiological facts 4
1.2 Neurons . . . . . . . . . . . . . . 4
1.2.1 Hodgkin-Huxley neurons. 5
1.2.2 Integrate-and-fire neurons 10
1.2.3 Binary (Ising) neurons . . 12
1.3 Local Cortical Network Dynamics. 13
1.3.1 Mean field theory. . . . . . 14
1.3.2 Simulations with spiking neurons 18
1.4 Collective Computation: Associative Memory 22
1.4.1 Hopfield model . . . . . . . . . . . . . 22
1.4.2 Sparse-pattern model ......... 27
1.4.3 Memory with time-dependent patterns . 30
1.5 Concluding Remarks 43
1.6 Acknowledgments. 44
1. 7 References...... 44
2 The Gaze Control System 47
John van Opstal
2.1 Introduction......................... 47
2.2 The Gaze Control System in One and Two Dimensions. 48
2.3 New Aspects for Eye Rotations in 3D ... 56
2.4 Mathematics of 3D Rotational Kinematics. 59
2.4.1 Finite rotations. 59
2.4.2 Quaternions........ 61
2.4.3 Rotation vectors. . . . . . 64
2.5 Donders' Law and Listing's Law 64
2.5.1 Listing's law for head-fixed saccades 68
2.5.2 Spontaneous violations of Listing's law. 70
2.5.3 Parametrization of 3D saccades . . . . . 71
viii Contents
2.5.4 3D Models: The saccade programmer. 74
2.5.5 3D Models: The saccade generator 77
2.6 Head-free Saccadic Gaze Shifts in 3D . 81
2.7 Conclusion 84
2.8 References ............... . 88
3 Integrating Anatomy and Physiology of the Primary Visual
Pathway: From LGN to Cortex 97
K. Funke, Z. F. Kisvarday, M. Volgushev, and F. Worgotter
3.1 Introduction...................... 97
3.1.1 The primary visual pathway: An overview. 98
3.1.2 Definitions: The receptive field . . . . 99
3.2 The LGN . . . . . . . . . . . . . . . . . . . . 100
3.2.1 General view and functional anatomy 100
3.2.2 Physiological properties of the LGN 104
3.2.3 Extra-retinal control of LGN function 116
3.3 Models of the LGN . . . . . . . . . . . . . . . 123
3.3.1 Basic membrane model of an LGN cell. 123
3.3.2 Models of the hyperpolarized thalamic state . 124
3.3.3 Models of the depolarized thalamic state. . . 128
3.4 The Visual Cortex . . . . . . . . . . . . . . . . . . . 131
3.4.1 Anatomical organization of the primary visual cortex 131
3.4.2 Basic response properties of visual cortical neurons. 142
3.4.3 Mechanisms of selectivity of cortical responses:
Orientation selectivity . . . . . . . . 149
3.4.4 Representations in the visual cortex . . . . . 155
3.5 Models of the Visual Cortex . . . . . . . . . . . . . . 160
3.5.1 Models of the temporal structure of cortical
responses ............... . 161
3.5.2 Models of cortical cell characteristics . 163
3.5.3 Models of cortical maps 167
3.6 References ................... . 171
4 Neural Principles of Preattentive Scene Segmentation:
Hints from Cortical Recordings, Related Models, and
Perception 183
Reinhard Eckhorn
4.1 Introduction........................... 184
4.1.1 Preattentive scene segmentation is a prerequisite for
object recognition ................ 184
4.1.2 Principles of neural coding beyond the classical
receptive field . . . . . . . . . . . . . . . . . . . 185
4.1.3 Coupling beyond the classical receptive fields defines
association fields . . . . . . . . . . . . . . . . . . .. 185
Contents ix
4.2 Properties of Synchronized Fast Cortical Oscillations
(FCOs) ......................... 186
4.2.1 Sustained activation is required for the generation
of FCOs . . . . . . . . . . . . . . . . . . . . . . 186
4.2.2 Single neurons are differently involved in FCOs 186
4.3 Coding Contour Continuity . . . . . . . . . 187
4.4 Coding Region Continuity . . . . . . . . . . . . ..... 189
4.5 Coding the Separation of Adjacent Regions . . . . : 191
4.6 Spatially Restricted Synchronization Among FCOs . 192
4.6.1 Average zero-phase correlation within a cortical area 192
4.6.2 Average zero-phase correlation among two visual
cortex areas. . . . . . . . . . . . . . . . . . . . . . . 192
4.6.3 Why declines FCO coherence with cortical distance
and what are possible consequences for coding
object continuity? ............. 195
4.6.4 Scene segmentation at consecutive levels
of processing . . . . . . . . . . . . . . 197
4.7 Additional Properties of FCOs . . . . . . . . 199
4.7.1 Frequency and amplitude of FCOs are
highly variable . . . . . . . . . . . . . . . . . . . 199
4.7.2 Visual stimulation influences average oscillation
frequency of FCOs . . . . . . . . . 200
4.7.3 FCOs and temporal segmentation ...... 201
4.8 Stimulus-Locked Scene Segmentation . . . . . . . . . 202
4.8.1 Suppression of FCOs by fast stimulus-locked
activations ................... 203
4.8.2 Time courses of stimulus-locked and stimulus-
induced FCO-activity . . . . . . . . . 204
4.9 Early Labeling of Visual Objects by FCO- or
Rate-Coherence? 207
4.10 Appendix ................... . 208
4.11 References . . . . . . . . . . . . . . . . . . . . 210
5 Figure-Ground Segregation and Brightness Perception
at Illusory Contours: A Neuronal Model 217
E. Peterhans, R. van der Zwan, B. Heider, and F. Heitger
5.1 Introduction. 217
5.2 Methods......... 220
5.3 Results.......... 220
5.3.1 Neurophysiology 220
5.3.2 Computational model 224
5.4 Discussion........... 235
5.4.1 Occlusion cues . . . . 238
5.4.2 Occluding contours and surfaces 239
5.5 Acknowledgment 240
5.6 References................. 240
x Contents
6 Controlling the Focus of Visual Selective Attention 247
Ernst Niebur, Laurent ltti, and Christo! Koch
6.1 Introduction..................... 247
6.2 A Computational Model of The Dorsal Pathway 248
6.2.1 Model Assumptions 248
6.2.2 General architecture 249
6.2.3 Input features. . . 250
6.2.4 The saliency map . 252
6.3 Simulation Results . . . 257
6.3.1 Synthetic stimuli 257
6.3.2 Natural images . 258
6.4 Discussion........ 260
6.4.1 Psychophysical and physiological basis of the model 260
6.4.2 Limitations of the model. . . 265
6.4.3 Relationship to other models 266
6.4.4 Predictions 268
6.5 References............... 269
7 Activity-Gating Attentional Networks 277
J. Eggert and J. L. van Hemmen
7.1 Introduction................ 277
7.1.1 Different types of attention . . . 277
7.1.2 Why attentional processing at all? 278
7.1.3 Spotlight models . . . . . . . . . 280
7.1.4 The discussion forum metaphor. . 281
7.2 Activity-Gating Networks . . . . . . . . . 283
7.2.1 Working hypotheses about the coding of information 283
7.2.2 Implementation of neuronal ensembles . . . . . . 285
7.2.3 Computational units. . . . . . . . . . . . . . . . 288
7.2.4 Working hypotheses about the network function 290
7.2.5 Network architecture: Complementary
processing streams . . . . . . 294
7.2.6 Overall network organization 296
7.3 Results................. 298
7.3.1 Biased competition. . . . . . 298
7.3.2 Contour integration by laterally
transmitted expectation . . 301
7.3.3 Different types of attention . . . 301
7.4 Discussion................. 305
7.4.1 Microarchitecture and concurrent processing streams 305
7.4.2 Biased competition . . . . . . . . . 306
7.4.3 Origin of the attentional signal . . . . . . 306
7.4.4 Saliency and the focus of attention . . . . 307
7.4.5 Predictions of the model and conclusions 307
7.5 References...................... 308