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NEURAL NETWORKS: A special issue of Applied Optics
December 1, 1987 (vol. 26, no. 23)
Guest editors: Gail A. Carpenter and Stephen Grossberg
The Applied Optics special issue on neural networks brings together a
selection of research articles concerning both biological models of brain and
behavior and technological models for implementation in government and
industrial applications. Many of the articles analyze problems about pattern
recognition and image processing, notably those classes of problems for which
adaptive, massively parallel, fault-tolerant solutions are needed, and for
which neural networks provide solutions in the form of architectures that will
run in real-time when realized in hardware.
The articles are grouped into several topics: adaptive pattern recognition
models, image processing models, robotics models, optical implementations,
electronic implementations, and opto-electronic implementations. Each type of
neural network model is typically specialized to solve a variety of problems.
Models of back propagation, simulated annealing, competitive learning, adaptive
resonance, and associative map formation are found in a number of articles.
Each of the articles may thus be appreciated on several levels, from the
development of general modeling ideas, through the mathematical and
computational analysis of specialized model types, to the detailed explanation
of biological data or the fabrication of hardware. The table of contents
follows.
Single copies of this special issue are available from the Optical Society
of America, at $18/copy. Orders may be placed by returning the form below, or
by calling (202) 223-8130 (ask for Jeana Macleod).
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NEURAL NETWORKS: A special issue of Applied Optics
December 1, 1987 (vol. 26, no. 23)
Guest editors: Gail A. Carpenter and Stephen Grossberg
TABLE OF CONTENTS
ADAPTIVE PATTERN RECOGNITION MODELS
Teuvo Kohonen. Adaptive, associative, and self-organizing functions in
neural computing
Gail A. Carpenter and Stephen Grossberg. ART 2: Self-organization of
stable category recognition codes for analog input patterns
Jean-Paul Banquet and Stephen Grossberg. Probing cognitive processes
through the structure of event-related potentials during learning: An
experimental and theoretical analysis
Bart Kosko. Adaptive bidirectional associative memories
T.W. Ryan, C.L. Winter, and C.J. Turner. Dynamic control of an artificial
neural system: The Property Inheritance Network
C. Lee Giles and Tom Maxwell. Learning and generalization in high order
neural networks: An overview
Robert Hecht-Nielsen. Counterpropagation networks
Kunihiko Fukushima. A neural network model for selective attention in
visual pattern recognition and associative recall
IMAGE PROCESSING MODELS
Michael H. Brill, Doreen W. Bergeron, and William W. Stoner. Retinal
model with adaptive contrast sensitivity and resolution
Daniel Kersten, Alice J. O'Toole, Margaret E. Sereno, David C. Knill, and
James A. Anderson. Associative learning of scene parameters from images
ROBOTICS MODELS
Jacob Barhen, N. Toomarian, and V. Protopopescu. Optimization of the
computational load of a hypercube supercomputer onboard a mobile robot
Stephen Grossberg and Daniel S. Levine. Neural dynamics of attentionally
modulated Pavlovian conditioning: Blocking, inter-stimulus interval, and
secondary reinforcement
OPTICAL IMPLEMENTATIONS
Dana Z. Anderson and Diana M. Lininger. Dynamic optical interconnects:
Volume holograms and optical two-port operators
Arthur D. Fisher, W.L. Lippincott, and John N. Lee. Optical implementations
of associative networks with versatile adaptive learning capabilities
Clark C. Guest and Robert Te Kolste. Designs and devices for optical
bidirectional associative memories
Kelvin Wagner and Demetri Psaltis. Multilayer optical learning networks
ELECTRONIC IMPLEMENTATIONS
Larry D. Jackel, Hans P. Graf, and R.E. Howard. Electronic neural-network
chips
Larry D. Jackel, R.E. Howard, John S. Denker, W. Hubbard, and S.A. Solla.
Building a hierarchy with neural networks: An example - image vector
quantization
A.P. Thakoor, A. Moopenn, John Lambe, and Satish K. Khanna. Electronic
hardware implementations of neural networks
OPTO-ELECTRONIC IMPLEMENTATIONS
Nabil H. Farhat. Opto-electronic analogs of self-programming neural nets:
Architectures and methodologies for implementing fast stochastic learning
by simulated annealing
Yuri Owechko. Opto-electronic resonator neural networks
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