ICANN ’93: Proceedings of the International Conference on Artificial Neural Networks Amsterdam, The Netherlands 13–16 September 1993

1116 Pages·1993·73.87 MB·English

by  Ad Aertsen (auth.)

ICANN '93
ICANN '93
Proceedings of the International Conference
on Artificial Neural Networks
Amsterdam, The Netherlands
13-16 September 1993
Edited by
Stan Gielen and Bert Kappen
Springer-Verlag
London Berlin Heidelberg New York
Paris Tokyo Hong Kong
Barcelona Budapest
Stan Gielen and Bert Kappen
Dutch Foundation for Neural Networks
University of Nijmegen
Geert Grooteplein 21
6525 EZ Nijmegen
The Netherlands
ISBN 978-3-540-19839-0 ISBN 978-1-4471-2063-6 (eBook)
DOl 10.10071 978-1-4471-2063-6
British Library Cataloguing in Publication Data
A catalogue record for this book is available from the British Library
Library of Congress Cataloging-in-Publication Data
International Conference on Artificial Neural Networks (3rd: 1993: Amsterdam,
Netherlands)
ICANN '93: International Conference on Artificial Neural Networks,
Amsterdam, The Netherlands, 13-16 September 19931 edited by Stan Gielen and
Bert Kappen.
p. em.
Includes bibliographical references and index.
1. Neural networks (Computer science) -Congresses. 1. Gielen, Stan,
1952- . II. Kappen, Bert, 1958-- . III. Title.
QA76.87.I57 1993 93-5522
006.3-dc20 CIP
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Preface
This book contains the proceedings of the International Confer
ence on Artificial Neural Networks which was held between
September 13 and 16 in Amsterdam. It is the third in a series
which started two years ago in Helsinki and which last year took
place in Brighton. Thanks to the European Neural Network
Society, ICANN has emerged as the leading conference on
neural networks in Europe.
Neural networks is a field of research which has enjoyed a
rapid expansion and great popularity in both the academic
and industrial research communities. The field is motivated by
the commonly held belief that applications in the fields of
artificial intelligence and robotics will benefit from a good
understanding of the neural information processing properties
that underlie human intelligence. Essential aspects of neural
information processing are highly parallel execution of com
putation, integration of memory and process, and robustness
against fluctuations. It is believed that intelligent skills, such
as perception, motion and cognition, can be easier realized in
neuro-computers than in a conventional computing paradigm.
This requires active research in neurobiology to extract com
putational principles from experimental neurobiological find
ings, in physics and mathematics to study the relation
between architecture and function in neural networks, and in
cognitive science to study higher brain functions, such as
language and reasoning.
Neural networks technology has already lead to practical
methods that solve real problems in a wide area of industrial
applications. The clusters on robotics and applications contain
sessions on various sub-topics in these fields.
These proceedings contain an up-to-date overview of research
and applications, at universities as well as in industry. The large
number of contributions have passed through a rigorous process
of reviewing. Our program committee has been invaluable in
this process.
We would like to thank all organizers and volunteers for their
invaluable help that has made the conference possible. In
vi Preface
particular, thanks are due to the Dutch Foundation for
Neural Networks, the European Commission, Stichting Infor
matica Onderzoek Nederland, Shell Research and Siemens
Nederland for financial support.
Nijmegen, 1993 Stan Gielen
Bert Kappen
Contents
PLENARY CONTRIBUTIONS
A. Aertsen
Dynamic coupling in cortical neural networks ............................. 3
G. E. Hinton and D. van Camp
Keeping neural networks simple .............................................. 11
PRINCIPLES FROM NEUROBIOLOGY
Memory and selforganization---:-oral contributions
A. Treves and E. T. Rolls (invited paper)
The a';1to~ssociative hypothesis places constraints on hippocampal
organtzation .......................................................................... 21
M. Nakao, Y. Mizutani, K. Watanabe and M. Yamamoto
Metastability of network attractor and dream sleep ...................... 27
S. Wacquant, F. Joublin, F. Spengler, B. Godde, H. R. Dinse
Somatosensory cortical maps: reorganization following post-
ontogenetic plasticity-experiments and theory .......................... 31
D. J. Amit and N. Brunei
Adequate input for learning in attractor neural networks.............. 37
Memory and seiforganization-poster contributions
B. Bruckner and W. Zander
Neurobiological modelling and structured neural networks........... 43
H. Ikeno and S. Usui
Model analysis of associative learning in the photoreceptor of
marine mollusc, Hermissenda Crassicomis .................................... 47
J. L. Velay, ,. C. Gilhodes, B. Ans and Y. Coiton
A neural network model for motor shapes learning and
programming ........................................................................ 51
A. Murciano and ,. Zamora
Learning through adaptive value: a model working in a variable
environment ......................................................................... 55
E. Lebert and R. H. Pha!
Improving categorization with CALM maps ............................... 59
viii Contents
D. Heinke and H.-M. Gross
A simple self-organizing neural network architecture for selective
visual attention...................................................................... 63
R. Moeller and H.-M. Gross
Detection of coincidences and generation of hypotheses--a
proposal for an elementary cortical function ............................... 67
F. H. Guenther
DIVA: a self-organizing neural network model for motor
equivalent speech production .................................................. 71
M. M. van Hulle
Adaptive non-uniform AID conversion achieved with an
unsupervised learning rule maximizing information-theoretic
entropy ................................................................................ 75
G. Tambouratzis and T. J. Stonham
Optimal topology-preservation using self-organising logical neural
networks .............................................................................. 76
M. H. Spigt, D. S. Bree and M. Nielen
Incorporation of neurobiological aspects of Aplysia's associative
conditioning in neural networks for on-line pattern detection........ 80
F. A. Monaco
Description on the use of the autogenerative nodal memory model
(ANM) as controlling element of an autonomously responsive
system ................................................................................. 81
S. J. Mrchev
Human memory-neurocomputer (MeNeCo project): structure for
reverbation of the information in N-peaked nets (in STMemory) .... 82
Visuo-motor interaction-oral contributions
J. van Opstal and B. Kappen (invited paper)
Neural representation of saccadic eye movements in monkey
superior colliculus .................................................................. 84
D. Bullock, D. Greve, S. Grossberg and F. H. Guenther
A self-organizing neural network for learning a body-centered
invariant representation of 3-D target position ............................ 90
K. Kopecz, C. Engels and G. Schaner
Dynamic field approach to target selection in gaze control ............ 96
J. F. M. van Brederode and W. J. Spain
Differences in synaptic input and excitability between superficial
and deep pyramidal cells in the cat sensorimotor cortex ............... 102
Visuo-motor interaction-poster contributions
H.-M. R. Arnoldi
An adaptive sensory fusion approach for the superior colliculus .... 107
R. Hosaka and T. Nagano
A neural network model for spatial information representation ..... 111
Contents ix
P. Morasso, V. Sanguineti and T. Tsuji
A dynamical model for the generation of curved trajectories ......... 115
L. N. Kalia
Functional organisation in the cerebellum .................................. 119
D. G. Ruegg, L. Studer and J.-P. Gabriel
Activation and contraction of a muscle ...................................... 120
The visual system-oraI contributions
D. Sporns, G. Tononi and G. M. Edelman (invited paper)
Correlated neuronal activity and behaviour ................................ 125
F. Wolf, K. Pawelzik, T. Geisel, D.-S. Kim and T. Bonhoeffer
Map structure from pinwheel position....................................... 131
H.-U. Bauer, K. Pawelzik and T. Geisel
Emergence of transient oscillations in an ensemble of neurons ...... 136
T. Pomierski, H.-M. Gross and D. Wendt
A distributed multicolumnar system for primary cortical analysis of
real-world scenes ................................................................... 142
The visual system-poster contributions
A. J. Noest
Singularities in cortical orientation and direction maps: vortices,
strings and bubbles ................................................................ 149
P. Grandguillaume
A new model for spatial frequency and orientation tuning in the
visual cortex based on delayed inputs from the retina .................. 153
E. Nelle and F. Worgotter
Cascaded intrac ortical inhibition: modeling connection schemes on
a large scale simulator . .. .. ... .. ...... ...... ........ .. ... .. .. .. .. .... .. ...... .. ... 157
K. Pawelzik, J. Deppisch and T. Geisel
Hidden assembly dynamics and correlated neuronal responses ..... 161
G. Bugmann and J. G. Taylor
A model for latencies in the visual system.................................. 165
F. J. Diaz Pernas and J. LOpez Coronado
A neur~! architecture for textured color image segmentation and
recognition ........................................................................... 169
Dynamics of single neurons-oraI contributions
L. F. Abbott, G. LeMasson, M. Siegel and E. Marder (invited paper)
Activity-dependent modification of intrinsic neuronal properties ... 171
A. van Doyen and J. van Pelt
Implications of and activity-dependent neurite outgrowth for
developing neural networks .................................................... 177
C. Fyfe
PCA properties of intemeurons ................................................ 183
x Contents
H. R. Dinse, C. E. Schreiner, F. Spengler, B. Godde and B. Hartfiel
Temporal distributed processing-TDP: a time-based processing
scheme accounts for time dependent receptive fields and
representational maps .................................... ,....................... 189
Dynamics of single neurons-poster contributions
V. L6pez, J. A. Sigiienza, J. R. Dorronsoro and S. Carrillo-Menendez
Stochastic specificity in neural interaction .................................. 196
R. Lahoz-Beltra, A. Murciano, J. Zamora, F. Vico, J. M. Jerez,
S. R. Hameroff and J. E. Dayhoff
A comput~r simul,atio~ mO,del of backwards feedback across
synapse via arachidOnIC aCid .................................................... 200
G. Vaucher
Study of a self-learning artificial neuron model .......................... , 204
N. Katayama, M. Nakao, Y. Mizutani and M. Yamamoto
Simulation study on calcium-activated dynamics of compartment
dendrite model ..... , .................................. " .... , ......... , ......... "., 205
A. J. Klaassen and J. Hoekstra
On the adaptive capabilities of pulse-coded cable neurons ............ 206
J. Hoekstra
A local approximation of the cable equation for implementing a
local interaction model ............ , ................. , ... , .... , ...... ,.,........... 207
Y. Kamiyama, T. Suzuki, H, Ishii and S. Usui
Effect of glutamate uptake on the response dynamiCS of the retinal
horizontal cell ............................................................... , . . . . . . . 208
ROBOTICS
Robot vision-oral contributions
F. C. A. Groen, B. J. A. Krose and A. J. Noest (invited paper)
Neural networks for robot eye-hand coordination ....................... 211
P. Koistinen
Unsupervised formation of feature detectors using residual inputs. 219
M. Proesmans, E. Pauwels, L. J. Van Gool, T. Moons and
A. Oosterlinck
Geometry-driven diffusion: coupled diffusion maps as a model for
excitatory and inhibitory behaviour in vision .............................. 224
H. Keuchel, E. von Puttkamer and U. R. Zimmer
SPIN: learning and forgetting surface classifications with dynamic
neural networks . , ......... , .......... , . , , , ... , , .. , ............ , .. , ... , ... , .... ' . , . , 230
Robot vision-poster contributions
T. M. H. Dijkstra, E. Argante and C. C. A. M. Gielen
Motion parallax from catastrophies in scale-space ........................ 237
Contents xi
S. T. Toborg
Stability and convergence control in cooperative integration
networks .............................................................................. 241
H. Neumann and H. S. Stiehl
Towards a neural architecture for unified visual contrast and
brightness perception ............................................................. 245
E. Chen-Kuo Tsao and H.-Y. Liao
Fuzzy Kohonen clustering networks for reducing search space in
3-D object recognition ............................................................. 249
L. Raffo, S. P. Sabatini, G. Indiveri, D. D. Caviglia and G. M. Bisio
An active resistor mesh embedding cortical visual processing ........ 250
J. L. Contreras-Vidal and M. Aguilar
A fast BCSIFCS algorithm for image segmentation....................... 251
Robot control-oral contributions
P. Morasso, V. Sanguineti and 1'. Tsuji (invited paper)
Neural architecture for robot planning ....................................... 256
J. Heikkonen, P. Koikkalainen and E. Oja
From ~itu~tions to actions: motion behavior learning by self-
orgaruzation .......................................................................... 262
T. Wengerek and H. Ritter
Application of Q-Iearning in robot grasping tasks ........................ 268
M. Jansen, J. R. Beerhold and R. Eckmiller
I/O-stability for robot control with a global neural net inverse
model in the feedback loop ...................................................... 274
Robot control-poster contributions
J. M. Vleugels, J. N. Kok and M. H. Overmars
A self-organizing neural network for robot motion planning ......... 281
D. Cliff, I. Harvey and P. Husbands
Evolved recurrent dynamical networks use noise ........................ . 285
G. Fahner and R. Eckmiller
The Bellmann Mapping Machine for nonlinear approximation in
control policy space ................................................................ 289
J. N. H. Heemskerk and P. T. W. Hudson
A real-time robot demonstration controlled by the BSP400
neurocomputer...................................................................... 293
J. R. Beerhold, M. Jansen and R. Eckmiller
First results on stable adaptive robot control with RBF networks .... 297
J. I. Arciniegas, K. J. Cios and A. H. Eltimsahy
Fuzzy inference, radial basis functions, and control of flexible
robotic manipulators ........................... ,..... ..... .... ...... . .. ... ..... ... 301
N. H.-R. Goerke, C. M. Mullender and R. Eckmiller
A recurrent trajectory storage network with parceling of the
workspace .................................................................... ,....... 305