ICANN '94Proceedings of the International Conferenceon Artificial Neural NetworksSorrento, Italy26-29 May 1994
Volume 1, Parts 1 and 2
Edited byMaria Marinaro and Pietro G. Morasso
Springer-VerlagLondon Berlin Heidelberg New YorkParis Tokyo Hong KongBarcelona Budapest UB/TIB Hannover
111 772 737
Contents, Volume 1
Part 1 • Neurobiology
Why bright Kanizsa squares look closer: consistency ofsegmentations and surfaces in 3-D vision.S. Grossberg 3
Spatial pooling and perceptual framing by synchronizing corticaldynamics.S. Grossberg, A. Grunewald 10
Vertebrate retina: sub-sampling and aliasing effects can explaincolour-opponent and colour constancy phenomena.J.Herault 16
RETINA: a model of visual information processing in the retinalneural network.A. Faure, I. Rybak, A. Golovan, O. Cachard, N.A. Shevtsova,L.N. Podladchikova 22
The influence of the inhomogeneous dendritic field size of theretinal ganglion cells on the fixation.T. Yagi, K. Gouhara, Y. Uchikawa 26
Top-down interference in visual perception.C. Taddei-Ferretti, C. Musio, R.F. Colucci 30
Dynamic vision system: modeling the prey recognition ofcommon toads Bufo bufo.E. Stolte, E. Littmann, H. Ritter 34
Emergence of long range order in maps of orientationpreference.F. Wolf, K. Pawelzik, T. Geisel 38
Oriented ocular dominance bands in the self-organizing featuremap.H.-U. Bauer 42
How to use non-visual information for optic flow processing inmonkey visual cortical area MSTd.M. Lappe, F. Brentmer, K.-P. Hoffmann 46
A learning rule for self-organization of the velocity selectivity ofdirectionally selective cells.K. Miura, K. Kurata, T. Nagano 50
Motion analysis with recurrent neural nets.A. Psarrou,H. Buxton 54
vi Contents
Self-organizing a behaviour-oriented interpretation of objects inactive-vision.Hoehme, D. Heinke, T. Pomierski, R. Moeller 58Hybrid methods for robust irradiance analysis and 3-D shapereconstruction from images.F. Callari, U. Maniscako, P. Storniolo 62A parallel algorithm for simulating color perception.L. Tao, Y. Chen, G. Yao 66Positional competition in the BCS.L. Wieske 70A computational model for texton-based preattentive texturesegmentation.M.N.Shirazi, M. Hida, Y. Nishikawa .?. 74
Hopfield neural network for motion estimation andinterpretation.G. Convertino, M. Brattoli, A. Distante 78
Phase interactions between place cells during movement.J.G. Taylor, L.P. Michalis 82
Self-organization of an equilibrium-point motor controller.V. Sanguineti, P. Morasso 86
Study of a Purkinje unit as a basic oscillator of thecerebellar cortex.P. Chauvet, G.A. Chauvet 90
Compartmental interaction in the granular layer of thecerebellum.L.N.Kalia 94
Modeling biologically relevant temporal patterns.W. Zander, B. Brueckner, T. Behnisch, T. Wesarg 98
A model of the baroreceptor reflex neural network.J.S. Schwaber, LA. Rybak, R.F. Rogers 102
Modelization of vestibulo-ocular reflex (VOR) and motionsickness prediction.L. Zupan,J. Droulez, C. Darlot, P. Denise, A. Maruani 106
Kernel correlations of movements in neural network.N.Ishii 110
Analysis of the golf swing from weight-shift using neuralnetworks.H.S. Yoon, C.S. Bae, B.W. Min 114
Dry electrophysiology: an approach to the internal representationof brain functions through artificial neural networks.S. Usui, S. Nakauchi 118
ANNs and MAMFs: transparency or opacity?L.W. Stark 123
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Contents vii
Collective brain as dynamical system.M.Zak 130
Temporal pattern dependent spatial-distribution of LTP in thehippocampal CA1 area studied by an optical imaging method.M. Tsukada, T. Aihara, M. Mizuno 134
Synchronization-based complex model neurons.G. Hartmann, S. Drue 138Synchronization of integrate-and-fire neurons with delayedinhibitory lateral connections.L.S. Smith, D.E. Cairns, A. Nischwitz 142
Complex patterns of oscillations in a neural network model withactivity-dependent outgrowth.A. van Ooyen, J. van Pelt 146
Learning and the thalamic-NRT-cortex system.J.G. Taylor, F N. Alavi 150
Resetting the periodic activity of Hydra at a fixed phase.C. Taddei-Ferretti, C. Musio, S. Chillemi 154
Integral equations in compartmental model neurodynamics.P.C. Bressloff 158
Hysteresis in a two neuron-network: basic characteristics andphysiological implications.K. Pakdaman, A. van Ooyen, A.R. Houweling, J.-F. Vibert 162
Cooperation within networks of cortical automata basednetworks.L. Boutkhil, F. Joublin, S. Wacquant 166
Anisotropic correlation properties in the spatial structure ofcortical orientation maps.S.P. Sabatini, R. Raffo, G.M. Bisio 170
Part 2 • Mathematical ModelApplication of neural network and fuzzy logic in modelling andcontrol of fermentation processes.N.A. Jalel, B. Zhang, J.R. Leigh 177
Neural networks for the processing of fuzzy sets.G.Bortolan 181
Human sign recognition using fuzzy associative inferencesystem.T. Yamaguchi, T. Sato, H. Ushida, A. Imura 185
Bayesian properties and performances of adaptive fuzzysystems in pattern recognition problems.F. Masulli, F. Casalino, F. Vannucci 189
viii Contents
The representation of human judgement by using fuzzytechniques.A. Cannavacciuolo, G. Capaldo, A. Ventre, A. Volpe, G. Zollo 193
Fuzzy logic versus neural network technique in an identificationproblem.G. Cammarata, S. Cavalieri, A. Fichera 197
Phoneme recognition with hierarchical self organised neuralnetworks and fuzzy systems - A Case Study.N. Kasabov, E. Peev 201
Neuronal network models of the mind./. G. Taylor 205
The consciousness of a neural state machine.I. Aleksander 212
Forward reasoning and Caianiello's nets.£. Burattini, G. Tamburrini 218
An ANN model of anaphora: implications for nativism.S.H.Parfitt 222
The spatter code for encoding concepts at many levels.P. Kanerva 226
Learning in hybrid neural models.A.M. Colla, N. Longo, G. Morgavi, S. Ridella 230
A connectionist model for context effects in the picture-wordinterference task.P. A. Starreveld, J. N. H. Heemskerk 234
Inductive inference with recurrent radial basis functionnetworks.M. Gori, M. Maggini, G. Soda 238
Neural networks as a paradigm for knowledge elicitation.G.P. Fletcher, C. J. Hinde, A.A. West, D.J. Williams 242
Unsupervised detection of driving states with hierarchical selforganizing maps.P. Weierich, M. von Rosenberg 246
Using simulated annealing to train relaxation labeling processes.M. Pelillo, A. Maffione 250
A neural model for the execution of symbolic motor programs.CM. Privitera, P. Morasso 254
Evolution of typed expressions describing artificial nervoussystems.C.Jacob 258
BAR: a connectionist model of bilingual access representations.O. Soler, R. van Hoe 263
Contents ix
An architecture for image understanding by symbol and patternintegration.M. Nishi, K. Ohzeki, N. Sakurai, T. Omori 268
Encoding conceptual graphs by labeling RAAM.M. de Gerlache, A. Sperduti, A. Starita 272Hybrid system for ship detection in radar images.G. Fiorentini, G. Pasquariello, G. Satalino, F. Spilotros 276
Using ART2 and BP co-operatively to classify musical sequences.N. J.L.Griffith .c. 280
Forecasting using constrained neural networks.R. Kane, M. Milgram 284
The evaluations of environmental impact: cooperative systems.A. Pazos, A. Santos del Riego, J. Dorado 288
What generalizations of the self-organizing map make sense?T. Kohonen 292
A novel approach to measure the topology preservation offeature maps.Th. Villmann, R. Der, Th. Martinez 298
Self-Organized learning of 3 dimensions.Cs. Szepesvari, A. Loerincz 302
A model of fast and reversible representational plasticity usingKohonen mapping.M. Andres, O. Schlilter, F. Spengler, H.R. Dinse 306
Multiple self-organizing neural networks with the reducedinput dimension./. Kim, J. Ahn, CS. Kim, H. Hwang, S. Cho 310
Adaptive modulation of receptive fields in self-organizingnetworks.F. Firenze, P. Morasso 314
About the convergence of the generalized Kohonen algorithm.J.C. Fort, G. Pages 318Reordering transitions in self-organized feature maps withshort-range neighbourhood.R. Der, M. Herrmann 322
Speeding-up self-organizing maps: the quick reaction./. Monnerjahn 326Dynamic extensions of self-organizing maps./. Goppert, W. Rosenstiel 330
Feature selection with self-Organizing feature map./. livarinen, K. Valkealahti, A. Visa, O. Simula 334Unification of complementary feature map models.O. Scherf, K. Pawelzik, F.Wolf, T. Geisel 338
x Contents
Considerations of geometrical and fractal dimension of SOM toget better learning results.H. Speckmann, G. Raddatz, W. Rosenstiel 342
On the ordering conditions for self-organising maps.M. Budinich, J.G. Taylor 346
Representation and identification of fault conditions of ananaesthesia system by means of the self-organizing map.M. Vapola, O. Simula, T. Kohonen, P. Merilainen 350
Sensor arrays and self-organizing maps for odour analysis inartificial olfactory systems.F. Davide, C. Di Natale, A. DAmico 354
A self-organising neural network for the travelling salesmanproblem that is competitive with simulated annealing.M. Budinich 358
Learning attractors as a stochastic processD.J.Amit 362Nominal color coding of classified images by Hopfield networks.P. Campadelli, P.Mora , R. Schettini 373
Does terminal attractor backpropagation guarantee globaloptimization?M. Bianchini, M. Gori, M. Maggini 377
Learning and retrieval in attractor neural networks with noiseabove saturation.R. Erichsen Jr., W.R. Theumann 381
A method of teaching a neural network to generate vector fieldsfor a given attractor.N.H.-R. Goerke, R. Eckmiller 385
Recurrent neural networks with delays./. Guignot, P. Gallinari 389
Eman: equivalent mass attraction networkM. H. Erdem, G. Baskomurcu, Y. Ozturk 393
Analysis of an unsupervised indirect feedback network.M.D.Plumbley 397
Hopfield energy of random nets.£. Gelenbe 401
Multiple cueing of an associative net.M. Budinich, B. Graham, D. Willshaw 405
Programmable mixed implementation of the Boltzmann machine.V. Lafargue, E. Belhaire, H. Pujol, I. Berechet, P. Garda 409The influence of response functions in analogue attractor neuralnetworks.N. Brunei, R. Zecchina 413
Contents xi
Improvement of learning in recurrent networks by substitutingthe sigmoid activation function.J.M. Sopena, R. Alquezar 417
Attractor properties of recurrent networks with generalisingboolean nodes.A. de Padua Braga 421
On a class of Hopfield type neural networks for associativememory.B. Beliczynski 425
Storage capacity of associative random neural networks.C.Hubert 429
A generalized bidirectional associative memory with a hiddenorthogonal layer.F. Ibarra-Pico, J.M. Garcia-Chamizo 433
Finding correspondences between smoothly deformablecontours by means of an elastic neural network.F. Labonte, P. Cohen 437
Minimization of number of connections in feedback networks.V. Tereshko 441
An efficient method of pattern storage in the hopfield net.S. Coombes, J.G. Taylor 443
Recursive learning in recurrent neural networks with varyingarchitecture.D. Obradovic 447
Pruning in recurrent neural networks.G. Castellano, A.M. Fanelli, M. Pelillo 451
Making hard problems linearly separable - Incremental radialbasis function approaches.B.Fritzke 455
'Partition of unity' RBF networks are universal functionapproximators./. Hakala, C. Koslowski, R. Eckmiller 459
Optimal local estimation of RBF parameters.S. Marchini, N.A. Borghese 463Acceleration of Gaussian radial basis function networks forfunction-approximation./. Hakala, J. Puzicha, R. Eckmiller 467
Uniqueness of functional representations by Gaussian basisfunction networks.V. Kurkovd, R. Neruda 471
A dynamic mixture of Gaussians neural network for sequenceclassification.M. Ceccarelli, J.T. Hounsou 475
xii Contents
Hierarchical mixtures of experts and the EM algorithm.M.I. Jordan, R.A. Jacobs 479Outline of a linear neural network and applications.E.R. Caianiello, M. Marinaro, S. Rampone, R. Tagliaferri 487
Numerical experiments on the information criteria for layeredfeedforward neural nets. l
K. Hagiwara, S. Usui 493
Quantifying a critical training set size for generalization andoverfitting using teacher neural networks.R. Lange, R. Manner 497
Formal representation of neural networks.R. Freund, F. Tafill 501Training-dependent measurement.Z.Yang 505Genetic algorithms as optimisers for feedforward neuralnetworks.L. Vermeersch, F. Dumortier, G. Vansteenkiste 509
Selecting a critical subset of given examples during learning.B.T.Zhang 517
On the circuit complexity of feedforward neural networks.V. Beiu, J.A. Peperstraete, J. Vandewalle, R. Lauwereins 521
Avoiding local minima by a classical range expansion algorithm.D. Gorse, A. Shepherd, J.G. Taylor 525
Learning time series by neural networks.D.W. Allen, J.G. Taylor 529The error absorption for fitting an under-fitting (skeleton) net.Z. Yang 533
Fast backpropagation using modified sigmoidal functions.F.C.Morabito 537
Input contribution analysis in a double input layered neuralnetwork.Z. Shen, M. Clarke, R.W. Jones 541
A unified approach to derive gradient algorithms for arbitraryneural network structures.F. Beaufays, E. A. Wan 545
Interpretation of BP-trained net outputs.S. Gomez, L. Garrido 549
Fluctuated-threshold effect in multilayered neural network.K. Iwami, N. Matsui, T. Araki 553
On the properties of error functions that affect the speed ofbackpropagation learning.R.J. Gaynier, T. Downs 557
Contents xiii
Neural network optimization for good generalizationperformance.J. Zhao,J. Shawe-Taylor 561
Block-recursive least squares technique for training multilayerperceptrons.R. Parisi, E.D. Di Claudio, G. Orlandi 565
Neural networks for iterative computation of inversefunctions.S. Anoulova 569
Cascade correlation convergence theorem.G.P. Drago, S. Ridella 573
Optimal weight Initialization for neural networks.R.Rojas 577
Neural nets with superlinear VC-dimension.W.Maass 581
A randomised distributed primer for the updating control onanonymous ANNs.A. Calabrese, F.M.G. Franca 585
Catastrophic interference in learning processes by neuralnetworks.E. Pessa, M. P. Penna 589
Systematicity in IH-analysis.D.Lundh 593
Integrating distance measure and inner product neurons.F. Mana, D. Albesano, R. Gemello 597
Teaching by showing in Kendama based on optimizationprinciple.M. Kawato, F. Gandolfo, H. Gomi, Y. Wada 601
From coarse to fine: a novel way to train neural networks.L.-Q. Xu, T. Hall 607
Learning the activation function for the neurons in neuralnetworks.G.P. Fletcher, C J. Hinde 611
Projection learning and graceful degradation.K. Weigl, M. Berthod 615
Learning with zero error in feedforward neural networks.M.L. Lo Cascio, G. Pesamosca 619
Robustness of Hebbian and anti-Hebbian learning.T. Fomin, A. Loerincz 623Computational experiences of new direct methods for theon-line training of MLP-networks with binary outputs.M. Di Martino, S. Fanelli, M. Protasi 627
xiv Contents
Optimising local Hebbian learning: use the 8-rule.J.W.M. van Dam, B.J.A. Krose, F.C A. Groen, 631
Efficient neural net oc-p-evaluators.A.P. Heinz 635
A parallel algorithm for a dynamic eta/alpha estimation inbackpropagation learning.M. Raus, W. Ameling 639
Dynamic pattern selection: effectively training backpropagationneural networks.A. Robel 643
A learning rule which implicitly stores training history inweights.F. Peper, H. Noda 647
A comparison study of unbounded and real-valuedreinforcement associative reward-penalty algorithms.R. Neville, T.J. Stonham 651
To swing up an inverted pendulum using stochastic real-valuedreinforcement learning.A. Standfuss, R. Eckmiller 655
Efficient reinforcement learning strategies for the pole balancingproblem.D. Kontoravdis, A. Likas, A. Stafylopatis 659
Reinforcement learning in Kohonen feature maps.N.R.Ball 663
CMAC manipulator control using a reinforcement learnedtrajectory planner.D.P.W. Graham, G.M.T. D'Eleuterio 667
A fast reinforcement learning paradigm with application toCMAC control systems.D.P.W. Graham, G.M.T. D'Eleuterio 671
Information geometry and the EM algorithm.S. Amari 675
SSM: a statistical stepwise method for weight elimination.M. Cottrell, B. Girard, Y. Girard, M. Mangeas, C, Muller 681
Computing the probability density in connectionist regression.A.N. Srivastava,A.S. Weigend 685
Estimation of conditional densities: a comparison of neuralnetwork approaches.R. Neuneier, F. Hergert, W. Finnoff, D. Ormoneit 689
Regularizing stochastic Pott neural networks by penalizingmutual information.G. Deco, T. Martinetz 693
Contents xv
Least mean squares learning algorithm in self referential linearstochastic models.£. Barucci, L. Landi 697
An approximation network with maximal transinformation.R.W. Brause 701
Extended functionality for probabilistic RAM neurons.D. Gorse, J.G. Taylor, T.G. Clarkson 705Statistical biases in backpropagation learning.C. Thornton 709
An approximation of nonlinear canonical correlation analysis bymultilayer perceptrons.H. Asoh, O. Takechi 713
Information minimization to improve generalizationperformance.R. Kamimura, S. Nakanishi 717
Learning and interpretation of weights in neural networks.C.C.A.M. Gielen 721
Variable selection with optimal cell damage.T. Cibas, F.Fogelman Soulie, P. Gallinari, S. Raudys 727
Comparison of constructive algorithms for neural networks.F.M. Frattale Mascioli, G. Martinelli, G. Lazzaro 731
Task decomposition and correlations in growing artificial Neuralnetworks./. M. Lange, H.-M. Voigt, D. Wolf 735
XNeuroGene: a system for evolving artificial neural networks.C. Jacob, J. Rehder, J. Siemandel, A. Friedmann 739Incremental training strategies.7. Cloete, J. Ludik 743
Modular object-oriented neural network simulators andtopology generalizations.G. Thimm, R. Grau, E. Fiesler 747
Gradient-based adaptation of Network Structure.B. de Vries 751
A connectionist model using multiplexed oscillations andsynchrony to enable dynamic connections.J.-C. Martin 755
Some results on correlation dimension of time series generatedby a network of phase oscillators.R. Borisyuk, A. Casaleggio, Y. Kazanovich, G. Morgavi 759
Towards the application of networks with synchronizedoscillatory dynamics in vision.H.-U. Bauer 763
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xvi Contents
New impulse neuron circuit for oscillatory neural networks.J.-H.Shin 767
Adaptive topologically distributed encoding.M. Eldracher, H. Geiger 771
On-line learning with momentum for nonlinear learning rules.W. Wiegerinck, A. Komoda, T. Heskes 775
Constructive neural network algorithm for approximation ofmultivariable function with compact support.N.Magnitskii 779
A Hebb-like learning rule for cell assemblies formation.F.J. Vico, F. Sandoval, J. Almaraz 881CARVE - a constructive algorithm for real valued examples.S. Young, T. Downs 785
A supervised learning rule for the single spike model.K.Eder 789
Comparative bibliography of ontogenic neural networks.E.Fiesler 793
Controlled growth of cascade correlation nets.L.K. Hansen, M.W. Pedersen 797