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