Table of Contents

• Incorporating knowledge in multi-layer networks: the example of protein secondary structure predictionC. Mejía, Françoise Fogelman-Soulié. 3-13 [doi]
• Product Units with Trainable Exponents and Multi-Layer NetworksRichard Durbin, David E. Rumelhart. 15-26 [doi]
• Recurrent Backpropagation and Hopfield NetworksLuis B. Almeida, João P. Neto. 27-36 [doi]
• Optimization of the number of hidden cells in a multilayer perceptron. Validation in the linear caseFrançois Vallet. 37-40 [doi]
• Single-layer learning revisited: a stepwise procedure for building and training a neural networkStefan Knerr, Léon Personnaz, Gérard Dreyfus. 41-50 [doi]
• Synchronous Boltzmann Machines and Gibbs Fields: Learning AlgorithmsRobert Azencott. 51-63 [doi]
• Fast Computation of Kohonen Self-OrganizationJouko Lampinen, Erkki Oja. 65-74 [doi]
• Learning algorithms in neural networks: recent resultsMarc Mézard. 75-79 [doi]
• Statistical approach to the Jutten-Hérault algorithmPierre Comon. 81-88 [doi]
• The N Programming LanguageGaël de La Croix Vaubois, Catherine Moulinoux, Benoît Derot. 89-92 [doi]
• Neural Networks DynamicsEric Goles. 93-102 [doi]
• Dynamical Analysis of Classifier SystemsMario Compiani, Daniele Montanari, Roberto Serra, Paolo Simonini. 103-107 [doi]
• Neuro-Computing Aspects in Motor Planning & ControlPietro Morasso, Gianni Vercelli, Renato Zaccaria. 109-115 [doi]
• Neural Networks and Symbolic A.IMatthew K. O. Lee. 117-120 [doi]
• Integrated artificial neural networks: components for higher level architectures with new propertiesMichel Weinfeld. 123-130 [doi]
• Basic VLSI Circuits for Neural NetworksKarl Goser. 131-140 [doi]
• An Analog VLSI Architecture for Large Neural NetworksMichel Verleysen, Paul G. A. Jespers. 141-144 [doi]
• Analog implementation of a permanent unsupervised learning algorithmChristian Jutten, Jeanny Hérault. 145-152 [doi]
• An Analog Cell for VLSI Implementation of Neural NetworksEros Pasero, Leonardo M. Reyneri. 153-156 [doi]
• Use of Pulse Rate and Width Modulations in a Mixed Analog/digital Cell for Artificial Neural SystemsDante Del Corso, Francesco Gregoretti, Leonardo M. Reyneri. 157-160 [doi]
• Parallel Implementation of a Multi-Layer PerceptronF. Baiardi, R. Mussardo, R. Serra, G. Valastro. 161-166 [doi]
• A monolithic processor array for stochastic relaxation using optical random number generationPatrick Garda, K. Madani, Francis Devos, Pierre Chavel, Philippe Lalanne, Jean Taboury. 167-172 [doi]
• Dedicated neural network A retina for edge detectionD. Collobert, S. Salaün, P. Guyader, J. Guerin, B. Vinouze. 173-179 [doi]
• Neural Network Applications in the Edinburgh Concurrent Supercomputer ProjectM. G. Norman, Nicholas J. Radcliffe, Gareth D. Richards, Frank J. Smieja, D. J. Wallace, J. F. Collins, S. J. Hayward, B. M. Forrest. 181-193 [doi]
• The Semi-Parallel Architectures of Neuro-ComputersPierre Peretto, Robert Van Zurk, André Mougin, Christian Gamrat. 195-204 [doi]
• Speech coding with multilayer networksYoshua Bengio, Régis Cardin, Piero Cosi, Renato de Mori, Ettore Merlo. 207-216 [doi]
• Statistical Inference in Multilayer Perceptrons and Hidden Markov Models with Applications in Continuous Speech RecognitionHervé Bourlard, Nelson Morgan, Christian Wellekens. 217-226 [doi]
• Probabilistic Interpretation of Feedforward Classification Network Outputs, with Relationships to Statistical Pattern RecognitionJohn S. Bridle. 227-236 [doi]
• Data Compression Using Multilayer PerceptronsPascal Blanchet. 237-240 [doi]
• Guided Propagation: current state of theory and applicationsDominique G. Béroule. 241-260 [doi]
• Speaker adaptation using multi-layer feed-forward automata and canonical correlation analysisK. Choukri, Gérard Chollet, Claude Montacié. 261-264 [doi]
• Analysis of linear predictive data as speech and of ARMA processes by a class of single-layer connectionist modelsFrank Fallside. 265-283 [doi]
• High Level Speech Processing by Competitive Neural Networks From Psychology to SimulationThierry Michaux, Pedro L. Galindo, Susana González. 285-296 [doi]
• Connected Word Recognition Using Neural NetworksUmit Dagitan, Nese Yalabik. 297-300 [doi]
• Handwritten Digit Recognition: Applications of Neural Net Chips and Automatic LearningY. Le Cun, Larry D. Jackel, Bernhard E. Boser, John S. Denker, Hans Peter Graf, Isabelle Guyon, Donnie Henderson, Richard E. Howard, Wayne E. Hubbard. 303-318 [doi]
• A method to de-alias the scatterometer wind field: a real world applicationFouad Badran, Sylvie Thiria, Michel Crépon. 319-324 [doi]
• Detection of microcalcifications in mammographic imagesCatherine Bourrely, Serge Muller. 325-328 [doi]
• What is a feature, that it may define a character, and a character, that it may be defined by a feature ?Ethem Alpaydin. 329-332 [doi]
• A Study of Image Compression with BackpropagationMathilde Mougeot, Robert Azencott, Bernard Angéniol. 333-336 [doi]
• Distortion Invariant Image Recognition by Madaline and Back-Propagation Learning Multi-NetworksV. Cruz, G. Cristobal, T. Michaux, S. Barquin. 337-343 [doi]
• An Algorithm for Optical FlowEnrico De Micheli, G. Radonich, Sergio Uras, Vincent Torre. 345-356 [doi]
• Multicellular Processing Units for Neural Networks: Model of Columns in the Cerebral CortexYves Burnod. 359-368 [doi]
• A Potentially Powerful Connectionist Unit: The Cortical ColumnFrédéric Guyot, Frédéric Alexandre, Jean-Paul Haton, Yves Burnod. 369-377 [doi]
• Complex information processing in real neuronesJørn Hounsgaard, Jens Midtgaard. 379-388 [doi]
• Formal Approach and Neural Network Simulation of the Co-ordination between Posture and MovementMichel Dufossé. 389-394 [doi]
• Cheapmonkey: Comparing an ANN and the Primate Brain on a Simple Perceptual Task: Orientation DiscriminationGuy A. Orban, Mark Devos, Rufin Vogels. 395-404 [doi]