Volume 6, Number 1, 1993

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  Stephen Grossberg, John G. Taylor: The fifth anniversary of neural networks. 1

• 
  Kurt Hornik, Maxwell B. Stinchcombe, Halbert White: Letters to the editor. 3
• 
  Vladik Kreinovich: Letters to the editor. 3-4
• 
  Terry Bossomaier, Natalina Isidoro, Adrian Loeff: Errors from grid approximation of IFS codes. 5-6

• 
  Marc M. Van Hulle, Tom Tollenaere: A modular artificial neural network for texture processing. 7-32
• 
  Kunihiko Fukushima, Taro Imagawa: Recognition and segmentation of connected characters with selective attention. 33-41
• 
  Stephen Grossberg, Frank H. Guenther, Daniel Bullock, Douglas N. Greve: Neural representations for sensory-motor control, II: Learning a head-centered visuomotor representation of 3-D target position. 43-67
• 
  Jeffrey Hoffman, Josef Skrzypek, Jacques J. Vidal: Cluster network for recognition of handwritten, cursive script characters. 69-78
• 
  Andrew H. Gee, Sreeram V. B. Aiyer, Richard W. Prager: An analytical framework for optimizing neural networks. 79-97
• 
  Sam-Kit Sin, Rui J. P. de Figueiredo: Efficient learning procedures for optimal interpolative nets. 99-113
• 
  Masahiko Morita: Associative memory with nonmonotone dynamics. 115-126
• 
  Friedrich Biegler-König, Frank Bärmann: A learning algorithm for multilayered neural networks based on linear least squares problems. 127-131
• 
  Kevin N. Gurney: Training nets of stochastic units using system identification. 133-145

• 
  Conferences on neural networks and related topics. 151-159

Volume 6, Number 2, 1993

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  Shun-ichi Amari: A universal theorem on learning curves. 161-166
• 
  Shuji Yoshizawa, Masahiko Morita, Shun-ichi Amari: Capacity of associative memory using a nonmonotonic neuron model. 167-176
• 
  Rüdiger W. Brause: The error-bounded descriptional complexity of approximation networks. 177-187
• 
  John J. Shynk, Neil J. Bershad: Stationary points of a single-layer perceptron for nonseparable data models. 189-202
• 
  Kevin T. Judd, Kazuyuki Aihara: Pulse propagation networks: A neural network model that uses temporal coding by action potentials. 203-215
• 
  Koji Nakajima, Yoshihiro Hayakawa: Correct reaction neural network. 217-222
• 
  Albert Y. Zomaya, Tarek M. Nabhan: Centralized and decentralized neuro-adaptive robot controllers. 223-244
• 
  Haluk Ögmen: A neural theory of retino-cortical dynamics. 245-273
• 
  Michael Sabourin, Amar Mitiche: Modeling and classification of shape using a Kohonen associative memory with selective multiresolution. 275-283
• 
  Christopher Ting, Keng-Chee Chuang: An adaptive algorithm for neocognitron to recognize analog images. 285-299

Volume 6, Number 3, 1993

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  Mats Bengtsson: A neural system as a dynamical model for early vision. 313-325
• 
  Haruo Kobayashi, Takashi Matsumoto, Tetsuya Yagi, Takuji Shimmi: Image processing regularization filters on layered architecture. 327-350
• 
  Thierry Denoeux, Régis Lengellé: Initializing back propagation networks with prototypes. 351-363
• 
  Alessandro Sperduti, Antonina Starita: Speed up learning and network optimization with extended back propagation. 365-383
• 
  Charles W. Lee: Learning in neural networks by using tangent planes to constraint surfaces. 385-392
• 
  Lars Kai Hansen: Stochastic linear learning: Exact test and training error averages. 393-396
• 
  Mohamad T. Musavi, K. Kalantri, Wahid Ahmed, Khue Hiang Chan: A minimum error neural network (MNN). 397-407
• 
  Jörgen M. Karlholm: Associative memories with short-range, higher order couplings. 409-421
• 
  John G. Taylor, Stephen Coombes: Learning higher order correlations. 423-427
• 
  Martin Brown, Chris J. Harris, Patrick C. Parks: The interpolation capabilities of the binary CMAC. 429-440
• 
  Geoffrey J. Chappell, John G. Taylor: The temporal Kohönen map. 441-445

Volume 6, Number 4, 1993

• 
  Stephen Grossberg: A solution of the figure-ground problem for biological vision. 463-483

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  Hiroaki Gomi, Mitsuo Kawato: Recognition of manipulated objects by motor learning with modular architecture networks. 485-497
• 
  Gyöngyi Gaál: Population coding by simultaneous activities of neurons in intrinsic coordinate systems defined by their receptive field weighting functions. 499-515
• 
  Wolfram Schiffmann, H. Willi Geffers: Adaptive control of dynamic systems by back propagation networks. 517-524
• 
  Martin Fodslette Møller: A scaled conjugate gradient algorithm for fast supervised learning. 525-533
• 
  Asim Roy, Lark Sang Kim, Somnath Mukhopadhyay: A polynomial time algorithm for the construction and training of a class of multilayer perceptrons. 535-545
• 
  Ulrich Ramacher: Hamiltonian dynamics of neural networks. 547-557
• 
  Giancarlo Parodi, Sandro Ridella, Rodolfo Zunino: Using chaos to generate keys for associative noise-like coding memories. 559-572
• 
  Gregory Allen Kohring: On the Q-state neuron problem in attractor neural networks. 573-581
• 
  Stevan V. Odri, Dusan P. Petrovacki, Gordana A. Krstonosic: Evolutional development of a multilevel neural network. 583-595
• 
  Conferences on neural networks and related topics. 597-606

Volume 6, Number 5, 1993

• 
  Dale A. Brown: Letters to the editor. 607-608
• 
  Alexander Korn: Letters to the editor. 608
• 
  David G. Stork: Letter to the editor. 609
• 
  Nico Weymaere: Letter to the editor. 611
• 
  Arjen van Ooyen, Bernard Nienhuis: Response to letter by N. Weymaere. 611-612

• 
  Terry M. Caelli, David McG. Squire, Tom P. J. Wild: Model-based neural networks. 613-625
• 
  Lei Xu: Least mean square error reconstruction principle for self-organizing neural-nets. 627-648
• 
  Pascal Koiran: On the complexity of approximating mappings using feedforward networks. 649-653
• 
  Michel Benaïm: The "off line learning approximation" in continuous time neural networks: An adiabatic theorem. 655-665
• 
  Frank E. McFadden, Yun Peng, James A. Reggia: Local conditions for phase transitions in neural networks with variable connection strengths. 667-676
• 
  Theodore A. Burton: Averaged neural networks. 677-680
• 
  Harald Englisch, Yegao Xiao, Kailun Yao: Strongly diluted networks with selfinteraction. 681-688
• 
  Haruhisa Takahashi, Etsuji Tomita, Tsutomu Kawabata: Separability of internal representations in multilayer perceptrons with application to learning. 689-703
• 
  J. M. Minor: Parity with two layer feedforward nets. 705-707
• 
  Katsunori Shimohara, Tadasu Uchiyama, Yukio Tokunaga: Subconnection neural network for event-driven temporal sequence processing. 709-718
• 
  Youngjik Lee, Sang-Hoon Oh, Myung Won Kim: An analysis of premature saturation in back propagation learning. 719-728
• 
  Shigeo Abe, Masahiro Kayama, Hiroshi Takenaga, Tadaaki Kitamura: Extracting algorithms from pattern classification neural networks. 729-735

• 
  Conferences on neural networks and related topics. 741-752

Volume 6, Number 6, 1993

• 
  Brendan L. Rogers: New conditioned stimulus trace circuit for the rabbit's nictitating membrane response. 753-769
• 
  William Finnoff, Ferdinand Hergert, Hans-Georg Zimmermann: Improving model selection by nonconvergent methods. 771-783
• 
  Hubertus M. A. Andree, Gerard T. Barkema, Wim Lourens, Arie Taal, Jos C. Vermeulen: A comparison study of binary feedforward neural networks and digital circuits. 785-790
• 
  Faouzi Bouslama, Akira Ichikawa: Application of neural networks to fuzzy control. 791-799
• 
  Ken-ichi Funahashi, Yuichi Nakamura: Approximation of dynamical systems by continuous time recurrent neural networks. 801-806
• 
  Thierry Catfolis: A method for improving the real-time recurrent learning algorithm. 807-821
• 
  Mark D. Plumbley: Efficient information transfer and anti-Hebbian neural networks. 823-833
• 
  Pierre Courrieu: A convergent generator of neural networks. 835-844
• 
  Ali A. Minai, Ronald D. Williams: On the derivatives of the sigmoid. 845-853
• 
  Masahiko Arai: Bounds on the number of hidden units in binary-valued three-layer neural networks. 855-860
• 
  Moshe Leshno, Vladimir Ya. Lin, Allan Pinkus, Shimon Schocken: Multilayer feedforward networks with a nonpolynomial activation function can approximate any function. 861-867
• 
  Shashank K. Mehta, Laszlo Fulop: An analog neural network to solve the hamiltonian cycle problem. 869-881

• 
  Conferences on neural networks and related topics. 883-893

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