Volume 6, Number 1, January 1994

• 
  Granger G. Sutton III, James A. Reggia, Steven L. Armentrout, C. Lynne D'Autrechy: Cortical Map Reorganization as a Competitive Process. 1-13

• 
  Alain Destexhe, Zachary F. Mainen, Terrence J. Sejnowski: An Efficient Method for Computing Synaptic Conductances Based on a Kinetic Model of Receptor Binding. 14-18

• 
  Alexander V. Lukashin, Apostolos P. Georgopoulos: A Neural Network for Coding of Trajectories by Time Series of Neuronal Population Vectors. 19-28
• 
  Terence D. Sanger: Theoretical Considerations for the Analysis of Population Coding in Motor Cortex. 29-37
• 
  Dean V. Buonomano, Michael D. Mauk: Neural Network Model of the Cerebellum: Temporal Discrimination and the Timing of Motor Responses. 38-55
• 
  Allan Gottschalk, Malcolm D. Ogilvie, Diethelm W. Richter, Allan I. Pack: Computational Aspects of the Respiratory Pattern Generator. 56-68
• 
  Thomas LoFaro, Nancy Kopell, Eve Marder, Scott L. Hooper: Subharmonic Coordination in Networks of Neurons with Slow Conductances. 69-84
• 
  Ali A. Minai, William B. Levy: Setting the Activity Level in Sparse Random Networks. 85-99
• 
  Kenneth D. Miller, David J. C. MacKay: The Role of Constraints in Hebbian Learning. 100-126
• 
  Zhaoping Li, Joseph J. Atick: Toward a Theory of the Striate Cortex. 127-146
• 
  Barak A. Pearlmutter: Fast Exact Multiplication by the Hessian. 147-160
• 
  Andrew H. Gee, Richard W. Prager: Polyhedral Combinatorics and Neural Networks. 161-180

Volume 6, Number 2, March 1994

• 
  Michael I. Jordan, Robert A. Jacobs: Hierarchical Mixtures of Experts and the EM Algorithm. 181-214

• 
  Gerald Tesauro: TD-Gammon, a Self-Teaching Backgammon Program, Achieves Master-Level Play. 215-219
• 
  L. F. Cugliandolo: Correlated Attractors from Uncorrelated Stimuli. 220-224

• 
  Bard Ermentrout, Nancy Kopell: Learning of Phase Lags in Coupled Neural Oscillators. 225-241
• 
  Mark E. Nelson: A Mechanism for Neuronal Gain Control by Descending Pathways. 242-254
• 
  Geoffrey J. Goodhill, Harry G. Barrow: The Role of Weight Normalization in Competitive Learning. 255-269
• 
  Stephen J. Roberts, Lionel Tarassenko: A Probabilistic Resource Allocating Network for Novelty Detection. 270-284
• 
  William Finnoff: Diffusion Approximations for the Constant Learning Rate Backpropagation Algorithm and Resistance to Local Minima. 285-295
• 
  Françoise Beaufays, Eric A. Wan: Relating Real-Time Backpropagation and Backpropagation-Through-Time: An Application of Flow Graph Interreciprocity. 296-306
• 
  Pierre Baldi, Yves Chauvin: Smooth On-Line Learning Algorithms for Hidden Markov Models. 307-318
• 
  Michel Benaïm: On Functional Approximation with Normalized Gaussian Units. 319-333
• 
  Alan L. Yuille, Paul E. Stolorz, Joachim Utans: Statistical Physics, Mixtures of Distributions, and the EM Algorithm. 334-340

Volume 6, Number 3, May 1994

• 
  Alan L. Yuille, J. J. Kosowsky: Statistical Physics Algorithms That Converge. 341-356

• 
  Randall C. O'Reilly, Mark H. Johnson: Object Recognition and Sensitive Periods: A Computational Analysis of Visual Imprinting. 357-389

• 
  Ning Qian: Computing Stereo Disparity and Motion with Known Binocular Cell Properties. 390-404
• 
  Edwin E. Munro, Larry E. Shupe, Eberhard E. Fetz: Integration and Differentiation in Dynamic Recurrent Neural Networks. 405-419
• 
  Chung-Ming Kuan, Kurt Hornik, Halbert White: A Convergence Result for Learning in Recurrent Neural Networks. 420-440
• 
  Csaba Szepesvári, László Balázs, András Lörincz: Topology Learning Solved by Extended Objects: A Neural Network Model. 441-458
• 
  Pascal Koiran: Dynamics of Discrete Time, Continuous State Hopfield Networks. 459-468
• 
  K. P. Unnikrishnan, Kootala P. Venugopal: Alopex: A Correlation-Based Learning Algorithm for Feedforward and Recurrent Neural Networks. 469-490
• 
  Jean-Pierre Nadal, Néstor Parga: Duality Between Learning Machines: A Bridge Between Supervised and Unsupervised Learning. 491-508
• 
  Hong Pi, Carsten Peterson: Finding the Embedding Dimension and Variable Dependencies in Time Series. 509-520
• 
  Dimitry Gorinevsky, Thomas H. Connolly: Comparison of Some Neural Network and Scattered Data Approximations: The Inverse Manipulator Kinematics Example. 521-542
• 
  Vera Kurková, Paul C. Kainen: Functionally Equivalent Feedforward Neural Networks. 543-558

Volume 6, Number 4, July 1994

• 
  David J. Field: What Is the Goal of Sensory Coding? 559-601

• 
  Ernst Niebur, Florentin Wörgötter: Design Principles of Columnar Organization in Visual Cortex. 602-614

• 
  Geoffrey J. Goodhill, David J. Willshaw: Elastic Net Model of Ocular Dominance: Overall Stripe Pattern and Monocular Deprivation. 615-621
• 
  Öjvind Bernander, Christof Koch, Marius Usher: The Effect of Synchronized Inputs at the Single Neuron Level. 622-641
• 
  Haim Sompolinsky, Michail Tsodyks: Segmentation by a Network of Oscillators with Stored Memories. 642-657
• 
  Yukio Hayashi: Numerical Bifurcation Analysis of an Oscillatory Neural Network with Synchronous/Asynchronous Connections. 658-667
• 
  J. Devin McAuley, Joseph Stampfli: Analysis of the Effects of Noise on a Model for the Neural Mechanism of Short-Term Active Memory. 668-678
• 
  Bard Ermentrout: Reduction of Conductance-Based Models with Slow Synapses to Neural Nets. 679-695
• 
  Kenji Doya, Allen I. Selverston: Dimension Reduction of Biological Neuron Models by Artificial Neural Networks. 696-717
• 
  Gary M. Scott, W. Harmon Ray: Neural Network Process Models Based on Linear Model Structures. 718-738
• 
  Juha Karhunen: Stability of Oja's PCA Subspace Rule. 739-747
• 
  Man-Fung Cheung, Kevin M. Passino, Stephen Yurkovich: Supervised Training of Neural Networks via Ellipsoid Algorithms. 748-760
• 
  N. Scott Cardell, Wayne H. Joerding, Ying Li: Why Some Feedforward Networks Cannot Learn Some Polynomials. 761-766

Volume 6, Number 5, September 1994

• 
  Stephen P. Luttrell: A Bayesian Analysis of Self-Organizing Maps. 767-794
• 
  Marius Usher, Martin Stemmler, Christof Koch, Zeev Olami: Network Amplification of Local Fluctuations Causes High Spike Rate Variability, Fractal Firing Patterns and Oscillatory Local Field Potentials. 795-836

• 
  Alan M. N. Fu: Statistical Analysis of an Autoassociative Memory Network. 837-841

• 
  Jirí Síma: Loading Deep Networks Is Hard. 842-850
• 
  Vladimir Vapnik, Esther Levin, Yann LeCun: Measuring the VC-Dimension of a Learning Machine. 851-876
• 
  Wolfgang Maass: Neural Nets with Superlinear VC-Dimension. 877-884
• 
  Jihong Lee: A Novel Design Method for Multilayer Feedforward Neural Networks. 885-901
• 
  Jean-Dominique Gascuel, Bahram Moobed, Michel Weinfeld: An Internal Mechanism for Detecting Parasite Attractors in a Hopfield Network. 902-915
• 
  Thorsteinn Rögnvaldsson: On Langevin Updating in Multilayer Perceptrons. 916-926
• 
  Hossam Osman, Moustafa M. Fahmy: Probabilistic Winner-Take-All Learning Algorithm for Radial-Basis-Function Neural Classifiers. 927-943
• 
  T. Matsumoto, K. Kondo: Realization of the "Weak Rod" by a Double Layer Parallel Network. 944-956
• 
  Daniel J. Amit, Stefano Fusi: Learning in Neural Networks with Material Synapses. 957-982
• 
  Pierre-Yves Burgi, Norberto M. Grzywacz: Model Based on Extracellular Potassium for Spontaneous Synchronous Activity in Developing Retinas. 983-1004
• 
  Michael S. Lewicki: Bayesian Modeling and Classification of Neural Signals. 1005-1030

Volume 6, Number 6, November 1994

• 
  Bartlett W. Mel: Information Processing in Dendritic Trees. 1031-1085

• 
  Paul A. Rhodes, Charles M. Gray: Simulations of Intrinsically Bursting Neocortical Pyramidal Neurons. 1086-1110
• 
  Venkatesh N. Murthy, Eberhard E. Fetz: Effects of Input Synchrony on the Firing Rate of a Three-Conductance Cortical Neuron Model. 1111-1126
• 
  Ying-Cheng Lai, Raimond L. Winslow, Murray B. Sachs: The Functional Role of Excitatory and Inhibitory Interactions in Chopper Cells of the Anteroventral Cochlear Nucleus. 1127-1140
• 
  Carlos Lourenço, Agnessa Babloyantz: Control of Chaos in Networks with Delay: A Model for Synchronization of Cortical Tissue. 1141-1154
• 
  Peter Manolios, Robert Fanelli: First-Order Recurrent Neural Networks and Deterministic Finite State Automata. 1155-1173
• 
  Lawrence K. Saul, Michael I. Jordan: Learning in Boltzmann Trees. 1174-1184
• 
  Tommi Jaakkola, Michael I. Jordan, Satinder P. Singh: On the Convergence of Stochastic Iterative Dynamic Programming Algorithms. 1185-1201
• 
  R. S. Shadafan, M. Niranjan: A Dynamic Neural Network Architecture by Sequential Partitioning of the Input Space. 1202-1222
• 
  Lars Kai Hansen, Carl Edward Rasmussen: Pruning from Adaptive Regularization. 1223-1232
• 
  Yoshifusa Ito: Approximation Capability of Layered Neural Networks with Sigmoid Units on Two Layers. 1233-1243
• 
  Motoaki Kawanabe, Shun-ichi Amari: Estimation of Network Parameters in Semiparametric Stochastic Perceptron. 1244-1261
• 
  Kurt Hornik, Maxwell B. Stinchcombe, Halbert White, Peter Auer: Degree of Approximation Results for Feedforward Networks Approximating Unknown Mappings and Their Derivatives. 1262-1275
• 
  Yong Liu: Influence Function Analysis of PCA and BCM Learning. 1276-1288
• 
  Harris Drucker, Corinna Cortes, Lawrence D. Jackel, Yann LeCun, Vladimir Vapnik: Boosting and Other Ensemble Methods. 1289-1301

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