NIPS 1987: Denver, CO, USA

@proceedings{DBLP:conf/nips/1987,
  editor    = {Dana Z. Anderson},
  title     = {Neural Information Processing Systems, Denver, Colorado, USA,
               1987},
  booktitle = {NIPS},
  publisher = {American Institue of Physics},
  year      = {1988},
  isbn      = {0-88318-569-5},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

• Yaser S. Abu-Mostafa:
  Connectivity Versus Entropy. 1-8
  
• Joshua Alspector, Robert B. Allen, Victor Hu, Srinagesh Satyanarayana:
  Stochastic Learning Networks and their Electronic Implementation. 9-21
  
• Amir F. Atiya:
  Learning on a General Network. 22-30
  
• Les E. Atlas, Toshiteru Homma, Robert J. Marks II:
  An Artificial Neural Network for Spatio-Temporal Bipolar Patterns: Application to Phoneme Classification. 31-40
  
• Pierre Baldi, Santosh S. Venkatesh:
  On Properties of Networks of Neuron-Like Elements. 41-51
  
• Eric B. Baum, Frank Wilczek:
  Supervised Learning of Probability Distributions by Neural Networks. 52-61
  
• William Baxter, Bruce Dow:
  Centric Models of the Orientation Map in Primary Visual Cortex. 62-71
  
• J. Bernasconi:
  Analysis and Comparison of Different Learning Algorithms for Pattern Association Problems. 72-81
  
• Lyle J. Borg-Graham:
  Simulations Suggest Information Processing Roles for the Diverse Currents in Hippocampal Neurons. 82-94
  
• James M. Bower, Amir F. Atiya:
  Optimal Neural Spike Classification. 95-102
  
• James M. Bower, Yiu-Fai Wong, Jashojiban Banik:
  Neural Networks for Template Matching: Application to Real-Time Classification of the Action Potentials of Real Neurons. 103-113
  
• James M. Bower, Matthew A. Wilson:
  A Computer Simulation of Olfactory Cortex with Functional Implications for Storage and Retrieval of Olfactory Information. 114-126
  
• Nathan H. Brown Jr.:
  Neural Network Implementation Approaches for the Connection Machine. 127-136
  
• Jehoshua Bruck, Joseph W. Goodman:
  On the Power of Neural Networks for Solving Hard Problems. 137-143
  
• David J. Burr:
  Speech Recognition Experiments with Perceptrons. 144-153
  
• L. Richard Carley:
  Presynaptic Neural Information Processing. 154-163
  
• John Y. Cheung, Massoud Omidvar:
  Mathematical Analysis of Learning Behavior of Neuronal Models. 164-173
  
• Tzi-Dar Chiueh, Rodney M. Goodman:
  A Neural Network Classifier Based on Coding Theory. 174-183
  
• Philip A. Chou:
  The Capacity of the Kanerva Associative Memory is Exponential. 184-191
  
• Joshua Chover:
  Phase Transitions in Neural Networks. 192-200
  
• Darryl D. Coon, A. G. Unil Perera:
  New Hardware for Massive Neural Networks. 201-210
  
• Amir Dembo, Ofer Zeitouni:
  High Density Associative Memories. 211-218
  
• John S. Denker, Ben S. Wittner:
  Network Generality, Training Required, and Precision Required. 219-222
  
• Mark Derthick, Joe Tebelskis:
  'Ensemble' Boltzmann Units have Collective Computational Properties like those of Hopfield and Tank Neurons. 223-232
  
• Gérard Dreyfus, Isabelle Guyon, Jean-Pierre Nadal, Léon Personnaz:
  High Order Neural Networks for Efficient Associative Memory Design. 233-241
  
• Frank H. Eeckman:
  The Sigmoid Nonlinearity in Prepyriform Cortex. 242-248
  
• E. Ersu, H. Tolle:
  Hierarchical Learning Control - An Approach with Neuron-Like Associative Memories. 249-261
  
• Manuel F. Fernández:
  On Tropistic Processing and Its Applications. 262-269
  
• Eberhard E. Fetz:
  Correlational Strength and Computational Algebra of Synaptic Connections Between Neurons. 270-277
  
• Michael Fleisher:
  The Hopfield Model with Multi-Level Neurons. 278-289
  
• Michael T. Gately:
  Cycles: A Simulation Tool for Studying Cyclic Neural Networks. 290-296
  
• Paolo Gaudiano:
  Temporal Patterns of Activity in Neural Networks. 297-300
  
• C. Lee Giles, R. D. Griffin, T. Maxwell:
  Encoding Geometric Invariances in Higher-Order Neural Networks. 301-309
  
• Richard M. Golden:
  Probabilistic Characterization of Neural Model Computations. 310-316
  
• Richard Granger, Jose A. Ambros-Ingerson, Howard Henry, Gary Lynch:
  Partitioning of Sensory Data by a Cortical Network. 317-337
  
• Dan W. Hammerstrom:
  The Connectivity Analysis of Simple Association. 338-347
  
• Stephen Jose Hanson, David J. Burr:
  Minkowski-r Back-Propagation: Learning in Connectionist Models with Non-Euclidian Error Signals. 348-357
  
• Geoffrey E. Hinton, James L. McClelland:
  Learning Representations by Recirculation. 358-366
  
• James C. Houk:
  Schema for Motor Control Utilizing a Network Model of the Cerebellum. 367-376
  
• Ken Hsu, David Brady, Demetri Psaltis:
  Experimental Demonstrations of Optical Neural Computers. 377-386
  
• William Y. Huang, Richard Lippmann:
  Neural Net and Traditional Classifiers. 387-396
  
• Ju-Seog Jang, Soo-Young Lee, Sang-Yung Shin:
  An Optimization Network for Matrix Inversion. 397-401
  
• Jagmeet S. Kanwal:
  How the Catfish Tracks Its Prey: An Interactive 'Pipelined' Processing System May Direct Foraging via Reticulospinal Neurons. 402-411
  
• James D. Keeler:
  Capacity for Patterns and Sequences in Kanerva's SDM as Compared to Other Associative Memory Models. 412-421
  
• Christof Koch, Jin Luo, Carver Mead, James Hutchinson:
  Computing Motion Using Resistive Networks. 422-431
  
• Anthony Kuh:
  Performance Measures for Associative Memories that Learn and Forget. 432-441
  
• Alan S. Lapedes, Robert M. Farber:
  How Neural Nets Work. 442-456
  
• Clifford Lau, Vicente Honrubia:
  Distributed Neural Information Processing in the Vestibulo-Ocular System. 457-466
  
• Dietrich Lehmann, D. Brandeis, A. Horst, H. Ozaki, I. Pal:
  Spontaneous and Information-Triggered Segments of Series of Human Brain Electric Field Maps. 467-473
  
• Harrison MonFook Leong:
  Optimization with Artificial Neural Network Systems: A Mapping Principle and a Comparison to Gradient Based Methods. 474-484
  
• Ralph Linsker:
  Towards an Organizing Principle for a Layered Perceptual Network. 485-494
  
• Hendricus G. Loos:
  Reflexive Associative Memories. 495-504
  
• Bruce A. MacDonald:
  Connecting to the Past. 505-514
  
• Stuart Mackie, Hans Peter Graf, Daniel B. Schwartz, John S. Denker:
  Microelectronic Implementations of Connectionist Neural Networks. 515-523
  
• Charles M. Marcus, R. M. Westervelt:
  Basins of Attraction for Electronic Neural Networks. 524-533
  
• Robert J. Marks II, Les E. Atlas, Seho Oh, James A. Ritcey:
  The Performance of Convex Set Projection Based Neural Networks. 534-543
  
• Bartlett W. Mel:
  MURPHY: A Robot that Learns by Doing. 544-553
  
• Anthony N. Michel, Jay A. Farrell, Wolfgang Porod:
  Stability Results for Neural Networks. 554-563
  
• Alexander Moopenn, H. Langenbacher, A. P. Thakoor, S. K. Khanna:
  Programmable Synaptic Chip for Electronic Neural Networks. 564-572
  
• Alan F. Murray, Anthony V. W. Smith, Zoe F. Butler:
  Bit-Serial Neural Networks. 573-583
  
• André J. Noest:
  Phasor Neural Networks. 584-591
  
• Thomas Petsche, Bradley W. Dickinson:
  A Trellis-Structured Neural Network. 592-601
  
• Fernando J. Pineda:
  Generalization of Back propagation to Recurrent and Higher Order Neural Networks. 602-611
  
• John C. Platt, Alan H. Barr:
  Constrained Differential Optimization. 612-621
  
• Tomaso Poggio, Anya C. Hurlbert:
  Learning a Color Algorithm from Examples. 622-631
  
• A. J. Robinson, F. Failside:
  Static and Dynamic Error Propagation Networks with Application to Speech Coding. 632-641
  
• Bruce E. Rosen, James M. Goodwin, Jacques J. Vidal:
  Learning by State Recurrence Detection. 642-651
  
• Ronald Rosenfeld, David S. Touretzky:
  Scaling Properties of Coarse-Coded Symbol Memories. 652-661
  
• F. H. Schuling, H. A. K. Mastebroek, W. H. Zaagman:
  An Adaptive and Heterodyne Filtering Procedure for the Imaging of Moving Objects. 662-673
  
• Christopher L. Scofield, Douglas L. Reilly, Charles Elbaum, Leon N. Cooper:
  Pattern Class Degeneracy in an Unrestricted Storage Density Memory. 674-682
  
• Christopher L. Scofield:
  A Mean Field Theory of Layer IV of Visual Cortex and Its Application to Artificial Neural Networks. 683-692
  
• J. F. Shepanski, S. A. Macy:
  Teaching Artificial Neural Systems to Drive: Manual Training Techniques for Autonomous Systems. 693-700
  
• Ralph M. Siegel:
  Discovering Structure from Motion in Monkey, Man and Machine. 701-708
  
• Ronald H. Silverman, Andrew S. Noetzel:
  Time-Sequential Self-Organization of Hierarchical Neural Networks. 709-714
  
• Alexander Singer, John P. Donoghue:
  A Computer Simulation of Cerebral Neocortex: Computational Capabilities of Nonlinear Neural Networks. 715-729
  
• Paul Smolensky:
  Analysis of Distributed Representation of Constituent Structure in Connectionist Systems. 730-739
  
• Andreas Stafylopatis, Marios D. Dikaiakos, D. Kontoravdis:
  Spatial Organization of Neural Networks: A Probabilistic Modeling Approach. 740-749
  
• W. Scott Stornetta, Tad Hogg, Bernardo A. Huberman:
  A Dynamical Approach to Temporal Pattern Processing. 750-759
  
• Guo-Zheng Sun, Yee-Chun Lee, Hsing-Hen Chen:
  A Novel Net that Learns Sequential Decision Process. 760-766
  
• Hisashi Suzuki, Suguru Arimoto:
  Self-Organization of Associative Database and Its Applications. 767-774
  
• Gene A. Tagliarini, Edward W. Page:
  A Neural-Network Solution to the Concentrator Assignment Problem. 775-782
  
• Manoel Fernando Tenorio:
  Using Neural Networks to Improve Cochlear Implant Speech Perception. 783-793
  
• Gerald Tesauro, Terrence J. Sejnowski:
  A 'Neural' Network that Learns to Play Backgammon. 794-803
  
• Sherryl Tomboulian:
  Introduction to a System for Implementing Neural Net Connections on SIMD Architectures. 804-813
  
• Mario P. Vecchi, Jawad A. Salehi:
  Neuromorphic Networks Based on Sparse Optical Orthogonal Codes. 814-823
  
• Jacques J. Vidal, John Haggerty:
  Synchronization in Neural Nets. 824-829
  
• Harry Wechsler, George Lee Zimmerman:
  Invariant Object Recognition Using a Distributed Associative Memory. 830-839
  
• Richard C. Windecker:
  Learning in Networks of Nondeterministic Adaptive Logic Elements. 840-849
  
• Ben S. Wittner, John S. Denker:
  Strategies for Teaching Layered Networks Classification Tasks. 850-859
  
• John L. Wyatt Jr., D. L. Standley:
  A Method for the Design of Stable Lateral Inhibition Networks that is Robust in the Presence of Circuit Parasitics. 860-868
  

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