index int64 0 20.3k | text stringlengths 0 1.3M | year stringdate 1987-01-01 00:00:00 2024-01-01 00:00:00 | No stringlengths 1 4 |
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0 | BIT - SERIAL NEURAL NETWORKS Alan F. Murray, Anthony V. W. Smith and Zoe F. Butler. Department of Electrical Engineering, University of Edinburgh, The King's Buildings, Mayfield Road, Edinburgh, Scotland, EH93JL. ABSTRACT 573 A bit - serial VLSI neural network is described from an initial architect... | 1987 | 1 |
1 | 474 OPTIMIZA nON WITH ARTIFICIAL NEURAL NETWORK SYSTEMS: A MAPPING PRINCIPLE AND A COMPARISON TO GRADIENT BASED METHODS t Harrison MonFook Leong Research Institute for Advanced Computer Science NASA Ames Research Center 230-5 Moffett Field, CA, 94035 ABSTRACT General formulae for mapping o... | 1987 | 10 |
2 | OPTIMAL NEURAL SPIKE CLASSIFICATION Abstract Amir F. Atiya(*) and James M. Bower(**) (*) Dept. of Electrical Engineering (**) Division of Biology California Institute of Technology Ca 91125 Being able to record the electrical activities of a number of neurons simultaneously is likely to be impor... | 1987 | 11 |
3 | 495 REFLEXIVE ASSOCIATIVE MEMORIES Hendrlcus G. Loos Laguna Research Laboratory, Fallbrook, CA 92028-9765 ABSTRACT In the synchronous discrete model, the average memory capacity of bidirectional associative memories (BAMs) is compared with that of Hopfield memories, by means of a calculat10n of the... | 1987 | 12 |
4 | 534 The Performance of Convex Set projection Based Neural Networks Robert J. Marks II, Les E. Atlas, Seho Oh and James A. Ritcey Interactive Systems Design Lab, FT-IO University of Washington, Seattle, Wa 98195. ABSTRACT We donsider a class of neural networks whose performance can be analyzed an... | 1987 | 13 |
5 | 144 SPEECH RECOGNITION EXPERIMENTS WITH PERCEPTRONS D. J. Burr Bell Communications Research Morristown, NJ 07960 ABSTRACT Artificial neural networks (ANNs) are capable of accurate recognition of simple speech vocabularies such as isolated digits [1]. This paper looks at two more difficult voc... | 1987 | 14 |
6 | ON PROPERTIES OF NETWORKS OF NEURON-LIKE ELEMENTS Pierre Baldi· and Santosh S. Venkatesht 15 December 1987 Abstract The complexity and computational capacity of multi-layered, feedforward neural networks is examined. Neural networks for special purpose (structured) functions are examined from the p... | 1987 | 15 |
7 | 'Ensemble' Boltzmann Units have Collective Computational Properties like those of Hopfield and Tank Neurons Mark Derthick and Joe Tebelskis Department of Computer Science Carnegie-Mellon University 1 Introduction 223 There are three existing connection::;t models in which network states are assi... | 1987 | 16 |
8 | 262 ON TROPISTIC PROCESSING AND ITS APPLICATIONS Manuel F. Fernandez General Electric Advanced Technology Laboratories Syracuse, New York 13221 ABSTRACT The interaction of a set of tropisms is sufficient in many cases to explain the seemingly complex behavioral responses exhibited by varied c... | 1987 | 17 |
9 | 814 NEUROMORPHIC NETWORKS BASED ON SPARSE OPTICAL ORTHOGONAL CODES Mario P. Vecchi and Jawad A. Salehi Bell Communications Research 435 South Street Morristown, NJ 07960-1961 Abstrad A family of neuromorphic networks specifically designed for communications and optical signal processing appli... | 1987 | 18 |
10 | 794 A 'Neural' Network that Learns to Play Backgammon G. Tesauro Center for Complex Systems Research, University of Illinois at Urbana-Champaign, 508 S. Sixth St., Champaign, IL 61820 T. J. Sejnowski Biophysics Dept., Johns Hopkins University, Baltimore, MD 21218 ABSTRACT We describe a class of ... | 1987 | 19 |
11 | CONNECTIVITY VERSUS ENTROPY Yaser S. Abu-Mostafa California Institute of Technology Pasadena, CA 91125 ABSTRACT 1 How does the connectivity of a neural network (number of synapses per neuron) relate to the complexity of the problems it can handle (measured by the entropy)? Switching theory would... | 1987 | 2 |
12 | 358 LEARNING REPRESENTATIONS BY RECIRCULATION Geoffrey E. Hinton Computer Science and Psychology Departments, University of Toronto, Toronto M5S lA4, Canada James L. McClelland Psychology and Computer Science Departments, Carnegie-Mellon University, Pittsburgh, PA 15213 ABSTRACT We describe a... | 1987 | 20 |
13 | 715 A COMPUTER SIMULATION OF CEREBRAL NEOCORTEX: COMPUTATIONAL CAPABILITIES OF NONLINEAR NEURAL NETWORKS Alexander Singer* and John P. Donoghue** *Department of Biophysics, Johns Hopkins University, Baltimore, MD 21218 (to whom all correspondence should be addressed) **Center for Neural Science, Br... | 1987 | 21 |
14 | 674 P A 'ITERN CLASS DEGENERACY IN AN UNRESTRICfED STORAGE DENSITY MEMORY Christopher L. Scofield, Douglas L. Reilly, Charles Elbaum, Leon N. Cooper Nestor, Inc., 1 Richmond Square, Providence, Rhode Island, 02906. ABSTRACT The study of distributed memory systems has produced a number of mode... | 1987 | 22 |
15 | 850 Strategies for Teaching Layered Networks Classification Tasks Ben S. Wittner 1 and John S. Denker AT&T Bell Laboratories Holmdel, New Jersey 07733 Abstract There is a widespread misconception that the delta-rule is in some sense guaranteed to work on networks without hidden units. As previou... | 1987 | 23 |
16 | 830 Invariant Object Recognition Using a Distributed Associative Memory Harry Wechsler and George Lee Zimmerman Department or Electrical Engineering University or Minnesota Minneapolis, MN 55455 Abstract This paper describes an approach to 2-dimensional object recognition. Complex-log conformal map... | 1987 | 24 |
17 | 290 CYCLES: A Simulation Tool for Studying Cyclic Neural Networks Michael T. Gately Texas Instruments Incorporated, Dallas, TX 75265 ABSTRACT A computer program has been designed and implemented to allow a researcher to analyze the oscillatory behavior of simulated neural networks with cyclic conne... | 1987 | 25 |
18 | 22 Abstract LEARNING ON A GENERAL NETWORK Amir F. Atiya Department of Electrical Engineering California Institute of Technology Ca 91125 This paper generalizes the backpropagation method to a general network containing feedback t;onnections. The network model considered consists of interconnected g... | 1987 | 26 |
19 | Neural Net and Traditional Classifiers1 William Y. Huang and Richard P. Lippmann MIT Lincoln Laboratory Lexington, MA 02173, USA 387 Abstract. Previous work on nets with continuous-valued inputs led to generative procedures to construct convex decision regions with two-layer perceptrons (one hidden ... | 1987 | 27 |
20 | 652 Scaling Properties of Coarse-Coded Symbol Memories Ronald Rosenfeld David S. Touretzky Computer Science Department Carnegie Mellon University Pittsburgh, Pennsylvania 15213 Abstract: Coarse-coded symbol memories have appeared in several neural network symbol processing models. In order to de... | 1987 | 28 |
21 | 824 SYNCHRONIZATION IN NEURAL NETS Jacques J. Vidal University of California Los Angeles, Los Angeles, Ca. 90024 John Haggerty· ABSTRACT The paper presents an artificial neural network concept (the Synchronizable Oscillator Networks) where the instants of individual firings in the form of point ... | 1987 | 29 |
22 | 278 ABSTRACT THE HOPFIELD MODEL WITH MUL TI-LEVEL NEURONS Michael Fleisher Department of Electrical Engineering Technion - Israel Institute of Technology Haifa 32000, Israel The Hopfield neural network. model for associative memory is generalized. The generalization replaces two state neurons by... | 1987 | 3 |
23 | 174 A Neural Network Classifier Based on Coding Theory Tzt-Dar Chlueh and Rodney Goodman eanrornla Instltute of Technology. Pasadena. eanromla 91125 ABSTRACT The new neural network classifier we propose transforms the classification problem into the coding theory problem of decoding a noisy codewor... | 1987 | 30 |
24 | 515 MICROELECTRONIC IMPLEMENTATIONS OF CONNECTIONIST NEURAL NETWORKS Stuart Mackie, Hans P. Graf, Daniel B. Schwartz, and John S. Denker AT&T Bell Labs, Holmdel, NJ 07733 Abstract In this paper we discuss why special purpose chips are needed for useful implementations of connectionist neural networ... | 1987 | 31 |
25 | 730 Analysis of distributed representation of constituent structure in connectionist systems Paul Smolensky Department of Computer Science, University of Colorado, Boulder, CO 80309-0430 Abstract A general method, the tensor product representation, is described for the distributed representation of ... | 1987 | 32 |
26 | HIERARCHICAL LEARNING CONTROL AN APPROACH WITH NEURON-LIKE ASSOCIATIVE MEMORIES E. Ersu ISRA Systemtechnik GmbH, Schofferstr. 15, D-6100 Darmstadt, FRG H. Tolle TH Darmstadt, Institut fur Regelungstechnik, Schlo~graben 1, D-6100 Darmstadt, FRG ABSTRACT 249 Advances in brain theory need two compl... | 1987 | 33 |
27 | 154 PRESYNApnC NEURAL INFORMAnON PROCESSING L. R. Carley Department of Electrical and Computer Engineering Carnegie Mellon University, Pittsburgh PA 15213 ABSTRACT The potential for presynaptic information processing within the arbor of a single axon will be discussed in this paper. Current knowled... | 1987 | 34 |
28 | AN OPTIMIZATION NETWORK FOR MATRIX INVERSION Ju-Seog Jang, S~ Young Lee, and Sang-Yung Shin Korea Advanced Institute of Science and Technology, P.O. Box 150, Cheongryang, Seoul, Korea ABSTRACT Inverse matrix calculation can be considered as an optimization. We have demonstrated that this problem can b... | 1987 | 35 |
29 | 524 BASINS OF ATTRACTION FOR ELECTRONIC NEURAL NETWORKS C. M. Marcus R. M. Westervelt Division of Applied Sciences and Department of Physics Harvard University, Cambridge, MA 02138 ABSTRACT We have studied the basins of attraction for fixed point and oscillatory attractors in an electronic an... | 1987 | 36 |
30 | 564 PROGRAMMABLE SYNAPTIC CHIP FOR ELECTRONIC NEURAL NETWORKS A. Moopenn, H. Langenbacher, A.P. Thakoor, and S.K. Khanna Jet Propulsion Laboratory California Institute of Technology Pasadena, CA 91009 ABSTRACT A binary synaptic matrix chip has been developed for electronic neural networks. ... | 1987 | 37 |
31 | 622 LEARNING A COLOR ALGORITHM FROM EXAMPLES Anya C. Hurlbert and Tomaso A. Poggio Artificial Intelligence Laboratory and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA ABSTRACT A lightness algorithm that separates surface reflect... | 1987 | 38 |
32 | 602 GENERALIZATION OF BACKPROPAGATION TO RECURRENT AND HIGHER ORDER NEURAL NETWORKS Fernando J. Pineda Applied Physics Laboratory, Johns Hopkins University Johns Hopkins Rd., Laurel MD 20707 Abstract A general method for deriving backpropagation algorithms for networks with recurrent and high... | 1987 | 39 |
33 | 442 Abstract: How Neural Nets Work Alan Lapedes Robert Farber Theoretical Division Los Alamos National Laboratory Los Alamos, NM 87545 There is presently great interest in the abilities of neural networks to mimic "qualitative reasoning" by manipulating neural incodings of symbols. Less work ... | 1987 | 4 |
34 | Neural Network Implementation Approaches for the Connection Machine Nathan H. Brown, Jr. MRJlPerkin Elmer, 10467 White Granite Dr. (Suite 304), Oakton, Va. 22124 ABSlRACf 127 The SIMD parallelism of the Connection Machine (eM) allows the construction of neural network simulations by the use of s... | 1987 | 40 |
35 | On the Power of Neural Networks for Solving Hard Problems J ehoshua Bruck Joseph W. Goodman Information Systems Laboratory Departmen t of Electrical Engineering Stanford University Stanford, CA 94305 Abstract This paper deals with a neural network model in which each neuron performs a thre... | 1987 | 41 |
36 | 402 HOW THE PROCESSING CATFISH TRACKS ITS PREY: AN INTERACTIVE "PIPELINED" SYSTEM MAY DIRECT FORAGING VIA RETlCULOSPINAL NEURONS. Jagmeet S. Kanwal Dept. of Cellular & Structural Biology, Univ. of Colorado, Sch. of Medicine, 4200 East, Ninth Ave., Denver, CO 80262. ABSTRACT Ict... | 1987 | 42 |
37 | 584 PHASOR NEURAL NETVORKS Andr~ J. Noest, N.I.B.R., NL-ll0S AZ Amsterdam, The Netherlands. ABSTRACT A novel network type is introduced which uses unit-length 2-vectors for local variables. As an example of its applications, associative memory nets are defined and their performance analyzed. Real syst... | 1987 | 43 |
38 | 422 COMPUTING MOTION USING RESISTIVE NETWORKS Christof Koch, Jin Luo, Carver Mead California Institute of Technology, 216-76, Pasadena, Ca. 91125 James Hutchinson Jet Propulsion Laboratory, California Institute of Technology Pasadena, Ca. 91125 INTRODUCTION To us, and to other biological organis... | 1987 | 44 |
39 | EXPERIMENTAL DEMONSTRATIONS OF OPTICAL NEURAL COMPUTERS Ken Hsu, David Brady, and Demetri Psaltis Department of Electrical Engineering California Institute of Technology Pasadena, CA 91125 ABSTRACT 377 We describe two expriments in optical neural computing. In the first a closed optical feedb... | 1987 | 45 |
40 | 544 MURPHY: A Robot that Learns by Doing Bartlett W. Mel Center for Complex Systems Research University of Illinois 508 South Sixth Street Champaign, IL 61820 January 2, 1988 Abstract MURPHY consists of a camera looking at a robot arm, with a connectionist network architecture situated in ... | 1987 | 46 |
41 | SPONTANEOUS AND INFORMATION-TRIGGERED SEGMENTS OF SERIES OF HUMAN BRAIN ELECTRIC FIELD MAPS 467 D. lehmann, D. Brandeis*, A. Horst, H. Ozaki* and I. Pal* Neurol09Y Department, University Hospital, 8091 Zurich, Switzerland ABSTRACT The brain works in a state-dependent manner: processin9 strate9ies a... | 1987 | 47 |
42 | 82 SIMULATIONS SUGGEST INFORMATION PROCESSING ROLES FOR THE DIVERSE CURRENTS IN HIPPOCAMPAL NEURONS Lyle J. Borg-Graham Harvard-MIT Division of Health Sciences and Technology and Center for Biological Information Processing, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139 ... | 1987 | 48 |
43 | AN ARTIFICIAL NEURAL NETWORK FOR SPATIOTEMPORAL BIPOLAR PATTERNS: APPLICATION TO PHONEME CLASSIFICATION Toshiteru Homma Les E. Atlas Robert J. Marks II Interactive Systems Design Laboratory Department of Electrical Engineering, Ff-l0 University of Washington Seattle, Washington 98195 ABSTRACT... | 1987 | 49 |
44 | 740 SPATIAL ORGANIZATION OF NEURAL NEn~ORKS: A PROBABILISTIC MODELING APPROACH A. Stafylopatis M. Dikaiakos D. Kontoravdis National Technical University of Athens, Department of Electrical Engineering, Computer Science Division, 15773 Zographos, Athens, Greece. ABSTRACT The aim of this paper ... | 1987 | 5 |
45 | Teaching Artificial Neural Systems to Drive: Manual Training Techniques for Autonomous Systems J. F. Shepanski and S. A. Macy TRW, Inc. One Space Park, 02/1779 Redondo Beach, CA 90278 Abetract 693 We have developed a methodology for manually training autononlous control systems based on artif... | 1987 | 50 |
46 | 270 Correlational Strength and Computational Algebra of Synaptic Connections Between Neurons Eberhard E. Fetz Department of Physiology & Biophysics, University of Washington, Seattle, WA 98195 ABSTRACT Intracellular recordings in spinal cord motoneurons and cerebral cortex neurons have provided ... | 1987 | 51 |
47 | DISCOVERING STRUCfURE FROM MOTION IN MONKEY, MAN AND MACHINE Ralph M. Siegel· The Salk Institute of Biology, La Jolla, Ca. 92037 ABSTRACT 701 The ability to obtain three-dimensional structure from visual motion is important for survival of human and non-human primates. Using a parallel processing m... | 1987 | 52 |
48 | 632 STATIC AND DYNAMIC ERROR PROPAGATION NETWORKS WITH APPLICATION TO SPEECH CODING A J Robinson, F Fallside Cambridge University Engineering Department Trumpington Street, Cambridge, England Abstract Error propagation nets have been shown to be able to learn a variety of tasks in which a sta... | 1987 | 53 |
49 | 367 SCHEMA I'OR MOTOR CONTROL OT ILl ZING A NETWORK MODEL 01' THE CEREBELLUM James C. Houk, Ph.D. Northwestern University Medical School, Chicago, Illinois 60201 ABSTRACT This paper outlines a schema for movement control based on two stages of signal processing. The higher s... | 1987 | 54 |
50 | DISTRIBUTED NEURAL INFORMATION PROCESSING IN THE VESTIBULO-OCULAR SYSTEM Clifford Lau Office of Naval Research Detach ment Pasadena, CA 91106 Vicente Honrubia* UCLA Division of Head and Neck Surgery Los Angeles, CA 90024 ABSTRACT A new distributed neural information-processing model is pro... | 1987 | 55 |
51 | TIME-SEQUENTIAL SELF-ORGANIZATION OF HIERARCHICAL NEURAL NETWORKS Ronald H. Silverman Cornell University Medical College, New York, NY 10021 Andrew S. Noetzel polytechnic University, Brooklyn, NY 11201 ABSTRACT Self-organization of multi-layered networks can be realized by time-sequential org... | 1987 | 56 |
52 | 860 A METHOD FOR THE DESIGN OF STABLE LATERAL INHIBITION NETWORKS THAT IS ROBUST IN THE PRESENCE OF CIRCUIT PARASITICS J.L. WYATT, Jr and D.L. STANDLEY Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge, Massachusetts 02139 ABSTRACT In... | 1987 | 57 |
53 | 612 Constrained Differential Optimization John C. Platt Alan H. Barr California Institute of Technology, Pasadena, CA 91125 Abstract Many optimization models of neural networks need constraints to restrict the space of outputs to a subspace which satisfies external criteria. Optimizations using ene... | 1987 | 58 |
54 | ENCODING GEOMETRIC INVARIANCES IN HIGHER-ORDER NEURAL NETWORKS C.L. Giles Air Force Office of Scientific Research, Bolling AFB, DC 20332 R.D. Griffin Naval Research Laboratory, Washington, DC 20375-5000 T. Maxwell Sachs-Freeman Associates, Landover, MD 20785 ABSTRACT 301 We describe a m... | 1987 | 59 |
55 | A NEURAL-NETWORK SOLUTION TO THE CONCENTRATOR ASSIGNNlENT PROBLEM Gene A. Tagliarini Edward W. Page Department of Computer Science, Clemson University, Clemson, SC 29634-1906 ABSTRACT 775 Networks of simple analog processors having neuron-like properties have been employed to compute good sol... | 1987 | 6 |
56 | 760 A NOVEL NET THAT LEARNS SEQUENTIAL DECISION PROCESS G.Z. SUN, Y.C. LEE and H.H. CHEN Department of PhYJicJ and AJtronomy and InJtitute for Advanced Computer StudieJ UNIVERSITY OF MARYLAND,COLLEGE PARK,MD 20742 ABSTRACT We propose a new scheme to construct neural networks to classify patte... | 1987 | 60 |
57 | 164 MATHEMATICAL ANALYSIS OF LEARNING BEHAVIOR OF NEURONAL MODELS By JOHN Y. CHEUNG MASSOUD OMIDVAR SCHOOL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCE UNIVERSITY OF OKLAHOMA NORMAN, OK 73019 Presented to the IEEE Conference on "Neural Information Processing SystemsNatural and Synthetic," D... | 1987 | 61 |
58 | 201 NEW HARDWARE FOR MASSIVE NEURAL NETWORKS D. D. Coon and A. G. U. Perera Applied Technology Laboratory University of Pittsburgh Pittsburgh, PA 15260. ABSTRACT Transient phenomena associated with forward biased silicon p + - n - n + structures at 4.2K show remarkable similarities with biological ... | 1987 | 62 |
59 | 662 AN ADAPTIVE AND HETERODYNE FILTERING PROCEDURE FOR THE IMAGING OF MOVING OBJECTS F. H. Schuling, H. A. K. Mastebroek and W. H. Zaagman Biophysics Department, Laboratory for General Physics Westersingel 34, 9718 eM Groningen, The Netherlands ABSTRACT Recent experimental work on the stimulus velo... | 1987 | 63 |
60 | 192 PHASE TRANSITIONS IN NEURAL NETWORKS Joshua Chover University of Wisconsin, Madison, WI 53706 ABSTRACT Various simulat.ions of cort.ical subnetworks have evidenced something like phase transitions with respect to key parameters. We demonstrate that. such transi t.ions must. indeed exist. in ... | 1987 | 64 |
61 | USING NEURAL NETWORKS TO IMPROVE COCHLEAR IMPLANT SPEECH PERCEPTION Manoel F. Tenorio School of Electrical Engineering Purdue University West Lafayette, IN 47907 ABSTRACT 783 An increasing number of profoundly deaf patients suffering from sensorineural deafness are using cochlear implants as pro... | 1987 | 65 |
62 | SELF-ORGANIZATION OF ASSOCIATIVE DATABASE AND ITS APPLICATIONS Hisashi Suzuki and Suguru Arimoto Osaka University, Toyonaka, Osaka 560, Japan ABSTRACT An efficient method of self-organizing associative databases is proposed together with applications to robot eyesight systems. The proposed databases c... | 1987 | 66 |
63 | TEMPORAL PATTERNS OF ACTIVITY IN NEURAL NETWORKS Paolo Gaudiano Dept. of Aerospace Engineering Sciences, University of Colorado, Boulder CO 80309, USA January 5, 1988 Abstract Patterns of activity over real neural structures are known to exhibit timedependent behavior. It would seem that the brain ... | 1987 | 67 |
64 | Network Generality, Training Required, and PrecisIon Required John S. Denker and Ben S. Wittner 1 AT&T Bell Laboratories Holmdel, New Jersey 07733 219 Keep your hand on your wallet. Leon Cooper, 1987 Abstract We show how to estimate (1) the number of functions that can be implemented by a ... | 1987 | 68 |
65 | HIGH ORDER NEURAL NETWORKS FOR EFFICIENT ASSOCIATIVE MEMORY DESIGN I. GUYON·, L. PERSONNAZ·, J. P. NADAL·· and G. DREYFUS· 233 • Ecole Superieure de Physique et de Chimie Industrielles de la Ville de Paris Laboratoire d'Electronique 10, rue Vauquelin 75005 Paris (France) •• Ecole Normale Superie... | 1987 | 69 |
66 | 642 LEARNING BY ST ATE RECURRENCE DETECfION Bruce E. Rosen, James M. Goodwint, and Jacques J. Vidal University of California, Los Angeles, Ca. 90024 ABSTRACT This research investigates a new technique for unsupervised learning of nonlinear control problems. The approach is applied both to Michie and C... | 1987 | 7 |
67 | 184 THE CAPACITY OF THE KANERVA ASSOCIATIVE MEMORY IS EXPONENTIAL P. A. Choul Stanford University. Stanford. CA 94305 ABSTRACT The capacity of an associative memory is defined as the maximum number of vords that can be stored and retrieved reliably by an address vithin a given sphere of attraction.... | 1987 | 70 |
68 | 242 THE SIGMOID NONLINEARITY IN PREPYRIFORM CORTEX Frank H. Eeckman University of California, Berkeley, CA 94720 ABSlRACT We report a study ·on the relationship between EEG amplitude values and unit spike output in the prepyriform cortex of awake and motivated rats. This relationship takes the form... | 1987 | 71 |
69 | 310 PROBABILISTIC CHARACTERIZATION OF NEURAL MODEL COMPUTATIONS Richard M. Golden t University of Pittsburgh, Pittsburgh, Pa. 15260 ABSTRACT Information retrieval in a neural network is viewed as a procedure in which the network computes a "most probable" or MAP estimate of the unknown information.... | 1987 | 72 |
70 | 840 LEARNING IN NETWORKS OF NONDETERMINISTIC ADAPTIVE LOGIC ELEMENTS Richard C. Windecker* AT&T Bell Laboratories, Middletown, NJ 07748 ABSTRACT This paper presents a model of nondeterministic adaptive automata that are constructed from simpler nondeterministic adaptive information processing... | 1987 | 73 |
71 | HIGH DENSITY ASSOCIATIVE MEMORIES! A"'ir Dembo Information Systems Laboratory, Stanford University Stanford, CA 94305 Ofer Zeitouni Laboratory for Information and Decision Systems MIT, Cambridge, MA 02139 ABSTRACT 211 A class of high dens ity assoc iat ive memories is constructed, starting... | 1987 | 74 |
72 | A MEAN FIELD THEORY OF LAYER IV OF VISUAL CORTEX AND ITS APPLICATION TO ARTIFICIAL NEURAL NETWORKS* Christopher L. Scofield Center for Neural Science and Physics Department Brown University Providence, Rhode Island 02912 and Nestor, Inc., 1 Richmond Square, Providence, Rhode Island, 02906. AB... | 1987 | 75 |
73 | NEURAL NETWORKS FOR TEMPLATE MATCHING: APPLICATION TO REAL-TIME CLASSIFICATION OF THE ACTION POTENTIALS OF REAL NEURONS Yiu-fai Wongt, Jashojiban Banikt and James M. Bower! tDivision of Engineering and Applied Science !Division of Biology California Institute of Technology Pasadena, CA 91125 ABS... | 1987 | 76 |
74 | 412 CAPACITY FOR PATTERNS AND SEQUENCES IN KANERVA'S SDM AS COMPARED TO OTHER ASSOCIATIVE MEMORY MODELS James O. Keeler Chemistry Department, Stanford University, Stanford, CA 94305 and RIACS, NASA-AMES 230-5 Moffett Field, CA 94035. e-mail: jdk@hydra.riacs.edu ABSTRACT The information capacity ... | 1987 | 77 |
75 | 338 The Connectivity Analysis of Simple Association - orHow Many Connections Do You Need! Dan Hammerstrom * Oregon Graduate Center, Beaverton, OR 97006 ABSTRACT The efficient realization, using current silicon technology, of Very Large Connection Networks (VLCN) with more than a billion connections... | 1987 | 78 |
76 | 432 Performance Measures for Associative Memories that Learn and Forget Anthony /(uh Department of Electrical Engineering University of Hawaii at Manoa Honolulu HI, 96822 ABSTRACT Recently, many modifications to the McCulloch/Pitts model have been proposed where both learning and forgetting o... | 1987 | 79 |
77 | 554 STABILITY RESULTS FOR NEURAL NETWORKS A. N. Michell, J. A. FarreUi , and W. Porod2 Department of Electrical and Computer Engineering University of Notre Dame Notre Dame, IN 46556 ABSTRACT In the present paper we survey and utilize results from the qualitative theory of large scale interconne... | 1987 | 8 |
78 | 62 Centric Models of the Orientation Map in Primary Visual Cortex William Baxter Department of Computer Science, S.U.N.Y. at Buffalo, NY 14620 Bruce Dow Department of Physiology, S.U.N.Y. at Buffalo, NY 14620 Abstract In the visual cortex of the monkey the horizontal organization of the preferred ... | 1987 | 80 |
79 | 114 A Computer Simulation of Olfactory Cortex With Functional Implications for Storage and Retrieval of Olfactory Information Matthew A. Wilson and James M. Bower Computation and Neural Systems Program Division of Biology, California Institute of Technology, Pasadena, CA 91125 ABSTRACT Based on ana... | 1987 | 81 |
80 | TOWARDS AN ORGANIZING PRINCIPLE FOR A LAYERED PERCEPTUAL NETWORK Ralph Linsker IBM Thomas J. Watson Research Center, Yorktown Heights, NY 10598 Abstract 485 An information-theoretic optimization principle is proposed for the development of each processing stage of a multilayered perceptual network.... | 1987 | 82 |
81 | 592 A Trellis-Structured Neural Network* Thomas Petschet and Bradley W. Dickinson Princeton University, Department of Electrical Engineering Princeton, N J 08544 Abstract We have developed a neural network which consists of cooperatively interconnected Grossberg on-center off-surround subnets and whic... | 1987 | 83 |
82 | 52 Supervised Learning of Probability Distributions by Neural Networks Eric B. Baum Jet Propulsion Laboratory, Pasadena CA 91109 Frank Wilczek t Department of Physics,Harvard University,Cambridge MA 02138 Abstract: We propose that the back propagation algorithm for supervised learning can be gen... | 1987 | 84 |
83 | Stochastic Learning Networks and their Electronic Implementation Joshua Alspector*. Robert B. Allen. Victor Hut. and Srinagesh Satyanarayanat Bell Communications Research. Morristown. NJ 01960 We describe a family of learning algorithms that operate on a recurrent, symmetrically connected. neuromorphic netw... | 1987 | 85 |
84 | CONNECTING TO THE PAST Bruce A. MacDonald, Assistant Professor Knowledge Sciences Laboratory, Computer Science Department The University of Calgary, 2500 University Drive NW Calgary, Alberta T2N IN4 ABSTRACT 505 Recently there has been renewed interest in neural-like processing systems, evidenced f... | 1987 | 86 |
85 | 317 PARTITIONING OF SENSORY DATA BY A CORTICAL NETWORK1 Richard Granger, Jose Ambros-Ingerson, Howard Henry, Gary Lynch Center for the Neurobiology of Learning and Memory University of California Irvine, CA. 91717 SUMMARY To process sensory data, sensory brain areas must preserve information about ... | 1987 | 87 |
86 | 750 A DYNAMICAL APPROACH TO TEMPORAL PATTERN PROCESSING W. Scott Stornetta Stanford University, Physics Department, Stanford, Ca., 94305 Tad Hogg and B. A. Huberman Xerox Palo Alto Research Center, Palo Alto, Ca. 94304 ABSTRACT Recognizing patterns with temporal context is important for such ... | 1987 | 88 |
87 | 348 Minkowski-r Back-Propaaation: Learnine in Connectionist Models with Non-Euclidian Error Silllais Stephen Jose Hanson and David J. Burr Bell Communications Research Morristown, New Jersey 07960 Abstract Many connectionist learning models are implemented using a gradient descent in a least squ... | 1987 | 89 |
88 | 804 INTRODUCTION TO A SYSTEM FOR IMPLEMENTING NEURAL NET CONNECTIONS ON SIMD ARCHITECTURES Sherryl Tomboulian Institute for Computer Applications in Science and Engineering NASA Langley Research Center, Hampton VA 23665 ABSTRACT Neural networks have attracted much interest recently, and using paral... | 1987 | 9 |
89 | 72 ANALYSIS AND COMPARISON OF DIFFERENT LEARNING ALGORITHMS FOR PATTERN ASSOCIATION PROBLEMS J. Bernasconi Brown Boveri Research Center CH-S40S Baden, Switzerland ABSTRACT We investigate the behavior of different learning algorithms for networks of neuron-like units. As test cases we use simple ... | 1987 | 90 |
90 | 410 NEURAL CONTROL OF SENSORY ACQUISITION: THE VESTIBULO-OCULAR REFLEX. Michael G. Paulin, Mark E. Nelson and James M. Bower Division of Biology California Institute of Technology Pasadena, CA 91125 ABSTRACT We present a new hypothesis that the cerebellum plays a key role in actively controlling... | 1988 | 1 |
91 | 232 SPEECH PRODUCTION USING A NEURAL NETWORK WITH A COOPERATIVE LEARNING MECHANISM Mitsuo Komura Akio Tanaka International Institute for Advanced Study of Social Information Science, Fujitsu Limited 140 Miyamoto, Numazu-shi Shizuoka, 410-03 Japan ABSTRACT We propose a new neural network mo... | 1988 | 10 |
92 | 20 ASSOCIATIVE LEARNING VIA INHIBITORY SEARCH David H. Ackley Bell Communications Research Cognitive Science Research Group ABSTRACT ALVIS is a reinforcement-based connectionist architecture that learns associative maps in continuous multidimensional environments. The discovered locations of pos... | 1988 | 11 |
93 | 748 Performance of a Stochastic Learning Microchip • Joshua Alspector, Bhusan Gupta, and Robert B. Allen Bellcore, Morristown, NJ 07960 We have fabricated a test chip in 2 micron CMOS that can perform supervised learning in a manner similar to the Boltzmann machine. Patterns can be presented to it ... | 1988 | 12 |
94 | SONG LEARNING IN BIRDS M. Konishi Division of Biology California Institute of Technology Birds sing to communicate. Male birds use song to advertise their territories and attract females. Each bird species has a unique song or set of songs. Song conveys both species and individual identity. In most sp... | 1988 | 13 |
95 | 384 MODELING SMALL OSCILLATING BIOLOGICAL NETWORKS IN ANALOG VLSI Sylvie Ryckebusch, James M. Bower, and Carver Mead California Instit ute of Technology Pasadena, CA 91125 ABSTRACT We have used analog VLSI technology to model a class of small oscillating biological neural circuits known as central ... | 1988 | 14 |
96 | COMPARING BIASES FOR MINIMAL NETWORK CONSTRUCTION WITH BACK-PROPAGATION Lorien Y. Pratt Rutgers University Stephen Jo~ Hansont Bell Communications Research Morristown. New Jersey 07960 New Brunswick. New Jersey 08903 ABSTRACT Rumelhart (1987). has proposed a method for choosing minimal or ... | 1988 | 15 |
97 | WHAT SIZE NET GIVES VALID GENERALIZATION?* Eric B. Baum Department of Physics Princeton University Princeton NJ 08540 David Haussler Computer and Information Science University of California Santa Cruz, CA 95064 ABSTRACT We address the question of when a network can be expected to ge... | 1988 | 16 |
98 | 678 A LOW-POWER CMOS CIRCUIT WHICH EMULATES TEMWORALELECTIDCALPROPERTIES OF NEURONS Jack L. Meador and Clint S. Cole Electrical and Computer Engineering Dept. Washington State University Pullman WA. 99164-2752 ABSTRACf This paper describes a CMOS artificial neuron. The circuit is directly der... | 1988 | 17 |
99 | LINEAR LEARNING: LANDSCAPES AND ALGORITHMS Pierre Baldi Jet Propulsion Laboratory California Institute of Technology Pasadena, CA 91109 What follows extends some of our results of [1] on learning from examples in layered feed-forward networks of linear units. In particular we examine what happens when th... | 1988 | 18 |
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