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Nonlinear Neural Networks: Principles, Mechanisms, and Architectures
, 1988
"... An historical discussion is provided of the intellectual trends that caused nineteenth century interdisciplinary studies of physics and psychobiology by leading scientists such as Helmholtz, Maxwell, and Mach to splinter into separate twentiethcentury scientific movements. The nonlinear, nonstatio ..."
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Cited by 181 (20 self)
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An historical discussion is provided of the intellectual trends that caused nineteenth century interdisciplinary studies of physics and psychobiology by leading scientists such as Helmholtz, Maxwell, and Mach to splinter into separate twentiethcentury scientific movements. The nonlinear, nonstationary, and nonlocal nature of behavioral and brain data are emphasized. Three sources of contemporary neural network researchthe binary, linear, and continuousnonlinear modelsare noted. The remainder of the article describes results about continuousnonlinear models: Many models of contentaddressable memory are shown to be special cases of the CohenGrossberg model and global Liapunov function, including the additive, brainstateinabox, McCullochPitts, Boltzmann machine, HartlineRatliffMillet; shunting, maskingfield, bidirectional associative memory, VolterraLotka, GilpinAyala, and EigenSchuster models. A Liapunov functional method is described for proving global limit or oscillation theorems for nonlinear competitive systems when their decision schemes are globally consistent or inconsistent, respectively. The former case is illustrated by a model of a globally stable economic market, and the latter case is illustrated by a model of the voting paradox. Key properties of shunting competitive feedback networks are summarized, including the role of sigmoid signalling, automatic gain control, competitive choice and quantization, tunable filtering, total activity normalization, and noise suppression in pattern transformation and memory storage applications. Connections to models of competitive learning, vector quantization, and categorical perception are noted. Adaptive resonance
Bidirectional Associative Memories
 IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS
, 1988
"... Stability and encoding properties of twolayer nonlinear feedback neural networks are examined. Bidirectionality, forward and backard information flow, is introduced in neural nets to produce twoway associative search for stored associations (A, B, ). Passing information through M gives one directi ..."
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Cited by 155 (3 self)
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Stability and encoding properties of twolayer nonlinear feedback neural networks are examined. Bidirectionality, forward and backard information flow, is introduced in neural nets to produce twoway associative search for stored associations (A, B, ). Passing information through M gives one direction; passing it through its transpose M r gives the other. A bidirectional associative memory. (BAM) behaves as a hetero associative content addressable memory (CAM), storing and recalling the vector pairs (A1, Bi),..,(Am Bin) , where .4 {0,1}"and B We prove that every nbyp matrix M is a bidirectionally stable heteroas sociative CAM for both binary/bipolar and continuous neurons a, and hi. When the BAM neurons are activated, the network quickly evolves to a stable state of twopattern reverberation, or resonance. The stable reverberation corresponds to a system energy local minimum. Heteroassociafive inlormation is encoded iu a BAM by summing correlation matrices. The BAM storage capact .ty for reliable recall is roughly m < niin(n, p). No more heteroassociafive pairs can be 'reliably stored and recalled than the lesser of the dimensions of the pattern spaces (0,1 }"and 0,1 } P. The Appendix shos that it is better on average to use bipolar { 1,i} coding than binary. {0,1 } coding of heteroassociative pairs (.4, B,). BAM encoding and decoding are combined in the adaptive BAM, which extends global bidirectional stabflit), to realtime unsupervised learning. Temporal patterns (AE,., A,,) are represented as ordered lists of binary/bipolar vectors and stored in a temporal associative memory (TAM) nby matrix M as a limit cycle of the dynamical system. Forward recall proceeds through M, backward recall through M r . Temporal patterns are stored by summing contiguous bipolar...
Implementation Of Neural Networks On Parallel Architectures
, 1992
"... xi 1 Introduction 1 1.1 Problem Statement : : : : : : : : : : : : : : : : : : : : : : : : : 6 1.2 The Neuron : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 7 1.2.1 Biological Model : : : : : : : : : : : : : : : : : : : : : : 7 1.2.2 Computational Model : : : : : : : : : : : : : : : : : : ..."
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Cited by 9 (6 self)
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xi 1 Introduction 1 1.1 Problem Statement : : : : : : : : : : : : : : : : : : : : : : : : : 6 1.2 The Neuron : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 7 1.2.1 Biological Model : : : : : : : : : : : : : : : : : : : : : : 7 1.2.2 Computational Model : : : : : : : : : : : : : : : : : : : 9 1.3 Implementation Technologies : : : : : : : : : : : : : : : : : : : : 11 1.4 State of the Art : : : : : : : : : : : : : : : : : : : : : : : : : : : 14 1.5 Summary of Results : : : : : : : : : : : : : : : : : : : : : : : : 16 2 Implementation of Neural Models with Static Links 19 2.1 ANN Models with Static Links : : : : : : : : : : : : : : : : : : 20 2.1.1 The Hopfield Model : : : : : : : : : : : : : : : : : : : : : 21 2.1.2 The Perceptron Model : : : : : : : : : : : : : : : : : : : 23 2.1.3 The MultiLayer Model : : : : : : : : : : : : : : : : : : : 24 2.2 Basic Computational Requirements : : : : : : : : : : : : : : : : 25 2.2.1 Search Phase Computations : : : : : : : : : : : : : : ...
The Utility Problem in Case Based Reasoning
, 1993
"... ed in CaseBased Reasoning: Papers from the 1993 Workshop, July 1112, Washington, D.C., Technical Report WS9301, AAAI Press THE UTILITY PROBLEM IN CASE BASED REASONING ANTHONY G. FRANCIS ASHWIN RAM College of Computing College of Computing Georgia Institute of Technology Georgia Institute of Te ..."
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Cited by 1 (0 self)
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ed in CaseBased Reasoning: Papers from the 1993 Workshop, July 1112, Washington, D.C., Technical Report WS9301, AAAI Press THE UTILITY PROBLEM IN CASE BASED REASONING ANTHONY G. FRANCIS ASHWIN RAM College of Computing College of Computing Georgia Institute of Technology Georgia Institute of Technology Atlanta, Georgia 303320280 Atlanta, Georgia 303320280 (404) 3514574 (404) 8539372 centaur@cc.gatech.edu ashwin@cc.gatech.edu ABSTRACT Casebased reasoning systems may suffer from the utility problem, which occurs when knowledge learned in an attempt to improve a system's performance degrades performance instead. One of the primary causes of the utility problem is the slowdown of conventional memories as the number of stored items increases. Unrestricted learning algorithms can swamp their memory system, causing the system to slow down more than the average speedup provided by individual learned rules. Massive parallelism is often offered as a solution to this problem. However,...
Optically Connected Multiprocessors for Simulating Artificial Neural Networks
 In SPIE Proceedings vol. 882
, 1988
"... This paper investigates the architectural requirements in simulating large neural networks using a highly parallel multiprocessor with distributed memory and optical interconnects. First, we model the structure of a neural network and the functional behavior of individual cells. These models are use ..."
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Cited by 1 (1 self)
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This paper investigates the architectural requirements in simulating large neural networks using a highly parallel multiprocessor with distributed memory and optical interconnects. First, we model the structure of a neural network and the functional behavior of individual cells. These models are used to estimate the volume of messages that need to be exchanged among physical processors to simulate the weighted connections of the neural network. The distributed processor/memory organization is tailored to an electronic implementation for greater versatility and flexibility. Optical interconnects are used to satisfy the interprocessor communication bandwidth demands. The hybrid implementation attempts to balance the processing, memory and bandwidth demands in simulating asynchronous, valuepassing models for cooperative parallel computation with selflearning capabilities. 1. INTRODUCTION Connectionist models of computation 1 have been in the limelight as promising alternatives to tr...