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Issues

Volume 12, Issue 4
April 2000
Issue Cover
All Issues
ISSN 0899-7667
EISSN 1530-888X
In this Issue

Articles

Minimizing Binding Errors Using Learned Conjunctive Features
Bartlett W. Mel, József Fiser
Neural Computation (2000) 12 (4): 731–762.
View articletitled, Minimizing Binding Errors Using Learned Conjunctive Features
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The Multifractal Structure of Contrast Changes in Natural Images: From Sharp Edges to Textures
Antonio Turiel, Néstor Parga
Neural Computation (2000) 12 (4): 763–793.
View articletitled, The Multifractal Structure of Contrast Changes in Natural Images: From Sharp Edges to Textures
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Note

Exponential or Polynomial Learning Curves? Case-Based Studies
Hanzhong Gu, Haruhisa Takahashi
Neural Computation (2000) 12 (4): 795–809.
View articletitled, Exponential or Polynomial Learning Curves? Case-Based Studies
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Letters

Training Feedforward Neural Networks with Gain Constraints
Eric Hartman
Neural Computation (2000) 12 (4): 811–829.
View articletitled, Training Feedforward Neural Networks with Gain Constraints
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Variational Learning for Switching State-Space Models
Zoubin Ghahramani, Geoffrey E. Hinton
Neural Computation (2000) 12 (4): 831–864.
View articletitled, Variational Learning for Switching State-Space Models
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Retrieval Properties of a Hopfield Model with Random Asymmetric Interactions
Zhang Chengxiang, Chandan Dasgupta, Manoranjan P. Singh
Neural Computation (2000) 12 (4): 865–880.
View articletitled, Retrieval Properties of a Hopfield Model with Random Asymmetric Interactions
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On “Natural” Learning and Pruning in Multilayered Perceptrons
Tom Heskes
Neural Computation (2000) 12 (4): 881–901.
View articletitled, On “Natural” Learning and Pruning in Multilayered Perceptrons
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Synthesis of Generalized Algorithms for the Fast Computation of Synaptic Conductances with Markov Kinetic Models in Large Network Simulations
Michele Giugliano
Neural Computation (2000) 12 (4): 903–931.
View articletitled, Synthesis of Generalized Algorithms for the Fast Computation of Synaptic Conductances with Markov Kinetic Models in Large Network Simulations
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Hierarchical Bayesian Models for Regularization in Sequential Learning
J. F. G. de Freitas, M. Niranjan, A. H. Gee
Neural Computation (2000) 12 (4): 933–953.
View articletitled, Hierarchical Bayesian Models for Regularization in Sequential Learning
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Sequential Monte Carlo Methods to Train Neural Network Models
J. F. G. de Freitas, M. Niranjan, A. H. Gee, A. Doucet
Neural Computation (2000) 12 (4): 955–993.
View articletitled, Sequential Monte Carlo Methods to Train Neural Network Models
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