Neural Computation

Volume 9 : Issue 2

Table of Contents

• Probabilistic Independence Networks for Hidden Markov Probability Models 
  Padhraic Smyth, David Heckerman and Michael I. Jordan
  Page 227
• Using Expectation-Maximization for Reinforcement Learning 
  Peter Dayan and Geoffrey E. Hinton
  Page 271
• Computing with the Leaky Integrate-and-Fire Neuron: Logarithmic Computation and Multiplication 
  Doron Tal and Eric L. Schwartz
  Page 305
• Effect of Delay on the Boundary of the Basin of Attraction in a Self-Excited Single Graded-Response Neuron 
  K. Pakdaman, C. P. Malta, C. Grotta-Ragazzo and J.-F. Vibert
  Page 319
• Shattering All Sets of k Points in General Position Requires (k 1)/2 Parameters 
  Eduardo D. Sontag
  Page 337
• Statistical Inference, Occam's Razor, and Statistical Mechanics on the Space of Probability Distributions 
  Vijay Balasubramanian
  Page 349
• Bias/Variance Analyses of Mixtures-of-Experts Architectures 
  Robert A. Jacobs
  Page 369
• The Behavior of Forgetting Learning in Bidirectional Associative Memory 
  Chi Sing Leung and Lai Wan Chan
  Page 385
• Adaptive Encoding Strongly Improves Function Approximation with CMAC 
  Martin Eldracher, Alexander Staller and Ren Pompl
  Page 403
• An Analog Memory Circuit for Spiking Silicon Neurons 
  John G. Elias, David P. M. Northmore and Wayne Westerman
  Page 419
• Average-Case Learning Curves for Radial Basis Function Networks 
  Sean B. Holden and Mahesan Niranjan
  Page 441
• A Sequential Learning Scheme for Function Approximation Using Minimal Radial Basis Function Neural Networks 
  Lu Yingwei, N. Sundararajan and P. Saratchandran
  Page 461