Neural Computation

Volume 22 : Issue 8

Table of Contents

• A Spiking Neuron as Information Bottleneck 
  Lars Buesing and Wolfgang Maass
  Page 1961
• Estimating a State-Space Model from Point Process Observations: A Note on Convergence 
  Ke Yuan and Mahesan Niranjan
  Page 1993
• A Computationally Efficient Method for Nonparametric Modeling of Neural Spiking Activity with Point Processes 
  Todd P. Coleman and Sridevi S. Sarma
  Page 2002
• How Synaptic Release Probability Shapes Neuronal Transmission: Information-Theoretic Analysis in a Cerebellar Granule Cell 
  Angelo Arleo, Thierry Nieus, Michele Bezzi, Anna D'Errico, Egidio D'Angelo and Olivier J.-M. D. Coenen
  Page 2031
• Reconciling the STDP and BCM Models of Synaptic Plasticity in a Spiking Recurrent Neural Network 
  Daniel Bush, Andrew Philippides, Phil Husbands and Michael O'Shea
  Page 2059
• Multilayer Processing of Spatiotemporal Spike Patterns in a Neuron with Active Dendrites 
  Yingxue Wang and Shih-Chii Liu
  Page 2086
• Spectral Method and High-Order Finite Differences for the Nonlinear Cable Equation 
  Ahmet Omurtag and William W. Lytton
  Page 2113
• Competitive Layer Model of Discrete-Time Recurrent Neural Networks with LT Neurons 
  Wei Zhou and Jacek M. Zurada
  Page 2137
• Modeling Stereopsis via Markov Random Field 
  Yansheng Ming and Zhanyi Hu
  Page 2161
• Deep Belief Networks Are Compact Universal Approximators 
  Nicolas Le Roux and Yoshua Bengio
  Page 2192
• Sample-Spacings-Based Density and Entropy Estimators for Spherically Invariant Multidimensional Data 
  Intae Lee
  Page 2208