Neural Computation

Volume 21 : Issue 2

Table of Contents

• A Spiking Neural Network Model of an Actor-Critic Learning Agent 
  Wiebke Potjans, Abigail Morrison and Markus Diesmann
  Page 301
• A Gradient Learning Rule for the Tempotron 
  Robert Urbanczik and Walter Senn
  Page 340
• Simplicity and Efficiency of Integrate-and-Fire Neuron Models 
  Hans E. Plesser and Markus Diesmann
  Page 353
• Spike Train Statistics and Dynamics with Synaptic Input from any Renewal Process: A Population Density Approach 
  Cheng Ly and Daniel Tranchina
  Page 360
• Generating Spike Trains with Specified Correlation Coefficients 
  Jakob H. Macke, Philipp Berens, Alexander S. Ecker, Andreas S. Tolias and Matthias Bethge
  Page 397
• A Reproducing Kernel Hilbert Space Framework for Spike Train Signal Processing 
  Antnio R. C. Paiva, Il Park and Jos C. Prncipe
  Page 424
• Identifying Functional Connectivity in Large-Scale Neural Ensemble Recordings: A Multiscale Data Mining Approach 
  Seif Eldawlatly, Rong Jin and Karim G. Oweiss
  Page 450
• State-Dependent Computation Using Coupled Recurrent Networks 
  Ueli Rutishauser and Rodney J. Douglas
  Page 478
• The Problem of Rapid Variable Creation 
  Robert F. Hadley
  Page 510
• Nonparametric Conditional Density Estimation Using Piecewise-Linear Solution Path of Kernel Quantile Regression 
  Ichiro Takeuchi, Kaname Nomura and Takafumi Kanamori
  Page 533
• Arbitrary Norm Support Vector Machines 
  Kaizhu Huang, Danian Zheng, Irwin King and Michael R. Lyu
  Page 560
• Sequential Triangle Strip Generator Based on Hopfield Networks 
  Ji ma and Radim Lnnika
  Page 583