Neural Computation

Volume 21 : Issue 4

Table of Contents

• Spiking Neurons Can Learn to Solve Information Bottleneck Problems and Extract Independent Components 
  Stefan Klampfl, Robert Legenstein and Wolfgang Maass
  Page 911
• Measure of Correlation Orthogonal to Change in Firing Rate 
  Shun-ichi Amari
  Page 960
• Determining Burst Firing Time Distributions from Multiple Spike Trains 
  Luis F. Lago-Fernndez, Attila Szcs and Pablo Varona
  Page 973
• Wide-Band Information Transmission at the Calyx of Held 
  Zhijun Yang, Matthias H. Hennig, Michael Postlethwaite, Ian D. Forsythe and Bruce P. Graham
  Page 991
• Chemosensor-Driven Artificial Antennal Lobe Transient Dynamics Enable Fast Recognition and Working Memory 
  Mehmet K. Muezzinoglu, Ramon Huerta, Henry D. I. Abarbanel, Margaret A. Ryan and Mikhail I. Rabinovich
  Page 1018
• Recurrent Infomax Generates Cell Assemblies, Neuronal Avalanches, and Simple Cell-Like Selectivity 
  Takuma Tanaka, Takeshi Kaneko and Toshio Aoyagi
  Page 1038
• Accelerating Event-Driven Simulation of Spiking Neurons with Multiple Synaptic Time Constants 
  Michiel D'Haene, Benjamin Schrauwen, Jan Van Campenhout and Dirk Stroobandt
  Page 1068
• Delays and Oscillations in Networks of Spiking Neurons: A Two-Timescale Analysis 
  Dotan Di Castro, Ron Meir and Irad Yavneh
  Page 1100
• Stability of Localized Patterns in Neural Fields 
  Konstantin Doubrovinski and J. Michael Herrmann
  Page 1125
• Bayesian k-Means as a Maximization-Expectation Algorithm 
  Kenichi Kurihara and Max Welling
  Page 1145
• On the Asymptotic Equivalence Between Differential Hebbian and Temporal Difference Learning 
  Christoph Kolodziejski, Bernd Porr and Florentin Wrgtter
  Page 1173