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Issues

Volume 17, Issue 7
July 2005
Issue Cover
All Issues
ISSN 0899-7667
EISSN 1530-888X
In this Issue

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Categorization of Neural Excitability Using Threshold Models
A. Tonnelier
Neural Computation (2005) 17 (7): 1447–1455.
View articletitled, Categorization of Neural Excitability Using Threshold Models
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Letters

Theory of the Snowflake Plot and Its Relations to Higher-Order Analysis Methods
Gabriela Czanner, Sonja Grün, Satish Iyengar
Neural Computation (2005) 17 (7): 1456–1479.
View articletitled, Theory of the Snowflake Plot and Its Relations to Higher-Order Analysis Methods
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Asymptotic Theory of Information-Theoretic Experimental Design
Liam Paninski
Neural Computation (2005) 17 (7): 1480–1507.
View articletitled, Asymptotic Theory of Information-Theoretic Experimental Design
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Maximum Likelihood Set for Estimating a Probability Mass Function
Bruno M. Jedynak, Sanjeev Khudanpur
Neural Computation (2005) 17 (7): 1508–1530.
View articletitled, Maximum Likelihood Set for Estimating a Probability Mass Function
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Estimating Entropy Rates with Bayesian Confidence Intervals
Matthew B. Kennel, Jonathon Shlens, Henry D. I. Abarbanel, E. J. Chichilnisky
Neural Computation (2005) 17 (7): 1531–1576.
View articletitled, Estimating Entropy Rates with Bayesian Confidence Intervals
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Spike Timing Precision and Neural Error Correction: Local Behavior
Michael Stiber
Neural Computation (2005) 17 (7): 1577–1601.
View articletitled, Spike Timing Precision and Neural Error Correction: Local Behavior
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A New Approach to Spatial Covariance Modeling of Functional Brain Imaging Data: Ordinal Trend Analysis
Christian Habeck, John W. Krakauer, Claude Ghez, Harold A. Sackeim, David Eidelberg ...
Neural Computation (2005) 17 (7): 1602–1645.
View articletitled, A New Approach to Spatial Covariance Modeling of Functional Brain Imaging Data: Ordinal Trend Analysis
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Investigating the Fault Tolerance of Neural Networks
Elko B. Tchernev, Rory G. Mulvaney, Dhananjay S. Phatak
Neural Computation (2005) 17 (7): 1646–1664.
View articletitled, Investigating the Fault Tolerance of Neural Networks
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