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Volume 22, Issue 2

February 2010

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ISSN 0899-7667

EISSN 1530-888X

In this Issue

Review
Article
Letters

Review

Memory Capacities for Synaptic and Structural Plasticity

Andreas Knoblauch, Günther Palm, Friedrich T. Sommer

Neural Computation (2010) 22 (2): 289–341.

Abstract

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Article

Derivatives of Logarithmic Stationary Distributions for Policy Gradient Reinforcement Learning

Tetsuro Morimura, Eiji Uchibe, Junichiro Yoshimoto, Jan Peters, Kenji Doya

Neural Computation (2010) 22 (2): 342–376.

Abstract

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Letters

Systematic Fluctuation Expansion for Neural Network Activity Equations

Michael A. Buice, Jack D. Cowan, Carson C. Chow

Neural Computation (2010) 22 (2): 377–426.

Abstract

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Cross-Correlations in High-Conductance States of a Model Cortical Network

John Hertz

Neural Computation (2010) 22 (2): 427–447.

Abstract

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Dynamics and Robustness of Familiarity Memory

J. M. Cortes, A. Greve, A. B. Barrett, M. C. W. van Rossum

Neural Computation (2010) 22 (2): 448–466.

Abstract

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Supervised Learning in Spiking Neural Networks with ReSuMe: Sequence Learning, Classification, and Spike Shifting

Filip Ponulak, Andrzej Kasiński

Neural Computation (2010) 22 (2): 467–510.

Abstract

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Convolutional Networks Can Learn to Generate Affinity Graphs for Image Segmentation

Srinivas C. Turaga, Joseph F. Murray, Viren Jain, Fabian Roth, Moritz Helmstaedter ...

Neural Computation (2010) 22 (2): 511–538.

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On the Effects of Signal Acuity in a Multi-Alternative Model of Decision Making

Tyler McMillen, Sam Behseta

Neural Computation (2010) 22 (2): 539–580.

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Review

Memory Capacities for Synaptic and Structural Plasticity

Article

Derivatives of Logarithmic Stationary Distributions for Policy Gradient Reinforcement Learning

Letters

Systematic Fluctuation Expansion for Neural Network Activity Equations

Cross-Correlations in High-Conductance States of a Model Cortical Network

Dynamics and Robustness of Familiarity Memory

Supervised Learning in Spiking Neural Networks with ReSuMe: Sequence Learning, Classification, and Spike Shifting

Convolutional Networks Can Learn to Generate Affinity Graphs for Image Segmentation

On the Effects of Signal Acuity in a Multi-Alternative Model of Decision Making

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