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Volume 31, Issue 6
June 2019
Issue Cover
All Issues
ISSN 0899-7667
EISSN 1530-888X
In this Issue

Article

Quantifying Information Conveyed by Large Neuronal Populations
John A. Berkowitz, Tatyana O. Sharpee
Neural Computation (2019) 31 (6): 1015–1047.
View articletitled, Quantifying Information Conveyed by Large Neuronal Populations
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Letters

Controlling Complexity of Cerebral Cortex Simulations—I: CxSystem, a Flexible Cortical Simulation Framework
Vafa Andalibi, Henri Hokkanen, Simo Vanni
Neural Computation (2019) 31 (6): 1048–1065.
View articletitled, Controlling Complexity of Cerebral Cortex Simulations—I: CxSystem, a Flexible Cortical Simulation Framework
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Controlling Complexity of Cerebral Cortex Simulations—II: Streamlined Microcircuits
Henri Hokkanen, Vafa Andalibi, Simo Vanni
Neural Computation (2019) 31 (6): 1066–1084.
View articletitled, Controlling Complexity of Cerebral Cortex Simulations—II: Streamlined Microcircuits
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Decoding Movements from Cortical Ensemble Activity Using a Long Short-Term Memory Recurrent Network
Po-He Tseng, Núria Armengol Urpi, Mikhail Lebedev, Miguel Nicolelis
Neural Computation (2019) 31 (6): 1085–1113.
View articletitled, Decoding Movements from Cortical Ensemble Activity Using a Long Short-Term Memory Recurrent Network
Supplementary data
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Asynchronous Event-Based Motion Processing: From Visual Events to Probabilistic Sensory Representation
Mina A. Khoei, Sio-hoi Ieng, Ryad Benosman
Neural Computation (2019) 31 (6): 1114–1138.
View articletitled, Asynchronous Event-Based Motion Processing: From Visual Events to Probabilistic Sensory Representation
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A Geometrical Analysis of Global Stability in Trained Feedback Networks
Francesca Mastrogiuseppe, Srdjan Ostojic
Neural Computation (2019) 31 (6): 1139–1182.
View articletitled, A Geometrical Analysis of Global Stability in Trained Feedback Networks
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Learning Moral Graphs in Construction of High-Dimensional Bayesian Networks for Mixed Data
Suwa Xu, Bochao Jia, Faming Liang
Neural Computation (2019) 31 (6): 1183–1214.
View articletitled, Learning Moral Graphs in Construction of High-Dimensional Bayesian Networks for Mixed Data
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Improving the Antinoise Ability of DNNs via a Bio-Inspired Noise Adaptive Activation Function Rand Softplus
Yunhua Chen, Yingchao Mai, Jinsheng Xiao, Ling Zhang
Neural Computation (2019) 31 (6): 1215–1233.
View articletitled, Improving the Antinoise Ability of DNNs via a Bio-Inspired Noise Adaptive Activation Function Rand Softplus
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