Neural Computation, Volume 22

Volume 22, Number 1, January 2010

• Christopher DiMattina, Kechen Zhang:
  How to Modify a Neural Network Gradually Without Changing Its Input-Output Functionality. 1-47
  

• Daniel K. Wójcik, Szymon Leski:
  Current Source Density Reconstruction from Incomplete Data. 48-60
  

• Sean Byrnes, Anthony N. Burkitt, David B. Grayden, Hamish Meffin:
  Spiking Neuron Model for Temporal Sequence Recognition. 61-93
  
• Lars Wolff, Benjamin Lindner:
  Mean, Variance, and Autocorrelation of Subthreshold Potential Fluctuations Driven by Filtered Conductance Shot Noise. 94-120
  
• Robert Haslinger, Kristina Lisa Klinkner, Cosma Rohilla Shalizi:
  The Computational Structure of Spike Trains. 121-157
  
• Seif Eldawlatly, Yang Zhou, Rong Jin, Karim G. Oweiss:
  On the Use of Dynamic Bayesian Networks in Reconstructing Functional Neuronal Networks from Spike Train Ensembles. 158-189
  
• Elisa Magosso, Melissa Zavaglia, Andrea Serino, Giuseppe Di Pellegrino, Mauro Ursino:
  Visuotactile Representation of Peripersonal Space: A Neural Network Study. 190-243
  
• Terry Elliott:
  Discrete States of Synaptic Strength in a Stochastic Model of Spike-Timing-Dependent Plasticity. 244-272
  
• Florian Landis, Thomas Ott, Ruedi Stoop:
  Hebbian Self-Organizing Integrate-and-Fire Networks for Data Clustering. 273-288
  

Volume 22, Number 2, February 2010

• Andreas Knoblauch, Günther Palm, Friedrich T. Sommer:
  Memory Capacities for Synaptic and Structural Plasticity. 289-341
  

• Tetsuro Morimura, Eiji Uchibe, Junichiro Yoshimoto, Jan Peters, Kenji Doya:
  Derivatives of Logarithmic Stationary Distributions for Policy Gradient Reinforcement Learning. 342-376
  

• Michael A. Buice, Jack D. Cowan, Carson C. Chow:
  Systematic Fluctuation Expansion for Neural Network Activity Equations. 377-426
  
• John Hertz:
  Cross-Correlations in High-Conductance States of a Model Cortical Network. 427-447
  
• Jesús M. Cortés, Andrea Greve, Adam B. Barrett, Mark C. W. van Rossum:
  Dynamics and Robustness of Familiarity Memory. 448-466
  
• Filip Ponulak, Andrzej J. Kasinski:
  Supervised Learning in Spiking Neural Networks with ReSuMe: Sequence Learning, Classification, and Spike Shifting. 467-510
  
• Srinivas C. Turaga, Joseph F. Murray, Viren Jain, Fabian Roth, Moritz Helmstaedter, Kevin L. Briggman, Winfried Denk, H. Sebastian Seung:
  Convolutional Networks Can Learn to Generate Affinity Graphs for Image Segmentation. 511-538
  
• Tyler McMillen, Sam Behseta:
  On the Effects of Signal Acuity in a Multi-Alternative Model of Decision Making. 539-580
  

Volume 22, Number 3, March 2010

• Michael Famulare, Adrienne L. Fairhall:
  Feature Selection in Simple Neurons: How Coding Depends on Spiking Dynamics. 581-598
  
• Jan A. Freund, Alexander Nikitin, Nigel G. Stocks:
  Phase Locking Below Rate Threshold in Noisy Model Neurons. 599-620
  
• Bryan P. Tripp, Chris Eliasmith:
  Population Models of Temporal Differentiation. 621-659
  
• Yali Amit, Yibi Huang:
  Precise Capacity Analysis in Binary Networks with Multiple Coding Level Inputs. 660-688
  
• Vilson L. DalleMole, Aluizio Fausto Ribeiro Araújo:
  Growing Self-Organizing Surface Map: Learning a Surface Topology from a Point Cloud. 689-729
  
• Yiwen Wang, Bertram E. Shi:
  Autonomous Development of Vergence Control Driven by Disparity Energy Neuron Populations. 730-751
  
• C. C. Alan Fung, K. Y. Michael Wong, Si Wu:
  A Moving Bump in a Continuous Manifold: A Comprehensive Study of the Tracking Dynamics of Continuous Attractor Neural Networks. 752-792
  
• Giorgio Gnecco, Marcello Sanguineti:
  Regularization Techniques and Suboptimal Solutions to Optimization Problems in Learning from Data. 793-829
  

Volume 22, Number 4, April 2010

• Jo-Anne Ting, Aaron D'Souza, Sethu Vijayakumar, Stefan Schaal:
  Efficient Learning and Feature Selection in High-Dimensional Regression. 831-886
  
• Daniel R. Cavagnaro, Jay I. Myung, Mark A. Pitt, Janne V. Kujala:
  Adaptive Design Optimization: A Mutual Information-Based Approach to Model Discrimination in Cognitive Science. 887-905
  
• Thomas Deneux, Olivier D. Faugeras:
  EEG-fMRI Fusion of Paradigm-Free Activity Using Kalman Filtering. 906-948
  
• Rohit Gandrakota, V. S. Chakravarthy, Ranjan K. Pradhan:
  A Model of Indispensability of a Large Glial Layer in Cerebrovascular Circulation. 949-968
  
• Roberto C. Sotero, Ramón Martínez-Cancino:
  Dynamical Mean Field Model of a Neural-Glial Mass. 969-997
  
• Tiger W. Lin, George N. Reeke Jr.:
  A Continuous Entropy Rate Estimator for Spike Trains Using a K-Means-Based Context Tree. 998-1024
  
• P. S. Sastry, K. P. Unnikrishnan:
  Conditional Probability-Based Significance Tests for Sequential Patterns in Multineuronal Spike Trains. 1025-1059
  
• Huijuan Fang, Yongji Wang, Jiping He:
  Spiking Neural Networks for Cortical Neuronal Spike Train Decoding. 1060-1085
  
• Jen-Yung Chen:
  A Simulation Study Investigating the Impact of Dendritic Morphology and Synaptic Topology on Neuronal Firing Patterns. 1086-1111
  

Volume 22, Number 5, May 2010

• Jiaxiang Zhang, Rafal Bogacz:
  Optimal Decision Making on the Basis of Evidence Represented in Spike Trains. 1113-1148
  

• Tobias Larsen, David S. Leslie, Edmund J. Collins, Rafal Bogacz:
  Posterior Weighted Reinforcement Learning with State Uncertainty. 1149-1179
  
• Terry Elliott:
  A Non-Markovian Random Walk Underlies a Stochastic Model of Spike-Timing-Dependent Plasticity. 1180-1230
  
• Mengyuan Zhao, Satish Iyengar:
  Nonconvergence in Logistic and Poisson Models for Neural Spiking. 1231-1244
  
• Sidney R. Lehky:
  Decoding Poisson Spike Trains by Gaussian Filtering. 1245-1271
  
• Lars Büsing, Benjamin Schrauwen, Robert A. Legenstein:
  Connectivity, Dynamics, and Memory in Reservoir Computing with Binary and Analog Neurons. 1272-1311
  
• Samat Moldakarimov, Maxim Bazhenov, Terrence J. Sejnowski:
  Representation Sharpening Can Explain Perceptual Priming. 1312-1332
  
• Xiaolin Hu, Bo Zhang:
  A Gaussian Attractor Network for Memory and Recognition with Experience-Dependent Learning. 1333-1357
  
• Osamu Hoshino:
  Alteration of Ambient GABA by Phasic and Tonic Neuronal Activation. 1358-1382
  
• Takashi Kanamaru, Kazuyuki Aihara:
  Roles of Inhibitory Neurons in Rewiring-Induced Synchronization in Pulse-Coupled Neural Networks. 1383-1398
  

Volume 22, Number 6, June 2010

• Michael Pfeiffer, Bernhard Nessler, Rodney J. Douglas, Wolfgang Maass:
  Reward-Modulated Hebbian Learning of Decision Making. 1399-1444
  

• A. Scaglione, Karen A. Moxon, G. Foffani:
  General Poisson Exact Breakdown of the Mutual Information to Study the Role of Correlations in Populations of Neurons. 1445-1467
  
• Michiel D'Haene, Benjamin Schrauwen:
  Fast and Exact Simulation Methods Applied on a Broad Range of Neuron Models. 1468-1472
  

• Roland Memisevic, Geoffrey E. Hinton:
  Learning to Represent Spatial Transformations with Factored Higher-Order Boltzmann Machines. 1473-1492
  
• Felix Creutzig, Jan Benda, Sandra Wohlgemuth, Andreas Stumpner, Bernhard Ronacher, Andreas V. M. Herz:
  Timescale-Invariant Pattern Recognition by Feedforward Inhibition and Parallel Signal Processing. 1493-1510
  
• William H. Alexander, Joshua W. Brown:
  Hyperbolically Discounted Temporal Difference Learning. 1511-1527
  
• Rubén Moreno-Bote, Néstor Parga:
  Response of Integrate-and-Fire Neurons to Noisy Inputs Filtered by Synapses with Arbitrary Timescales: Firing Rate and Correlations. 1528-1572
  
• Arturo Berrones:
  Bayesian Inference Based on Stationary Fokker-Planck Sampling. 1573-1596
  
• Pengsheng Zheng, Wansheng Tang, Jianxiong Zhang:
  Efficient Continuous-Time Asymmetric Hopfield Networks for Memory Retrieval. 1597-1614
  
• Jaehyung Lee, Soo-Young Lee:
  Learning the Dynamical System Behind Sensory Data. 1615-1645
  
• Jiucang Hao, Intae Lee, Te-Won Lee, Terrence J. Sejnowski:
  Independent Vector Analysis for Source Separation Using a Mixture of Gaussians Prior. 1646-1673
  

Volume 22, Number 7, July 2010

• Zbynek Pawlas, Lev Klebanov, Viktor Benes, Michaela Prokesová, Jirí Popelár, Petr Lánský:
  First-Spike Latency in the Presence of Spontaneous Activity. 1675-1697
  
• Johannes Friedrich, Robert Urbanczik, Walter Senn:
  Learning Spike-Based Population Codes by Reward and Population Feedback. 1698-1717
  
• Shun-ichi Amari:
  Conditional Mixture Model for Correlated Neuronal Spikes. 1718-1736
  
• Bor-Sen Chen, Cheng-Wei Li:
  On the Noise-Enhancing Ability of Stochastic Hodgkin-Huxley Neuron Systems. 1737-1763
  
• Gideon Gradwohl, Yoram Grossman:
  Statistical Computer Model Analysis of the Reciprocal and Recurrent Inhibitory Postsynaptic Potentials in alpha-Motoneurons. 1764-1785
  
• Rubén Moreno-Bote:
  Decision Confidence and Uncertainty in Diffusion Models with Partially Correlated Neuronal Integrators. 1786-1811
  
• Laurent U. Perrinet:
  Role of Homeostasis in Learning Sparse Representations. 1812-1836
  
• R. Rossi Pool, G. Mato:
  Hebbian Plasticity and Homeostasis in a Model of Hypercolumn of the Visual Cortex. 1837-1859
  
• Jason Gauci, Kenneth O. Stanley:
  Autonomous Evolution of Topographic Regularities in Artificial Neural Networks. 1860-1898
  
• Huajin Tang, Haizhou Li, Rui Yan:
  Memory Dynamics in Attractor Networks with Saliency Weights. 1899-1926
  
• Mingjie Zhao, Herbert Jaeger:
  Norm-Observable Operator Models. 1927-1959
  

Volume 22, Number 8, August 2010

• Lars Buesing, Wolfgang Maass:
  A Spiking Neuron as Information Bottleneck. 1961-1992
  

• Ke Yuan, Mahesan Niranjan:
  Estimating a State-Space Model from Point Process Observations: A Note on Convergence. 1993-2001
  

• Todd P. Coleman, Sridevi S. Sarma:
  A Computationally Efficient Method for Nonparametric Modeling of Neural Spiking Activity with Point Processes. 2002-2030
  
• Angelo Arleo, Thierry Nieus, Michele Bezzi, Anna D'Errico, Egidio D'Angelo, Olivier J. M. D. Coenen:
  How Synaptic Release Probability Shapes Neuronal Transmission: Information-Theoretic Analysis in a Cerebellar Granule Cell. 2031-2058
  
• Daniel Bush, Andrew Philippides, Phil Husbands, Michael O'Shea:
  Reconciling the STDP and BCM Models of Synaptic Plasticity in a Spiking Recurrent Neural Network. 2059-2085
  
• Yingxue Wang, Shih-Chii Liu:
  Multilayer Processing of Spatiotemporal Spike Patterns in a Neuron with Active Dendrites. 2086-2112
  
• Ahmet Omurtag, William W. Lytton:
  Spectral Method and High-Order Finite Differences for the Nonlinear Cable Equation. 2113-2136
  
• Wei Zhou, Jacek M. Zurada:
  Competitive Layer Model of Discrete-Time Recurrent Neural Networks with LT Neurons. 2137-2160
  
• Yansheng Ming, Zhanyi Hu:
  Modeling Stereopsis via Markov Random Field. 2161-2191
  
• Nicolas Le Roux, Yoshua Bengio:
  Deep Belief Networks Are Compact Universal Approximators. 2192-2207
  
• Intae Lee:
  Sample-Spacings-Based Density and Entropy Estimators for Spherically Invariant Multidimensional Data. 2208-2227
  

Volume 22, Number 9, September 2010

• Barbara Hammer, Alexander Hasenfuss:
  Topographic Mapping of Large Dissimilarity Data Sets. 2229-2284
  

• Hugo Larochelle, Yoshua Bengio, Joseph P. Turian:
  Tractable Multivariate Binary Density Estimation and the Restricted Boltzmann Forest. 2285-2307
  
• Urs Köster, Aapo Hyvärinen:
  A Two-Layer Model of Natural Stimuli Estimated with Score Matching. 2308-2333
  
• Payam Piray, Mohammad Mahdi Keramati, Amir Dezfouli, Caro Lucas, Azarakhsh Mokri:
  Individual Differences in Nucleus Accumbens Dopamine Receptors Predict Development of Addiction-Like Behavior: A Computational Approach. 2334-2368
  
• Kentaro Katahira, Jun Nishikawa, Kazuo Okanoya, Masato Okada:
  Extracting State Transition Dynamics from Multiple Spike Trains Using Hidden Markov Models with Correlated Poisson Distribution. 2369-2389
  
• Richard F. Lyon, Martin Rehn, Samy Bengio, Thomas C. Walters, Gal Chechik:
  Sound Retrieval and Ranking Using Sparse Auditory Representations. 2390-2416
  
• Hideitsu Hino, Yu Fujimoto, Noboru Murata:
  A Grouped Ranking Model for Item Preference Parameter. 2417-2451
  
• Robert C. Wilson, Matthew R. Nassar, Joshua I. Gold:
  Bayesian Online Learning of the Hazard Rate in Change-Point Problems. 2452-2476
  

Volume 22, Number 10, October 2010

• Robert Haslinger, Gordon Pipa, Emery N. Brown:
  Discrete Time Rescaling Theorem: Determining Goodness of Fit for Discrete Time Statistical Models of Neural Spiking. 2477-2506
  

• Daniel Calabuig, Jose F. Monserrat, Narcís Cardona:
  Convergence and Stability of Quantized Hopfield Networks Operating in a Fully Parallel Mode. 2507-2521
  
• Anne C. Smith, Vinh Kha Nguyen, Mattias P. Karlsson, Loren M. Frank, Peter Smith:
  Probability of Repeating Patterns in Simultaneous Neural Data. 2522-2536
  
• N. M. Fernandes, B. D. L. Pinto, Lírio Onofre Baptista de Almeida, Jan Frans Willem Slaets, Roland Köberle:
  Recording from Two Neurons: Second-Order Stimulus Reconstruction from Spike Trains and Population Coding. 2537-2557
  
• Aniello Buonocore, Ludovico Caputo, Enrica Pirozzi, Luigi M. Ricciardi:
  On a Stochastic Leaky Integrate-and-Fire Neuronal Model. 2558-2585
  
• Satohiro Tajima, Hiromasa Takemura, Ikuya Murakami, Masato Okada:
  Neuronal Population Decoding Explains the Change in Signal Detection Sensitivity Caused by Task-Irrelevant Perceptual Bias. 2586-2614
  
• Jun Wang, Hendrik Jan Hoogeboom, Linqiang Pan, Gheorghe Paun, Mario J. Pérez-Jiménez:
  Spiking Neural P Systems with Weights. 2615-2646
  
• Mouhamad Jradeh:
  A New Bidimensional Neural Field Model with Heterogeneous Connection Topology. 2647-2654
  
• Dongpo Xu, Huisheng Zhang, Lijun Liu:
  Convergence Analysis of Three Classes of Split-Complex Gradient Algorithms for Complex-Valued Recurrent Neural Networks. 2655-2677
  
• Youngmin Cho, Lawrence K. Saul:
  Large-Margin Classification in Infinite Neural Networks. 2678-2697
  
• Luís M. Silva, Joaquim Marques de Sá, Luís A. Alexandre:
  The MEE Principle in Data Classification: A Perceptron-Based Analysis. 2698-2728
  

Volume 22, Number 11, November 2010

• Tanya Schmah, Grigori Yourganov, Richard S. Zemel, Geoffrey E. Hinton, Steven L. Small, Stephen C. Strother:
  Comparing Classification Methods for Longitudinal fMRI Studies. 2729-2762
  

• Christian Wehrhahn, Harald J. Teufel:
  How Are Lateral Chromatic Interactions Computed from Cone Signals? 2763-2784
  
• Alexander J. Dubbs, Brad A. Seiler, Marcelo O. Magnasco:
  A Fast _p Spike Alignment Metric. 2785-2808
  
• Chrisantha Fernando, Richard Goldstein, Eörs Szathmáry:
  The Neuronal Replicator Hypothesis. 2809-2857
  
• Yiming Ying, Colin Campbell:
  Rademacher Chaos Complexities for Learning the Kernel Problem. 2858-2886
  
• Hideitsu Hino, Noboru Murata:
  A Conditional Entropy Minimization Criterion for Dimensionality Reduction and Multiple Kernel Learning. 2887-2923
  
• Aree Witoelar, Anarta Ghosh, J. J. G. de Vries, Barbara Hammer, Michael Biehl:
  Window-Based Example Selection in Learning Vector Quantization. 2924-2961
  
• Qingshan Liu, Jinde Cao, Guanrong Chen:
  A Novel Recurrent Neural Network with Finite-Time Convergence for Linear Programming. 2962-2978
  

Volume 22, Number 12, December 2010

• Stefan Klampfl, Wolfgang Maass:
  A Theoretical Basis for Emergent Pattern Discrimination in Neural Systems Through Slow Feature Extraction. 2979-3035
  

• Reza Moazzezi, Peter Dayan:
  Change-Based Inference in Attractor Nets: Linear Analysis. 3036-3061
  
• Hiroyuki Nakahara, Sivaramakrishnan Kaveri:
  Internal-Time Temporal Difference Model for Neural Value-Based Decision Making. 3062-3106
  
• Charles F. Cadieu, Kilian Koepsell:
  Phase Coupling Estimation from Multivariate Phase Statistics. 3107-3126
  
• Marcel van Gerven, Floris P. de Lange, Tom Heskes:
  Neural Decoding with Hierarchical Generative Models. 3127-3142
  
• Bor-Sen Chen, Cheng-Wei Li:
  Robust Observer-Based Tracking Control of Hodgkin-Huxley Neuron Systems Under Environmental Disturbances. 3143-3178
  
• Jesús Malo, Valero Laparra:
  Psychophysically Tuned Divisive Normalization Approximately Factorizes the PDF of Natural Images. 3179-3206
  
• Dan Claudiu Ciresan, Ueli Meier, Luca Maria Gambardella, Jürgen Schmidhuber:
  Deep, Big, Simple Neural Nets for Handwritten Digit Recognition. 3207-3220
  
• Hongzhi Tong, Di-Rong Chen, Fenghong Yang:
  Least Square Regression with l^p-Coefficient Regularization. 3221-3235
  

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