dblp: Neural Computation, Volume 32 (original) (raw)

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Volume 32, Number 1, January 2020

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    Shagun Sodhani, Sarath Chandar, Yoshua Bengio:
    Toward Training Recurrent Neural Networks for Lifelong Learning. 1-35
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    Nicholas M. Boffi, Jean-Jacques E. Slotine:
    A Continuous-Time Analysis of Distributed Stochastic Gradient. 36-96
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    Shiyu Duan, Shujian Yu, Yunmei Chen, José C. Príncipe:
    On Kernel Method-Based Connectionist Models and Supervised Deep Learning Without Backpropagation. 97-135
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    Luis Sa-Couto, Andreas Wichert:
    Storing Object-Dependent Sparse Codes in a Willshaw Associative Network. 136-152
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    Anthony Strock, Xavier Hinaut, Nicolas P. Rougier:
    A Robust Model of Gated Working Memory. 153-181
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    Xiping Ju, Biao Fang, Rui Yan, Xiaoliang Xu, Huajin Tang:
    An FPGA Implementation of Deep Spiking Neural Networks for Low-Power and Fast Classification. 182-204
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    Andreas Knoblauch, Günther Palm:
    Iterative Retrieval and Block Coding in Autoassociative and Heteroassociative Memory. 205-260
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    Adrian E. G. Huber, Jithendar Anumula, Shih-Chii Liu:
    Optimal Sampling of Parametric Families: Implications for Machine Learning. 261-279

Volume 32, Number 2, February 2020

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    Sidney R. Lehky, Anh Huy Phan, Andrzej Cichocki, Keiji Tanaka:
    Face Representations via Tensorfaces of Various Complexities. 281-329
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    Nicolai Waniek:
    Transition Scale-Spaces: A Computational Theory for the Discretized Entorhinal Cortex. 330-394
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    Ewa Gudowska-Nowak, Maciej A. Nowak, Dante R. Chialvo, Jeremi K. Ochab, Wojciech Tarnowski:
    From Synaptic Interactions to Collective Dynamics in Random Neuronal Networks Models: Critical Role of Eigenvectors and Transient Behavior. 395-423
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    Jure Demsar, Rob Forsyth:
    Synaptic Scaling Improves the Stability of Neural Mass Models Capable of Simulating Brain Plasticity. 424-446
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    Kishan Wimalawarne, Makoto Yamada, Hiroshi Mamitsuka:
    Scaled Coupled Norms and Coupled Higher-Order Tensor Completion. 447-484
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    Xiaofeng Xu, Ivor W. Tsang, Chuancai Liu:
    Improving Generalization via Attribute Selection on Out-of-the-Box Data. 485-514

Volume 32, Number 3, March 2020

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    Elizabeth B. Torres, Richa Rai, Sejal Mistry, Brenda Gupta:
    Hidden Aspects of the Research ADOS Are Bound to Affect Autism Science. 515-561
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    Tao Bian, Daniel M. Wolpert, Zhong-Ping Jiang:
    Model-Free Robust Optimal Feedback Mechanisms of Biological Motor Control. 562-595
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    Thomas Hueber, Eric Tatulli, Laurent Girin, Jean-Luc Schwartz:
    Evaluating the Potential Gain of Auditory and Audiovisual Speech-Predictive Coding Using Deep Learning. 596-625
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    Saeed Farjami, Ryan P. D. Alexander, Derek Bowie, Anmar Khadra:
    Switching in Cerebellar Stellate Cell Excitability in Response to a Pair of Inhibitory/Excitatory Presynaptic Inputs: A Dynamical System Perspective. 626-658
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    Zhenghang Cui, Nontawat Charoenphakdee, Issei Sato, Masashi Sugiyama:
    Classification from Triplet Comparison Data. 659-681

Volume 32, Number 4, April 2020

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    Daiji Tsutsui:
    Center Manifold Analysis of Plateau Phenomena Caused by Degeneration of Three-Layer Perceptron. 683-710
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    Henrikas Vaitkevicius, Algimantas Svegzda, Rytis Stanikunas, Remigijus Bliumas, Alvydas Soliunas, Janus J. Kulikowski:
    Neural Model of Coding Stimulus Orientation and Adaptation. 711-740
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    Xugang Xi, Wenjun Jiang, Seyed M. Miran, Xian Hua, Yun-Bo Zhao, Chen Yang, Zhizeng Luo:
    Feature Extraction of Surface Electromyography Based on Improved Small-World Leaky Echo State Network. 741-758
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    Hoai An Le Thi, Vinh Thanh Ho:
    Online Learning Based on Online DCA and Application to Online Classification. 759-793
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    Yuval Harel, Ron Meir:
    Optimal Multivariate Tuning with Neuron-Level and Population-Level Energy Constraints. 794-828

Volume 32, Number 5, May 2020

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    Jing Gao, Peng Li, Zhikui Chen, Jianing Zhang:
    A Survey on Deep Learning for Multimodal Data Fusion. 829-864
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    Shreya Saxena, Sridevi V. Sarma, Munther A. Dahleh:
    Performance Limitations in Sensorimotor Control: Trade-Offs Between Neural Computation and Accuracy in Tracking Fast Movements. 865-886
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    Manuel Ciba, Robert Bestel, Christoph Nick, Guilherme Ferraz de Arruda, Thomas K. D. M. Peron, Cesar Henrique Comin, Luciano da Fontoura Costa, Francisco Aparecido Rodrigues, Christiane Thielemann:
    Comparison of Different Spike Train Synchrony Measures Regarding Their Robustness to Erroneous Data From Bicuculline-Induced Epileptiform Activity. 887-911
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    Asieh Abolpour Mofrad, Anis Yazidi, Hugo Lewi Hammer, Erik Arntzen:
    Equivalence Projective Simulation as a Framework for Modeling Formation of Stimulus Equivalence Classes. 912-968
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    Michael C. Burkhart, David M. Brandman, Brian Franco, Leigh R. Hochberg, Matthew T. Harrison:
    The Discriminative Kalman Filter for Bayesian Filtering with Nonlinear and Nongaussian Observation Models. 969-1017
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    Noah Frazier-Logue, Stephen José Hanson:
    The Stochastic Delta Rule: Faster and More Accurate Deep Learning Through Adaptive Weight Noise. 1018-1032

Volume 32, Number 6, June 2020

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    Weishun Zhong, Zhiyue Lu, David J. Schwab, Arvind Murugan:
    Nonequilibrium Statistical Mechanics of Continuous Attractors. 1033-1068
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    Terry Elliott:
    First Passage Time Memory Lifetimes for Multistate, Filter-Based Synapses. 1069-1143
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    Mengyu Tu, Ruohe Zhao, Avital Adler, Wen-Biao Gan, Zhe S. Chen:
    Efficient Position Decoding Methods Based on Fluorescence Calcium Imaging in the Mouse Hippocampus. 1144-1167
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    Masaaki Takada, Taiji Suzuki, Hironori Fujisawa:
    Independently Interpretable Lasso for Generalized Linear Models. 1168-1221
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    Steven J. Frank, Andrea M. Frank:
    Salient Slices: Improved Neural Network Training and Performance with Image Entropy. 1222-1237

Volume 32, Number 7, July 2020

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    Pratik S. Sachdeva, Jesse A. Livezey, Michael Robert DeWeese:
    Heterogeneous Synaptic Weighting Improves Neural Coding in the Presence of Common Noise. 1239-1276
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    Benjamin Straub, Gaby Schneider:
    A Model for the Study of the Increase in Stimulus and Change Point Detection with Small and Variable Spiking Delays. 1277-1321
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    Pascal Helson:
    A Mathematical Analysis of Memory Lifetime in a Simple Network Model of Memory. 1322-1354
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    Kaixuan Zhang, Qinglong Wang, Xue Liu, C. Lee Giles:
    Shapley Homology: Topological Analysis of Sample Influence for Neural Networks. 1355-1378
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    Yue Liu, Marc W. Howard:
    Generation of Scale-Invariant Sequential Activity in Linear Recurrent Networks. 1379-1407
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    Jakub Fil, Dominique F. Chu:
    Minimal Spiking Neuron for Solving Multilabel Classification Tasks. 1408-1429

Volume 32, Number 8, August 2020

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    Shun-ichi Amari:
    Any Target Function Exists in a Neighborhood of Any Sufficiently Wide Random Network: A Geometrical Perspective. 1431-1447
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    Alexandre René, André Longtin, Jakob H. Macke:
    Inference of a Mesoscopic Population Model from Population Spike Trains. 1448-1498
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    Yuangang Pan, Ivor W. Tsang, Avinash Kumar Singh, Chin-Teng Lin, Masashi Sugiyama:
    Stochastic Multichannel Ranking with Brain Dynamics Preferences. 1499-1530
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    Xinqi Li, Jun Wang, Sam Kwong:
    A Discrete-Time Neurodynamic Approach to Sparsity-Constrained Nonnegative Matrix Factorization. 1531-1562
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    Illia Horenko:
    On a Scalable Entropic Breaching of the Overfitting Barrier for Small Data Problems in Machine Learning. 1563-1579
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    Daiki Suehiro, Kohei Hatano, Eiji Takimoto, Shuji Yamamoto, Kenichi Bannai, Akiko Takeda:
    Theory and Algorithms for Shapelet-Based Multiple-Instance Learning. 1580-1613

Volume 32, Number 9, September 2020

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    Richard Gast, Helmut Schmidt, Thomas R. Knösche:
    A Mean-Field Description of Bursting Dynamics in Spiking Neural Networks with Short-Term Adaptation. 1615-1634
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    Ruohan Zhang, Dana H. Ballard:
    Parallel Neural Multiprocessing with Gamma Frequency Latencies. 1635-1663
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    Xin Hu, Jun Liu, Jie Ma, Yudai Pan, Lingling Zhang:
    Fine-Grained 3D-Attention Prototypes for Few-Shot Learning. 1664-1684
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    Masaki Kobayashi:
    Hyperbolic-Valued Hopfield Neural Networks in Synchronous Mode. 1685-1696
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    Ishan Wickramasingha, Ahmed Elrewainy, Michael Sobhy, Sherif S. Sherif:
    Tensor Least Angle Regression for Sparse Representations of Multidimensional Signals. 1697-1732
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    Yuko Kuroki, Liyuan Xu, Atsushi Miyauchi, Junya Honda, Masashi Sugiyama:
    Polynomial-Time Algorithms for Multiple-Arm Identification with Full-Bandit Feedback. 1733-1773

Volume 32, Number 10, October 2020

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    Shusen Pu, Peter J. Thomas:
    Fast and Accurate Langevin Simulations of Stochastic Hodgkin-Huxley Dynamics. 1775-1835
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    Wei Sun, Jeff Orchard:
    A Predictive-Coding Network That Is Both Discriminative and Generative. 1836-1862
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    Cunle Qian, Xuyun Sun, Yueming Wang, Xiaoxiang Zheng, Yiwen Wang, Gang Pan:
    Binless Kernel Machine: Modeling Spike Train Transformation for Cognitive Neural Prostheses. 1863-1900
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    Keishi Sando, Hideitsu Hino:
    Modal Principal Component Analysis. 1901-1935
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    Yaxin Shi, Yuangang Pan, Donna Xu, Ivor W. Tsang:
    Multiview Alignment and Generation in CCA via Consistent Latent Encoding. 1936-1979
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    Hongzhi Tong, Jiajing Gao:
    Analysis of Regression Algorithms with Unbounded Sampling. 1980-1997
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    Yu Inatsu, Masayuki Karasuyama, Keiichi Inoue, Hideki Kandori, Ichiro Takeuchi:
    Active Learning of Bayesian Linear Models with High-Dimensional Binary Features by Parameter Confidence-Region Estimation. 1998-2031
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    Yu Inatsu, Daisuke Sugita, Kazuaki Toyoura, Ichiro Takeuchi:
    Active Learning for Enumerating Local Minima Based on Gaussian Process Derivatives. 2032-2068

Volume 32, Number 11, November 2020

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    Terence D. Sanger, Mitsuo Kawato:
    A Cerebellar Computational Mechanism for Delay Conditioning at Precise Time Intervals. 2069-2084
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    Takuya Isomura, Karl J. Friston:
    Reverse-Engineering Neural Networks to Characterize Their Cost Functions. 2085-2121
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    Sama Daryanavard, Bernd Porr:
    Closed-Loop Deep Learning: Generating Forward Models With Backpropagation. 2122-2144
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    Ali Yousefi, Yalda Amidi, Behzad Nazari, Uri T. Eden:
    Assessing Goodness-of-Fit in Marked Point Process Models of Neural Population Coding via Time and Rate Rescaling. 2145-2186
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    Yu Terada, Tomoyuki Obuchi, Takuya Isomura, Yoshiyuki Kabashima:
    Inferring Neuronal Couplings From Spiking Data Using a Systematic Procedure With a Statistical Criterion. 2187-2211
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    Yoji Uno, Takehiro Suzuki, Takahiro Kagawa:
    Repetitive Control for Multi-Joint Arm Movements Based on Virtual Trajectories. 2212-2236
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    Masaki Kobayashi:
    Bicomplex Projection Rule for Complex-Valued Hopfield Neural Networks. 2237-2248
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    Changcun Huang:
    ReLU Networks Are Universal Approximators via Piecewise Linear or Constant Functions. 2249-2278
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    Victor Boutin, Angelo Franciosini, Franck Ruffier, Laurent Perrinet:
    Effect of Top-Down Connections in Hierarchical Sparse Coding. 2279-2309

Volume 32, Number 12, December 2020

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    Edward Paxon Frady, Spencer J. Kent, Bruno A. Olshausen, Friedrich T. Sommer:
    Resonator Networks, 1: An Efficient Solution for Factoring High-Dimensional, Distributed Representations of Data Structures. 2311-2331
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    Spencer J. Kent, Edward Paxon Frady, Friedrich T. Sommer, Bruno A. Olshausen:
    Resonator Networks, 2: Factorization Performance and Capacity Compared to Optimization-Based Methods. 2332-2388
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    Tiger W. Lin, Yusi Chen, Qasim Bukhari, Giri P. Krishnan, Maxim Bazhenov, Terrence J. Sejnowski:
    Differential Covariance: A New Method to Estimate Functional Connectivity in fMRI. 2389-2421
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    Amir Hossein Ghaderi, Bianca R. Baltaretu, Masood Nemati Andevari, Vishal Bharmauria, Fuat Balci:
    Synchrony and Complexity in State-Related EEG Networks: An Application of Spectral Graph Theory. 2422-2454
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    Woori Kim, Yongseok Yoo:
    Toward a Unified Framework for Cognitive Maps. 2455-2485
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    Yu Inatsu, Masayuki Karasuyama, Keiichi Inoue, Ichiro Takeuchi:
    Active Learning for Level Set Estimation Under Input Uncertainty and Its Extensions. 2486-2531
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    Guotian Xie:
    Redundancy-Aware Pruning of Convolutional Neural Networks. 2532-2556
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    Ruizhi Chen, Ling Li:
    Analyzing and Accelerating the Bottlenecks of Training Deep SNNs With Backpropagation. 2557-2600

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