Neural Networks Volume 64, Pages 1-64, April 2015

Special Issue on “Deep Learning of Representations”
Edited by Yoshua Bengio and Honglak Lee

1. Editorial introduction to the Neural Networks special issue on Deep Learning of Representations  
Pages: 1-3
Author(s): Yoshua Bengio, Honglak Lee

2. Two-layer contractive encodings for learning stable nonlinear features 
Pages: 4-11
Author(s): Hannes Schulz, Kyunghyun Cho, Tapani Raiko, Sven Behnke

3. Measuring the usefulness of hidden units in Boltzmann machines with mutual information  
Pages: 12-18
Author(s): Mathias Berglund, Tapani Raiko, Kyunghyun Cho

4. Deep learning of support vector machines with class probability output networks 
Pages: 19-28
Author(s): Sangwook Kim, Zhibin Yu, Rhee Man Kil, Minho Lee

5. Expected energy-based restricted Boltzmann machine for classification  
Pages: 29-38
Author(s): S. Elfwing, E. Uchibe, K. Doya

6. Deep Convolutional Neural Networks for Large-scale Speech Tasks  
Pages: 39-48
Author(s): Tara N. Sainath, Brian Kingsbury, George Saon, Hagen Soltau, Abdel-rahman Mohamed, George Dahl, Bhuvana Ramabhadran

7. Frame-by-frame language identification in short utterances using deep neural networks  
Pages: 49-58
Author(s): Javier Gonzalez-Dominguez, Ignacio Lopez-Moreno, Pedro J. Moreno, Joaquin Gonzalez-Rodriguez

8. Challenges in representation learning: A report on three machine learning contests  
Pages: 59-63
Author(s): Ian J. Goodfellow, Dumitru Erhan, Pierre Luc Carrier, Aaron Courville, Mehdi Mirza, Ben Hamner, Will Cukierski, Yichuan Tang, David Thaler, Dong-Hyun Lee, Yingbo Zhou, Chetan Ramaiah, Fangxiang Feng, Ruifan Li, Xiaojie Wang, Dimitris Athanasakis, John Shawe-Taylor, Maxim Milakov, John Park, Radu Ionescu, Marius Popescu, Cristian Grozea, James Bergstra, Jingjing Xie, Lukasz Romaszko, Bing Xu, Zhang Chuang, Yoshua Bengio

Neural Networks, Volume 63, Pages 1-292, March 2015

Cognitive Science

A neural network for learning the meaning of objects and words from a featural representation  
Pages: 234-253
Author(s): Mauro Ursino, Cristiano Cuppini, Elisa Magosso

Neuroscience

Circuit design and exponential stabilization of memristive neural networks  
Pages: 48-56
Author(s): Wen, Tingwen Huang, Zhigang Zeng, Yiran Chen, Peng Li

Learning Systems

Performance improvement of classifier fusion for batch samples based on upper integral  
Pages: 87-93
Author(s): Hui-Min Feng, Xi-Zhao Wang

Convex nonnegative matrix factorization with manifold regularization  
Pages: 94-103
Author(s): Wenjun Hu, Kup-Sze Choi, Peiliang Wang, Yunliang Jiang, Shitong Wang

Towards an intelligent framework for multimodal affective data analysis  
Pages: 104-116
Author(s): Soujanya Poria, Erik Cambria, Amir Hussain, Guang-Bin Huang

Approximate kernel competitive learning  
Pages: 117-132
Author(s): Jian-Sheng Wu, Wei-Shi Zheng, Jian-Huang Lai

A vector reconstruction based clustering algorithm particularly for large-scale text collection  
Pages: 141-155
Author(s): Ming Liu, Chong Wu, Lei Chen

Active learning for semi-supervised clustering based on locally linear propagation reconstruction  
Pages: 170-184
Author(s): Chin-Chun Chang, Po-Yi Lin

Adaptive learning rate of SpikeProp based on weight convergence analysis  
Pages: 185-198
Author(s): Sumit Bam Shrestha, Qing Song

Fully probabilistic control for stochastic nonlinear control systems with input dependent noise  
Pages: 199-207
Author(s): Randa Herzallah

Self-organizing maps based on limit cycle attractors  
Pages: 208-222
Author(s): Di-Wei Huang, Rodolphe J. Gentili, James A. Reggia

Mathematical and Computational Analysis

Projective synchronization of fractional-order memristor-based neural networks  
Pages: 1-9
Author(s): Hai-Bo Bao, Jin-De Cao

Estimates on compressed neural networks regression  
Pages: 10-17
Author(s): Yongquan Zhang, Youmei Li, Jianyong Sun, Jiabing Ji

Global exponential stability of delayed Markovian jump fuzzy cellular neural networks with generally incomplete transition probability  
Pages: 18-30
Author(s): Yonggui Kao, Lei Shi, Jing Xie, Hamid Reza Karimi

Jackson-type inequalities for spherical neural networks with doubling weights  
Pages: 57-65
Author(s): Shaobo Lin, Jinshan Zeng, Lin Xu, Zongben Xu

RBF-network based sparse signal recovery algorithm for compressed sensing reconstruction  
Pages: 66-78
Author(s): Vidya L., Vivekanand V., Shyamkumar U., Deepak Mishra

Finite-time synchronization control of a class of memristor-based recurrent neural networks  
Pages: 133-140
Author(s): Minghui Jiang, Shuangtao Wang, Jun Mei, Yanjun Shen

Dynamics of neural networks over undirected graphs  
Pages: 156-169
Author(s): Eric Goles, Gonzalo A. Ruz

Convergence and attractivity of memristor-based cellular neural networks with time delays  
Pages: 223-233
Author(s): Sitian Qin, Jun Wang, Xiaoping Xue

Massively parallel neural circuits for stereoscopic color vision: Encoding, decoding and identification  
Pages: 254-271
Author(s): Aurel A. Lazar, Yevgeniy B. Slutskiy, Yiyin Zhou

Neural network for constrained nonsmooth optimization using Tikhonov regularization  
Pages: 272-281
Author(s): Sitian Qin, Dejun Fan, Guangxi Wu, Lijun Zhao

Engineering and Applications

Robust sequential learning of feedforward neural networks in the presence of heavy-tailed noise  
Pages: 31-47
Author(s): Najdan Vuković, Zoran Miljković

Fast Clustered Radial Basis Function Network as an adaptive predictive controller  
Pages: 79-86
Author(s): Dino Kosic

Designing a deep brain stimulator to suppress pathological neuronal synchrony  
Pages: 282-292
Author(s): Ghazal Montaseri, Mohammad Javad Yazdanpanah, Fariba Bahrami

Neural Networks, Volume 62, Pages 1-118, February 2015

Communication and Brain  
Author(s): Yutaka Sakaguchi, Takeshi Aihara, Peter Ford Dominey, Ichiro Tsuda
Pages: 1-2

Mathematical Theory and Model

Mathematical modeling for evolution of heterogeneous modules in the brain  
Author(s): Yutaka Yamaguti, Ichiro Tsuda
Pages: 3-10

Self-organization of a recurrent network under ongoing synaptic plasticity  
Author(s): Takaaki Aoki
Pages: 11-19

Hodge–Kodaira decomposition of evolving neural networks  
Author(s): Keiji Miura, Takaaki Aoki
Pages: 20-24

Memories as bifurcations: Realization by collective dynamics of spiking neurons under stochastic inputs
Author(s): Tomoki Kurikawa, Kunihiko Kaneko
Pages: 25-31

Multistate network model for the pathfinding problem with a self-recovery property
Author(s): Kei-Ichi Ueda, Masaaki Yadome, Yasumasa Nishiura
Pages: 32-38

Neural coordination can be enhanced by occasional interruption of normal firing patterns: A self-optimizing spiking neural network model  
Author(s): Alexander Woodward, Tom Froese, Takashi Ikegami
Pages: 39-46

Physiology, Neuroscience and Model

Phase shifts in alpha-frequency rhythm detected in electroencephalograms influence reaction time  
Author(s): Yasushi Naruse, Ken Takiyama, Masato Okada, Hiroaki Umehara, Yutaka Sakaguchi
Pages: 47-51

Spatial consistency of neural firing regulates long-range local field potential synchronization: A computational study  
Author(s): Naoyuki Sato
Pages: 52-61

Arm-use dependent lateralization of gamma and beta oscillations in primate medial motor areas  
Author(s): Ryosuke Hosaka, Toshi Nakajima, Kazuyuki Aihara, Yoko Yamaguchi, Hajime Mushiake
Pages: 62-66

Spatiotemporal patterns of current source density in the prefrontal cortex of a behaving monkey  
Author(s): Kazuhiro Sakamoto, Norihiko Kawaguchi, Kohei Yagi, Hajime Mushiake
Pages: 67-72

Computational model of visual hallucination in dementia with Lewy bodies  
Author(s): Hiromichi Tsukada, Hiroshi Fujii, Kazuyuki Aihara, Ichiro Tsuda
Pages: 73-82

Behavioral and System model

Immediate return preference emerged from a synaptic learning rule for return maximization  
Author(s): Yoshiya Yamaguchi, Takeshi Aihara, Yutaka Sakai
Pages: 83-90

A wavelet-based method for extracting intermittent discontinuities observed in human motor behavior  
Author(s): Yasuyuki Inoue, Yutaka Sakaguchi
Pages: 91-101

Exploiting the gain-modulation mechanism in parieto-motor neurons: Application to visuomotor transformations and embodied simulation  
Author(s): Sylvain Mahé, Raphaël Braud, Philippe Gaussier, Mathias Quoy, Alexandre Pitti
Pages: 102-111

Communication, concepts and grounding  
Author(s): Frank van der Velde
Pages: 112-117

Neural Networks Voume 61, Pages 1-118, January 2015

1. Neural Networks Referees in 2014
Pages: xi-xiii

2. Exciting Time for Neural Networks  
Pages: xv-xvi
Author(s): Kenji Doya, DeLiang Wang

NEURAL NETWORKS LETTERS

3. Dynamic analysis of periodic solution for high-order discrete-time Cohen–Grossberg neural networks with time delays  
Pages: 68-74
Author(s): Kaiyun Sun, Ancai Zhang, Jianlong Qiu, Xiangyong Chen, Chengdong Yang, Xiao Chen

REVIEWS

4. Trends in extreme learning machines: A review
Pages: 32-48
Author(s): Gao Huang, Guang-Bin Huang, Shiji Song, Keyou You

5. Deep learning in neural networks: An overview
Pages: 85-117
Author(s): Jürgen Schmidhuber

LEARNING SYSTEMS

6. An efficient sampling algorithm with adaptations for Bayesian variable selection
Pages: 22-31
Author(s): Takamitsu Araki, Kazushi Ikeda, Shotaro Akaho

7. A complex-valued neural dynamical optimization approach and its stability analysis
Pages: 59-67
Author(s): Songchuan Zhang, Youshen Xia, Weixing Zheng

8. Max–min distance nonnegative matrix factorization  
Pages: 75-84
Author(s): Jim Jing-Yan Wang, Xin Gao

MATHEMATICAL AND COMPUTATIONAL ANALYSIS

9. New synchronization criteria for memristor-based networks: Adaptive control and feedback control schemes
Pages: 1-9
Author(s): Ning Li, Jinde Cao

10. A one-layer recurrent neural network for constrained nonconvex optimization
Pages: 10-21
Author(s): Guocheng Li, Zheng Yan, Jun Wang

11. Passivity analysis for memristor-based recurrent neural networks with discrete and distributed delays
Pages: 49-58
Author(s): Guodong Zhang, Yi Shen, Quan Yin, Junwei Sun

Neural Networks Volume 60, Pages: 1-246, December 2014

Cognitive Science

1. How active perception and attractor dynamics shape perceptual categorization: A computational model  
Author(s): Nicola Catenacci Volpi, Jean Charles Quinton, Giovanni Pezzulo
Pages: 1-16

2. Connectionist interpretation of the association between cognitive dissonance and attention switching  
Author(s): Takao Matsumoto
Pages: 119-132

3. Neurocomputational approaches to modelling multisensory integration in the brain: A review  
Author(s): Mauro Ursino, Cristiano Cuppini, Elisa Magosso
Pages: 141-165

4. Person-by-person prediction of intuitive economic choice  
Author(s): George Mengov
Pages: 232-245


Neuroscience

5. Global exponential almost periodicity of a delayed memristor-based neural networks  
Author(s): Jiejie Chen, Zhigang Zeng, Ping Jiang
Pages: 33-43

6. Global robust asymptotic stability of variable-time impulsive BAM neural networks  
Author(s): Mustafa Şaylı, Enes Yılmaz
Pages: 67-73

7. Noise cancellation of memristive neural networks  
Author(s): Shiping Wen, Zhigang Zeng, Tingwen Huang, Xinghuo Yu
Pages: 74-83

8. Stability and bifurcation analysis of new coupled repressilators in genetic regulatory networks with delays  
Author(s): Guang Ling, Zhi-Hong Guan, Ding-Xin He, Rui-Quan Liao, Xian-He Zhang
Pages: 222-231


Learning Systems

9. Simple randomized algorithms for online learning with kernels  
Author(s): Wenwu He, James T. Kwok
Pages: 17-24

10. New approximation method for smooth error backpropagation in a quantron network  
Author(s): Simon de Montigny
Pages: 84-95

11. Unsupervised learnable neuron model with nonlinear interaction on dendrites  
Pages: 96-103
Author(s): Yuki Todo, Hiroki Tamura, Kazuya Yamashita, Zheng Tang

12. A convolutional recursive modified Self Organizing Map for handwritten digits recognition  
Author(s): Ehsan Mohebi, Adil Bagirov
Pages: 104-118

13. Logarithmic learning for generalized classifier neural network  
Author(s): Buse Melis Ozyildirim, Mutlu Avci
Pages: 133-140

14. Design of hybrid radial basis function neural networks (HRBFNNs) realized with the aid of hybridization of fuzzy clustering method (FCM) and polynomial neural networks (PNNs)  
Author(s): Wei Huang, Sung-Kwun Oh, Witold Pedrycz
Pages: 166-181

15. On extending the complex FastICA algorithms to noisy data  
Author(s): Zongli Ruan, Liping Li, Guobing Qian
Pages: 194-202

16. Online computing of non-stationary distributions velocity fields by an accuracy controlled growing neural gas  
Author(s): Hervé Frezza-Buet
Pages: 203-221


Mathematical and Computational Analysis

17. Impulsive exponential synchronization of randomly coupled neural networks with Markovian jumping and mixed model-dependent time delays  
Author(s): Xin Wang, Chuandong Li, Tingwen Huang, Ling Chen
Pages: 25-32

18. Continuous neural identifier for uncertain nonlinear systems with time delays in the input signal  
Author(s): M. Alfaro-Ponce, A. Argüelles, I. Chairez
Pages: 53-66


Engineering and Applications

19. Dynamic neural network-based robust observers for uncertain nonlinear systems  
Author(s): H.T. Dinh, R. Kamalapurkar, S. Bhasin, W.E. Dixon
Pages: 44-52

20. A computer vision system for rapid search inspired by surface-based attention mechanisms from human perception  
Author(s): Johannes Mohr, Jong-Han Park, Klaus Obermayer
Pages: 182-193

Neural Networks Special Issue: Neural Network Learning in Big Data

Big data is much more than storage of and access to data. Analytics plays an important role in making sense of that data and exploiting its value. But learning from big data has become a significant challenge and requires development of new types of algorithms. Most machine learning algorithms encounter theoretical challenges in scaling up to big data. Plus there are challenges of high dimensionality, velocity and variety for all types of machine learning algorithms. The neural network field has historically focused on algorithms that learn in an online, incremental mode without requiring in-memory access to huge amounts of data. The brain is arguably the best and most elegant big data processor and is the inspiration for neural network learning methods. Neural network type of learning is not only ideal for streaming data (as in the Industrial Internet or the Internet of Things), but could also be used for stored big data. For stored big data, neural network algorithms can learn from all of the data instead of from samples of the data. And the same is true for streaming data where not all of the data is actually stored. In general, online, incremental learning algorithms are less vulnerable to size of the data. Neural network algorithms, in particular, can take advantage of massively parallel (brain-like) computations, which use very simple processors, that other machine learning technologies cannot. Specialized neuromorphic hardware, originally meant for large-scale brain simulations, is becoming available to implement these algorithms in a massively parallel fashion. Neural network algorithms, therefore, can deliver very fast and efficient real-time learning through the use of hardware and this could be particularly useful for streaming data in the Industrial Internet. Neural network technologies thus can become significant components of big data analytics platforms and this special issue will begin that journey with big data.

For this special issue of Neural Networks, we invite papers that address many of the challenges of learning from big data. In particular, we are interested in papers on efficient and innovative algorithmic approaches to analyzing big data (e.g. deep networks, nature-inspired and brain-inspired algorithms), implementations on different computing platforms (e.g. neuromorphic, GPUs, clouds, clusters) and applications of online learning to solve real-world big data problems (e.g. health care, transportation, and electric power and energy management).

RECOMMENDED TOPICS:

Topics of interest include, but are not limited to:

1. Autonomous, online, incremental learning – theory, algorithms and applications in big data
2. High dimensional data, feature selection, feature transformation – theory, algorithms and applications for big data
3. Scalable neural network algorithms for big data
4. Neural network learning algorithms for high-velocity streaming data
5. Deep neural network learning
6. Neuromorphic hardware for scalable neural network learning
7. Big data analytics using neural networks in healthcare/medical applications
8. Big data analytics using neural networks in electric power and energy systems
9. Big data analytics using neural networks in large sensor networks
10. Big data and neural network learning in computational biology and bioinformatics

SUBMISSION PROCEDURE:

Prospective authors should visit http://ees.elsevier.com/neunet/ for information on paper submission. During the submission process, there will be steps to designate the submission to this special issue. However, please indicate on the first page of the manuscript that the manuscript is intended for the Special Issue: Neural Network Learning in Big Data. Manuscripts will be peer reviewed according to Neural Networks guidelines.

Manuscript submission due: December 15, 2014
First review completed: March 1, 2015
Revised manuscript due: April 1, 2015
Second review completed, final decisions to authors: April 15, 2015
Final manuscript due: April 30, 2015

GUEST EDITORS:

• Asim Roy, Arizona State University, USA (asim.roy@asu.edu) (lead guest editor)
• Kumar Venayagamoorthy, Clemson University, USA (gkumar@ieee.org)
• Nikola Kasabov, Auckland University of Technology, New Zealand (nkasabov@aut.ac.nz)
• Irwin King, Chinese University of Hong Kong, China (irwinking@gmail.com)

Neural Networks Volume 58, Pages 1-148, October 2014

Special Issue on Affective Neural Networks and Cognitive Learning Systems for Big Data Analysis
Edited by Amir Hussain, Erik Cambria, Björn Schuller and Newton Howard

1. Affective neural networks and cognitive learning systems for big data analysis  
Pages: 1-3
Author(s): Amir Hussain, Erik Cambria, Björn Schuller, Newton Howard
   
2. Discrete particle swarm optimization for identifying community structures in signed social networks
Pages: 4-13
Author(s): Qing Cai, Maoguo Gong, Bo Shen, Lijia Ma, Licheng Jiao
   
3. An incremental community detection method for social tagging systems using locality-sensitive hashing
Pages: 14-28
Author(s): Zhenyu Wu, Ming Zou
   
4. Affective topic model for social emotion detection
Pages: 29-37
Author(s): Yanghui Rao, Qing Li, Liu Wenyin, Qingyuan Wu, Xiaojun Quan
   
5. Modeling virtual organizations with Latent Dirichlet Allocation: A case for natural language processing
Pages: 38-49
Author(s): Alexander Gross, Dhiraj Murthy
   
6. Semi-supervised word polarity identification in resource-lean languages
Pages: 50-59
Author(s): Iman Dehdarbehbahani, Azadeh Shakery, Heshaam Faili
   
7. Incorporating conditional random fields and active learning to improve sentiment identification
Pages: 60-67
Author(s): Kunpeng Zhang, Yusheng Xie, Yi Yang, Aaron Sun, Hengchang Liu, Alok Choudhary
   
8. A classification of user-generated content into consumer decision journey stages
Pages: 68-81
Author(s): Silvia Vázquez, Óscar Muñoz-García, Inés Campanella, Marc Poch, Beatriz Fisas, Nuria Bel, Gloria Andreu
   
9. Sentiments analysis at conceptual level making use of the Narrative Knowledge Representation Language
Pages: 82-97
Author(s): Gian Piero Zarri
   
10. Exploring personalized searches using tag-based user profiles and resource profiles in folksonomy
Pages: 98-110
Author(s): Yi Cai, Qing Li, Haoran Xie, Huaqin Min
   
11. Community-aware user profile enrichment in folksonomy
Pages: 111-121
Author(s): Haoran Xie, Qing Li, Xudong Mao, Xiaodong Li, Yi Cai, Yanghui Rao
   
12. A multi-label, semi-supervised classification approach applied to personality prediction in social media
Pages: 122-130
Author(s): Ana Carolina E.S. Lima, Leandro Nunes de Castro
   
13. Semantically-based priors and nuanced knowledge core for Big Data, Social AI, and language understanding
Pages: 131-147
Author(s): Daniel Olsher