Neural Networks, Volume 106

Volume 106, October 2018

Mathematical and Computational Analysis

• 
  Neyir Ozcan:
  New conditions for global stability of neutral-type delayed Cohen-Grossberg neural networks. 1-7

Learning Systems

• 
  Jingliang Sun, Chunsheng Liu:
  Distributed zero-sum differential game for multi-agent systems in strict-feedback form with input saturation and output constraint. 8-19
• 
  Jianyuan Sun, Guoqiang Zhong, Kaizhu Huang, Junyu Dong:
  Banzhaf random forests: Cooperative game theory based random forests with consistency. 20-29

Neuroscience

• 
  Callie Federer, Joel Zylberberg:
  A self-organizing short-term dynamical memory network. 30-41

Learning Systems

• 
  Yiqun Yao, Jiaming Xu, Jing Shi, Bo Xu:
  Learning to activate logic rules for textual reasoning. 42-49

Mathematical and Computational Analysis

• 
  Sang-Yoon Kim, Woochang Lim:
  Effect of inhibitory spike-timing-dependent plasticity on fast sparsely synchronized rhythms in a small-world neuronal network. 50-66
• 
  Myeong-Jin Park, O. M. Kwon, Ji-Hyoung Ryu:
  Passivity and stability analysis of neural networks with time-varying delays via extended free-weighting matrices integral inequality. 67-78

Engineering and Applications

• 
  Shabab Bazrafkan, Shejin Thavalengal, Peter Corcoran:
  An end to end Deep Neural Network for iris segmentation in unconstrained scenarios. 79-95

Learning Systems

• 
  Jingjing Tang, Yingjie Tian, Xiaohui Liu, Dewei Li, Jia Lv, Gang Kou:
  Improved multi-view privileged support vector machine. 96-109

Mathematical and Computational Analysis

• 
  Raman Manivannan, Satyananda Panda, Kil To Chong, Jinde Cao:
  An Arcak-type state estimation design for time-delayed static neural networks with leakage term based on unified criteria. 110-126
• 
  Qinbing Fu, Cheng Hu, Jigen Peng, Shigang Yue:
  Shaping the collision selectivity in a looming sensitive neuron model with parallel ON and OFF pathways and spike frequency adaptation. 127-143
• 
  Ross D. Pantone, Jack D. Kendall, Juan C. Nino:
  Memristive nanowires exhibit small-world connectivity. 144-151

Learning Systems

• 
  Kai Wang, Weiyi Meng, Jiang Bian, Shijun Li, Sha Yang:
  Spatial context-aware user mention behavior modeling for mentionee recommendation. 152-167

Neuroscience

• 
  Francesca Miraglia, Fabrizio Vecchio, Paolo Maria Rossini:
  Brain electroencephalographic segregation as a biomarker of learning. 168-174

Learning Systems

• 
  Xiao-Lei Xia:
  Training sparse least squares support vector machines by the QR decomposition. 175-184

Engineering and Applications

• 
  Mohammad Ahangar Kiasari, Dennis Singh Moirangthem, Minho Lee:
  Joint moment-matching autoencoders. 185-193
• 
  Laura Cohen, Aude Billard:
  Social babbling: The emergence of symbolic gestures and words. 194-204

Mathematical and Computational Analysis

• 
  Adriano Barra, Matteo Beccaria, Alberto Fachechi:
  A new mechanical approach to handle generalized Hopfield neural networks. 205-222
• 
  Laura E. Suárez, Jack D. Kendall, Juan C. Nino:
  Evaluation of the computational capabilities of a memristive random network (MN³) under the context of reservoir computing. 223-236

Learning Systems

• 
  Feng Li, Wang Xiang, Jiaxu Wang, Xueming Zhou, Baoping Tang:
  Quantum weighted long short-term memory neural network and its application in state degradation trend prediction of rotating machinery. 237-248
• 
  Mateusz Buda, Atsuto Maki, Maciej A. Mazurowski:
  A systematic study of the class imbalance problem in convolutional neural networks. 249-259

Mathematical and Computational Analysis

• 
  Ning Li, Wei Xing Zheng:
  Synchronization criteria for inertial memristor-based neural networks with linear coupling. 260-270

Learning Systems

• 
  Angshul Majumdar:
  Graph structured autoencoder. 271-280

Mathematical and Computational Analysis

• 
  Huabin Chen, Peng Shi, Cheng-Chew Lim:
  Pinning impulsive synchronization for stochastic reaction-diffusion dynamical networks with delay. 281-293

Learning Systems

• 
  Chang Li, Maarten de Rijke:
  Incremental sparse Bayesian ordinal regression. 294-302