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Neural Networks, Volume 88
Volume 88, April 2017
- Xiaozhao Fang

, Yong Xu, Xuelong Li
, Zhihui Lai, Shaohua Teng
, Lunke Fei:
Orthogonal self-guided similarity preserving projection for classification and clustering. 1-8
- Yang Li

, Makito Oku
, Guoguang He
, Kazuyuki Aihara:
Elimination of spiral waves in a locally connected chaotic neural network by a dynamic phase space constraint. 9-21
- Jiaming Xu, Bo Xu, Peng Wang, Suncong Zheng, Guanhua Tian, Jun Zhao:

Self-Taught convolutional neural networks for short text clustering. 22-31
- W. Shane Grant, James Tanner, Laurent Itti:

Biologically plausible learning in neural networks with modulatory feedback. 32-48
- Bin Yang, Juan Wang, Jun Wang:

Stability analysis of delayed neural networks via a new integral inequality. 49-57 - Raoul Borges, Filipe Borges, Ewandson Luiz Lameu

, Antonio M. Batista
, Kelly C. Iarosz
, Iberê L. Caldas, Chris G. Antonopoulos
, Murilo S. Baptista:
Spike timing-dependent plasticity induces non-trivial topology in the brain. 58-64
- Chen Liu, Yulin Zhu, Fei Liu, Jiang Wang, Huiyan Li, Bin Deng, Chris Fietkiewicz

, Kenneth A. Loparo
:
Neural mass models describing possible origin of the excessive beta oscillations correlated with Parkinsonian state. 65-73
- Linlin Zong

, Xianchao Zhang, Long Zhao, Hong Yu, Qianli Zhao:
Multi-view clustering via multi-manifold regularized non-negative matrix factorization. 74-89 - Risheng Liu

, Di Wang, Yuzhuo Han, Xin Fan, Zhongxuan Luo:
Adaptive low-rank subspace learning with online optimization for robust visual tracking. 90-104 - Mircea Serban Pavel, Hannes Schulz, Sven Behnke

:
Object class segmentation of RGB-D video using recurrent convolutional neural networks. 105-113
- Paulo S. G. de Mattos Neto

, Tiago A. E. Ferreira
, Aranildo R. Lima
, Germano C. Vasconcelos
, George D. C. Cavalcanti:
A perturbative approach for enhancing the performance of time series forecasting. 114-124
- Zhengwen Tu

, Jinde Cao
, Ahmed Alsaedi
, Fuad E. Alsaadi
:
Global dissipativity of memristor-based neutral type inertial neural networks. 125-133

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