International Neural Network Society: 2013

Monday, 23 December 2013

Neural Networks Volume 50 Pages 1-182 February 2014

Neural Networks Letters

1. Existence and global exponential stability of periodic solution for high-order discrete-time BAM neural networks
Pages: 98-109
Author(s): Ancai Zhang, Jianlong Qiu, Jinhua She

2. Cellular computational networks—A scalable architecture for learning the dynamics of large networked systems
Pages: 120-123
Author(s): Bipul Luitel, Ganesh Kumar Venayagamoorthy

Cognitive Science

3. Supervised orthogonal discriminant subspace projects learning for face recognition
Pages: 33-46
Author(s): Yu Chen, Xiao-Hong Xu

Learning Systems

4. Direct Kernel Perceptron (DKP): Ultra-fast kernel ELM-based classification with non-iterative closed-form weight calculation
Pages: 60-71
Author(s): Manuel Fernández-Delgado, Eva Cernadas, Senén Barro, Jorge Ribeiro, José Neves

5. Batch gradient method with smoothing image regularization for training of feedforward neural networks
Pages: 72-78
Author(s): Wei Wu, Qinwei Fan, Jacek M. Zurada, Jian Wang, Dakun Yang, Yan Liu

6. Compressed classification learning with Markov chain samples
Pages: 90-97
Author(s): Feilong Cao, Tenghui Dai, Yongquan Zhang, Yuanpeng Tan

7. Semi-supervised learning of class balance under class-prior change by distribution matching
Pages: 110-119
Author(s): Marthinus Christoffel du Plessis, Masashi Sugiyama

8. Robust support vector machine-trained fuzzy system
Pages: 154-165
Author(s): Yahya Forghani, Hadi Sadoghi Yazdi

9. Large-scale linear nonparallel support vector machine solver
Pages: 166-174
Author(s): Yingjie Tian, Yuan Ping

10. Finite time convergent learning law for continuous neural networks
Pages: 175-182
Author(s): Isaac Chairez

Mathematical and Computational Analysis

11. A Bayesian inverse solution using independent component analysis
Pages: 47-59
Author(s): Jouni Puuronen, Aapo Hyvärinen

12. A one-layer recurrent neural network for constrained nonsmooth invex optimization
Pages: 79-89
Author(s): Guocheng Li, Zheng Yan, Jun Wang

13. Pointwise probability reinforcements for robust statistical inference
Pages: 124-141
Author(s): Benoît Frénay, Michel Verleysen

14. A linear recurrent kernel online learning algorithm with sparse updates
Pages: 142-153
Author(s): Haijin Fan, Qing Song

Engineering and Applications

15. Correcting and combining time series forecasters
Pages: 1-11
Author(s): Paulo Renato A. Firmino, Paulo S.G. de Mattos Neto, Tiago A.E. Ferreira

16. Hybrid fault diagnosis of nonlinear systems using neural parameter estimators
Pages: 12-32
Author(s): E. Sobhani-Tehrani, H.A. Talebi, K. Khorasani

Friday, 6 December 2013

Neural Networks Volume 48 Pages 1-208

Neural Networks Letters

1. Is mutual information adequate for feature selection in regression?
Pages: 1-7
Author(s): Benoît Frénay, Gauthier Doquire, Michel Verleysen

2. Neural architecture design based on extreme learning machine
Pages: 19-24
Author(s): Andrés Bueno-Crespo, Pedro J. García-Laencina, José-Luis Sancho-Gómez

3. Comments on the “No-Prop” algorithm
Pages: 59-60
Author(s): Meng-Hiot Lim

4. Exponential stabilization of delayed recurrent neural networks: A state estimation based approach
Pages: 153-157
Author(s): He Huang, Tingwen Huang, Xiaoping Chen, Chunjiang Qian

Neuroscience

5. A model of task-specific focal dystonia
Pages: 25-31
Author(s): Eckart Altenmüller, Dieter Müller

6. Global exponential synchronization of memristor-based recurrent neural networks with time-varying delays
Pages: 195-203
Author(s): Shiping Wen, Gang Bao, Zhigang Zeng, Yiran Chen, Tingwen Huang

Learning Systems

7. An efficient matrix bi-factorization alternative optimization method for low-rank matrix recovery and completion
Pages: 8-18
Author(s): Yuanyuan Liu, L.C. Jiao, Fanhua Shang, Fei Yin, F. Liu

8. Analysis of programming properties and the row–column generation method for 1-norm support vector machines
Pages: 32-43
Author(s): Li Zhang, WeiDa Zhou

9. Fully corrective boosting with arbitrary loss and regularization
Pages: 44-58
Author(s): Chunhua Shen, Hanxi Li, Anton van den Hengel

10. Fixed-final-time optimal control of nonlinear systems with terminal constraints
Pages: 61-71
Author(s): Ali Heydari, S.N. Balakrishnan

11. Solving graph data issues using a layered architecture approach with applications to web spam detection
Pages: 78-90
Author(s): Franco Scarselli, Ah Chung Tsoi, Markus Hagenbuchner, Lucia Di Noi

12. Fuzzy rough sets, and a granular neural network for unsupervised feature selection
Pages: 91-108
Author(s): Avatharam Ganivada, Shubhra Sankar Ray, Sankar K. Pal

13. Categorization and decision-making in a neurobiologically plausible spiking network using a STDP-like learning rule
Pages: 109-124
Author(s): Michael Beyeler, Nikil D. Dutt, Jeffrey L. Krichmar

14. 1-norm support vector novelty detection and its sparseness
Pages: 125-132
Author(s): Li Zhang, WeiDa Zhou

15. On the construction of the relevance vector machine based on Bayesian Ying-Yang harmony learning
Pages: 173-179
Author(s): Dansong Cheng, Minh Nhut Nguyen, Junbin Gao, Daming Shi

Wednesday, 4 December 2013

Neural Networks new articles 25 November - 1 December, 2013

1. Feature selection and multi-kernel learning for sparse representation on a manifold
Jim Jing-Yan Wang, Halima Bensmail, Xin Gao

2. Large-scale linear Nonparallel Support Vector Machine solver
Yingjie Tian, Yuan Ping

3. Neural network for solving convex quadratic bilevel programming problems
Xing He, Chuandong Li, Tingwen Huang, Chaojie Li

Wednesday, 27 November 2013

Neural Networks new articles 19-25 November, 2013

1. Compressed classification learning with Markov chain samples
Feilong Cao, Tenghui Dai, Yongquan Zhang, Yuanpeng Tan

2. Existence and global exponential stability of periodic solution for high-order discrete-time BAM neural networks
Ancai Zhang, Jianlong Qiu, Jinhua She

3. Pointwise probability reinforcements for robust statistical inference
Benoît Frénay, Michel Verleysen

4. Robust support vector machine-trained fuzzy system
Yahya Forghani, Hadi Sadoghi Yazdi

5. A linear recurrent kernel online learning algorithm with sparse updates
Haijin Fan, Qing Song

6. A one-layer recurrent neural network for constrained nonsmooth invex optimization
Guocheng Li, Zheng Yan, Jun Wang

7. Semi-supervised learning of class balance under class-prior change by distribution matching
Marthinus Christoffel du Plessis, Masashi Sugiyama

8. Batch gradient method with smoothing image regularization for training of feedforward neural networks
Wei Wu, Qinwei Fan, Jacek M. Zurada, Jian Wang, Dakun Yang, Yan Liu

Tuesday, 17 September 2013

Neural Networks Volume 47 Pages 1-150

1. Computation in the Cerebellum
Author(s): Dieter Jaeger, Henrik Jorntell, Mitsuo Kawato
Pages: 1-2

2. The importance of stochastic signaling processes in the induction of long-term synaptic plasticity
Author(s): Erik De Schutter
Pages: 3-10

3. Dendritic calcium signaling in cerebellar Purkinje cell
Author(s): Kazuo Kitamura, Masanobu Kano
Pages: 11-17

4. Bistability in Purkinje neurons: Ups and downs in cerebellar research
Author(s): Jordan D.T. Engbers, Fernando R. Fernandez, Ray W. Turner
Pages: 18-31

5. Mechanisms producing time course of cerebellar long-term depression
Author(s): Taegon Kim, Keiko Tanaka-Yamamoto
Pages: 32-35

6. Cerebellar LTD vs. motor learning—Lessons learned from studying GluD2
Author(s): Michisuke Yuzaki
Pages: 36-41

7. Adaptive coupling of inferior olive neurons in cerebellar learning
Author(s): Isao T. Tokuda, Huu Hoang, Nicolas Schweighofer, Mitsuo Kawato
Pages: 42-50

8. Solution to the inverse problem of estimating gap-junctional and inhibitory conductance in inferior olive neurons from spike trains by network model simulation
Author(s): Miho Onizuka, Huu Hoang, Mitsuo Kawato, Isao T. Tokuda, Nicolas Schweighofer, Yuichi Katori, Kazuyuki Aihara, Eric J. Lang, Keisuke Toyama
Pages: 51-63

9. Nucleo-olivary inhibition balances the interaction between the reactive and adaptive layers in motor control
Author(s): Ivan Herreros, Paul F.M.J. Verschure
Pages: 64-71

10. Transfer of memory trace of cerebellum-dependent motor learning in human prism adaptation: A model study
Author(s): Soichi Nagao, Takeru Honda, Tadashi Yamazaki
Pages: 72-80

11. Classical conditioning of motor responses: What is the learning mechanism?
Author(s): Germund Hesslow, Dan-Anders Jirenhed, Anders Rasmussen, Fredrik Johansson
Pages: 81-87

12. Cross-correlations between pairs of neurons in cerebellar cortex in vivo
Author(s): Fredrik Bengtsson, Pontus Geborek, Henrik Jörntell
Pages: 88-94

13. Using a million cell simulation of the cerebellum: Network scaling and task generality
Author(s): Wen-Ke Li, Matthew J. Hausknecht, Peter Stone, Michael D. Mauk
Pages: 95-102

14. Realtime cerebellum: A large-scale spiking network model of the cerebellum that runs in realtime using a graphics processing unit
Author(s): Tadashi Yamazaki, Jun Igarashi
Pages: 103-111

15. Modeling the generation of output by the cerebellar nuclei
Author(s): Volker Steuber, Dieter Jaeger
Pages: 112-119

16. Modeling cancelation of periodic inputs with burst-STDP and feedback
Author(s): K. Bol, G. Marsat, J.F. Mejias, L. Maler, A. Longtin
Pages: 120-133

17. Adaptive filters and internal models: Multilevel description of cerebellar function
Author(s): John Porrill, Paul Dean, Sean R. Anderson
Pages: 134-149

Tuesday, 9 July 2013

Neural Networks: 2-8 July, 2013

1. A model of task-specific focal dystonia
Author(s): Eckart Altenmüller, Dieter Müller

2. Universal approximation by radial basis function networks of Delsarte translates
Author(s): Cristian Arteaga, Isabel Marrero

3. Neural architecture design based on extreme learning machine
Author(s): Andrés Bueno-Crespo, Pedro J. García-Laencina, José-Luis Sancho-Gómez

4. Stochastic stability of discrete-time Markovian jump delay neural networks with impulses and incomplete information on transition probability
Author(s): Yu Zhang

Saturday, 1 June 2013

Registration for IJCNN 2013 is now open

Registration for IJCNN 2013 is now open. This conference will be held in Dallas, Texas, 4-9 August, 2013.

The deadline for early registration is 28 June 2013.

Friday, 31 May 2013

Reminder: Nominations for INNS Awards 2014

The deadline for these nominations is very soon!

The International Neural Network Society's Awards Program is established to recognize individuals who have made outstanding contributions in the field of Neural Networks. Up to three awards, at most one in each category, of $1,000 each, are presented annually to senior, highly accomplished researchers for outstanding contributions made in the field of Neural Networks.

The Hebb, Helmholtz and Gabor Awards:

The Hebb Award - recognizes achievement in biological learning.

The Helmholtz Award - recognizes achievement in sensation/perception.

The Gabor Award - recognizes achievement in engineering/application.

Young Investigator Awards:

Up to two awards of $500 each are presented annually to individuals with no more than five years postdoctoral experience and who are under forty years of age, for significant contributions in the field of Neural Networks.

Nominations:

1. The Awards Committee should receive nominations of no more than two pages in length, specifying:

The award category (Hebb, Helmholtz, Gabor, or Young Investigator) for which the candidate is being nominated.

The reasons for which the nominee should be considered for the award.

A list of at least five of the nominee's important and published papers.

2. The curriculum vitae of both the nominee and the nominator must be included with the nomination, including the name, address, position/title, phone, fax, and e-mail address for both the nominee and nominator.

3. The nominator must be an INNS member in good standing. Nominees do not have to be INNS members. If an award recipient is not an INNS member, they shall receive a free one-year INNS membership.

4. Nominators may not nominate themselves or their family members.

5. Individuals may not receive the same INNS Award more than once.

All nominations will be considered by the Awards Committee and selected ones forwarded to the INNS Board of Governors, along with the Committee's recommendations for award recipients. Voting shall be performed by the entire BoG.

The Awards Committee:

INNS Award Committee consists of the chair (Prof. Leonid Perlovsky) and two other members. All members must be INNS Governors in the year that they are appointed.

Please email the 2014 nominations along with their attachments directly to the chair of the Awards Committee at leonid@seas.harvard.edu, with a copy to the Secretary of the Society at hava@cs.umass.edu by June 1, 2013. Please use the following subject line in the email: INNS award nomination.

Neural Networks: 22-28 May

1. The learning problem of multi-layer neural networks
Author(s): Jung-Chao Ban, Chih-Hung Chang

2. Active learning for noisy oracle via density power divergence
Author(s): Yasuhiro Sogawa, Tsuyoshi Ueno, Yoshinobu Kawahara, Takashi Washio

3. Integer sparse distributed memory: Analysis and results
Author(s): Javier Snaider, Stan Franklin, Steve Strain, E. Olusegun George

4. Characterization of seizure-like events recorded in vivo in a mouse model of Rett syndrome
Author(s): Sinisa Colic, Robert G. Wither, Liang Zhang, James H. Eubanks, Berj L. Bardakjian

Sunday, 19 May 2013

Neural Networks: 7-13 May , 2013

1. Stochastic sampled-data control for state estimation of time-varying delayed neural networks
Author(s): Tae H. Lee, Ju H. Park, O.M. Kwon, S.M. Lee

2. Interval data clustering using self-organizing maps based on Mahalanobis distances
Author(s): Chantal Hajjar, Hani Hamdan

3. A mode-dependent approach to state estimation of recurrent neural networks with Markovian jumping parameters and mixed delays
Author(s): He Huang, Tingwen Huang, Xiaoping Chen

4. Gap junctions facilitate propagation of synchronous firing in the cortical neural population: A numerical simulation study
Author(s): Takashi Shinozaki, Yasushi Naruse, Hideyuki Câteau

5. Generation and annihilation of localized persistent-activity states in a two-population neural-field model
Author(s): M. Yousaf, B. Kriener, J. Wyller, G.T. Einevoll

6. A comment on “ image -stability and image -synchronization for fractional-order neural networks”
Author(s): Li Kexue, Peng Jigen, Gao Jinghuai

7. Neuron as a reward-modulated combinatorial switch and a model of learning behavior
Author(s): Marat M. Rvachev

Monday, 6 May 2013

Nominations for INNS Awards 2014

The International Neural Network Society's Awards Program is established to recognize individuals who have made outstanding contributions in the field of Neural Networks. Up to three awards, at most one in each category, of $1,000 each, are presented annually to senior, highly accomplished researchers for outstanding contributions made in the field of Neural Networks.

The Hebb, Helmholtz and Gabor Awards:

The Hebb Award - recognizes achievement in biological learning.

The Helmholtz Award - recognizes achievement in sensation/perception.

The Gabor Award - recognizes achievement in engineering/application.

Young Investigator Awards:

Up to two awards of $500 each are presented annually to individuals with no more than five years postdoctoral experience and who are under forty years of age, for significant contributions in the field of Neural Networks.

Nominations:

1. The Awards Committee should receive nominations of no more than two pages in length, specifying:

The award category (Hebb, Helmholtz, Gabor, or Young Investigator) for which the candidate is being nominated.

The reasons for which the nominee should be considered for the award.

A list of at least five of the nominee's important and published papers.

Thursday, 18 April 2013

Neural Networks Special Issue CFP

Affective and Cognitive Learning Systems for Big Social Data Analysis

Guest Editors
Amir Hussain (Lead Guest Editor), University of Stirling, United Kingdom (ahu@cs.stir.ac.uk)
Erik Cambria, National University of Singapore, Singapore (cambria@nus.edu.sg)
Björn Schuller, Technische Universität München, Germany (schuller@tum.de)
Newton Howard, MIT Media Laboratory, USA (nhmit@mit.edu)

Background and Motivation
As the Web rapidly evolves,Web users are evolving with it. In an era of social connectedness, people are becoming more and more enthusiastic about interacting, sharing, and collaborating through social networks, online communities, blogs, Wikis, and other online collaborative media. In recent years, this collective intelligence has spread to many different areas, with particular focus on fields related to everyday life such as commerce, tourism, education, and health, causing the size of the Web to expand exponentially. The distillation of knowledge from such a large amount of unstructured information, however, is an extremely difficult task, as the contents of today’s Web are perfectly suitable for human consumption, but remain hardly accessible to machines. The opportunity to capture the opinions of the general public about social events, political movements, company strategies, marketing campaigns, and product preferences has raised growing interest both within the scientific community, leading to many exciting open challenges, as well as in the business world, due to the remarkable benefits to be had from marketing and financial market prediction.

Existing approaches to opinion mining mainly rely on parts of text in which sentiment is explicitly expressed, e.g., through polarity terms or affect words (and their co-occurrence frequencies). However, opinions and sentiments are often conveyed implicitly through latent semantics, which make purely syntactical approaches ineffective. In this light, this Special Issue focuses on the introduction, presentation, and discussion of novel techniques that further develop and apply big data analysis tools and techniques for sentiment analysis. A key motivation for this Special Issue, in particular, is to explore the adoption of novel affective and cognitive learning systems to go beyond a mere word-level analysis of natural language text and provide novel concept-level tools and techniques that allow a more efficient passage from (unstructured) natural language to (structured) machine-processable data, in potentially any domain.

Articles are thus invited in areas such as machine learning, weakly supervised learning, active learning, transfer learning, deep neural networks, novel neural and cognitive models, data mining, pattern recognition, knowledge-based systems, information retrieval, natural language processing, and big data computing. Topics include, but are not limited to:

• Machine learning for big social data analysis
• Biologically inspired opinion mining
• Semantic multi-dimensional scaling for sentiment analysis
• Social media marketing
• Social media analysis, representation, and retrieval
• Social network modeling, simulation, and visualization
• Concept-level opinion and sentiment analysis
• Patient opinion mining
• Sentic computing
• Multilingual sentiment analysis
• Time-evolving sentiment tracking
• Cross-domain evaluation
• Domain adaptation for sentiment classification
• Multimodal sentiment analysis
• Multimodal fusion for continuous interpretation of semantics
• Human-agent, -computer, and -robot interaction
• Affective common-sense reasoning
• Cognitive agent-based computing
• Image analysis and understanding
• User profiling and personalization
• Affective knowledge acquisition for sentiment analysis

The Special Issue also welcomes papers on specific application domains of big social data analysis, e.g., influence networks, customer experience management, intelligent user interfaces, multimedia management, computer-mediated human-human communication, enterprise feedback management, surveillance, art. The authors will be required to follow the Author’s Guide for manuscript submission to Elsevier Neural Networks.

Timeframe
Call for Papers out: April 2013
Submission Deadline: August 1, 2013
Notification of Acceptance: November 1, 2013
Final Manuscripts Due: December 1, 2013
Date of Publication: March 2014

Composition and Review Procedures
The Elsevier Neural Networks Special Issue on Affective and Cognitive Learning Systems for Big Social Data Analysis will consist of papers on novel methods and techniques that further develop and apply big data analysis tools and techniques in the context of opinion mining and sentiment analysis. Some papers may survey various aspects of the topic. The balance between these will be adjusted to maximize the issue’s impact. All articles are expected to successfully negotiate the standard review procedures for Elsevier Neural Networks. Authors are required to follow Elsevier Neural Networks proceedings templates and to submit their manuscripts at http://ees.elsevier.com/neunet.

Monday, 8 April 2013

Neural Networks: New articles 2-8 April, 2013

1. Discriminant subspace learning constrained by locally statistical uncorrelation for face recognition
Author(s): Yu Chen, Wei-Shi Zheng, Xiao-Hong Xu, Jian-Huang Lai
Pages: 28-43

2. Probabilistic DHP adaptive critic for nonlinear stochastic control systems
Author(s): Randa Herzallah
Pages: 74-82

3. Learning in compressed space
Author(s): Alexander Fabisch, Yohannes Kassahun, Hendrik Wöhrle, Frank Kirchner
Pages: 83-93

4. Wavelet neural networks: A practical guide
Author(s): Antonios K. Alexandridis, Achilleas D. Zapranis
Pages: 1-27

5. A model of analogue winners-take-all neural circuit
Author(s): Pavlo V. Tymoshchuk
Pages: 44-61

6. Synthesis of high-complexity rhythmic signals for closed-loop electrical neuromodulation
Author(s): Osbert C. Zalay, Berj L. Bardakjian
Pages: 62-73

Thursday, 21 February 2013

Neural Networks: New articles 15-21 February 2013

1. Local minima in hierarchical structures of complex-valued neural networks
Author(s): Tohru Nitta

2. Nucleo-olivary inhibition balances the interaction between the reactive and adaptive layers in motor control
Author(s): Ivan Herreros, Paul F.M.J. Verschure

3. A supervised multi-spike learning algorithm based on gradient descent for spiking neural networks
Author(s): Yan Xu, Xiaoqin Zeng, Lixin Han, Jing Yang

4. Adaptive Markov chain Monte Carlo for auxiliary variable method and its application to parallel tempering
Author(s): Takamitsu Araki, Kazushi Ikeda

5. Efficient self-organizing multilayer neural network for nonlinear system modeling
Author(s): Hong-Gui Han, Li-Dan Wang, Jun-Fei Qiao

6. First experiments with PowerPlay
Author(s): Rupesh Kumar Srivastava, Bas R. Steunebrink, Jürgen Schmidhuber

Monday, 18 February 2013

Deadline extended for IJCNN 2013

The deadline for submitting papers to the International Joint Conference on Neural Networks (IJCNN) 2013 has been extended to 1 March 2013. Proposals for tutorials, workshops and panel sessions will also be accepted until this date. This conference will be held in Dallas, Texas, 4-9 August 2013.

Monday, 23 December 2013

Neural Networks Letters

Cognitive Science

Learning Systems

Mathematical and Computational Analysis

Engineering and Applications

Friday, 6 December 2013

Neural Networks Letters

Neuroscience

Learning Systems

Mathematical and Computational Analysis

Letter to the Editor

Erratum

Corrigendum

Wednesday, 4 December 2013

Wednesday, 27 November 2013

Tuesday, 17 September 2013

Tuesday, 9 July 2013

Saturday, 1 June 2013

Friday, 31 May 2013

Sunday, 19 May 2013

Monday, 6 May 2013

Thursday, 18 April 2013

Affective and Cognitive Learning Systems for Big Social Data Analysis

Monday, 8 April 2013

Thursday, 21 February 2013

Monday, 18 February 2013