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

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. 

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 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.

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

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

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.

Neural Networks: Twenty-fifth Anniversay Commemorative Issue

1. Editorial Board
Pages: IFC

2. Neural networks
Pages: iv-ix

3. Neural networks referees in 2012
Pages: x-xii

4. Editorial
Pages: xiii-xiv

5. Adaptive Resonance Theory: How a brain learns to consciously attend, learn, and recognize a changing world
Pages: 1-47
Author: Stephen Grossberg

6. Dreaming of mathematical neuroscience for half a century   
Pages: 48-51
Author: Shun-ichi Amari

7. Essentials of the self-organizing map  
Pages: 52-65
Author: Teuvo Kohonen

8. Synthetic event-related potentials: A computational bridge between neurolinguistic models and experiments
Pages: 66-92
Authors: Victor Barrès, Arthur Simons, Michael Arbib

9. DISCOV (DImensionless Shunting COlor Vision): A neural model for spatial data analysis 
Pages: 93-102
Authors: Gail A. Carpenter, Suhas E. Chelian

10. Artificial vision by multi-layered neural networks: Neocognitron and its advances 
Pages: 103-119
Author: Kunihiko Fukushima

11. Outline of a general theory of behavior and brain coordination  
Pages: 120-131
Authors: J.A. Scott Kelso, Guillaume Dumas, Emmanuelle Tognoli

12. Noise-enhanced clustering and competitive learning algorithms
Pages: 132-140
Authors: Osonde Osoba, Bart Kosko

13. A neural model of visual figure-ground segregation from kinetic occlusion
Pages: 141-164
Authors: Timothy Barnes, Ennio Mingolla

14. Neural associative memories and sparse coding
Pages: 165-171
Author: Günther Palm

15. Local circuit inhibition in the cerebral cortex as the source of gain control and untuned suppression
Pages: 172-181
Authors: Robert M. Shapley, Dajun Xing

16. The No-Prop algorithm: A new learning algorithm for multilayer neural networks
Pages: 182-188
Authors: Bernard Widrow, Aaron Greenblatt, Youngsik Kim, Dookun Park