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

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

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

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.

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

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