Wednesday, 21 June 2017

Deadline extension -- Special Issue on Spiking Neural Networks for Cognitive and Developmental Systems, IEEE TCDS

It is my honor to cordially invite you to submit papers to the Special Issue on Spiking Neural Networks for Cognitive and Developmental Systems in IEEE Transactions on Cognitive and Developmental Systems journal.

The deadline for submission of papers has been extended to 10th of August 2017. The reviewing process takes about 3-4 weeks. Accepted papers will be published by the end of 2017.

The new schedule for this special issue is as follows:

10 August 2017 Deadline for manuscript submission
10 September 2017 Notification of authors
10 October 2017 Deadline for submission of revised manuscripts
31 October 2017 Final decisions
End of 2017 – Special Issue Publication in IEEE TCDS

Please refer to: http://cis.ieee.org/ieee-transactions-on-cognitive-and-developmental-systems.html for further information about the TCDS journal.

Link to special issue of SNN: http://bit.ly/2sQlc1Y

SUBMISSION

Manuscripts should be prepared according to the “Information for Authors” of the journal found at http://goo.gl/0eMHUd and submissions should be made through the IEEE TCDS Manuscript center at https://mc.manuscriptcentral.com/tcds-ieee selecting the category “SI: Spiking Neural Networks”.

AIM AND SCOPE

Spiking Neural Networks (SNN) are a rapidly emerging means of neural information processing, drawing inspiration from brain processes. They have the potential to advance technologies and techniques in fields as diverse as medicine, finance, computing, and indeed any field that involves complex temporal or spatiotemporal data. SNN, as the third generation of neural networks, can operate on noisy data, in changing environments at low power and with high effectiveness. Due to their basis in biological neural networks, SNN research is strongly positioned to benefit from advances made in the fields of molecular, evolutionary and cognitive neuroscience.
There is presently considerable interest in this topic. We believe that this area is quickly establishing itself as an effective alternative to traditional machine learning technologies, and the interest in this area of research is growing rapidly.

This special issue aims to bring together research works of contemporary areas of SNN, including theoretical, computational, application-oriented, experimental studies, and emerging technologies such as neuromorphic hardware.
THEMES

Topics relevant to this special issue include, but are not limited to:
·         Theory of SNN
·         Learning algorithms for SNN, including Deep Learning
·         Computation with and within SNN
·         Theory or practice in biologically realistic neural simulation or biomimetic models
·         Big data and stream data processing in SNN
·         Multiple sensor networks data processing in SNN
·         Neuromorphic hardware systems and applications
·         Optimization of SNN
·         SNN models of cognitive development
·         Information coding for SNN
·         SNN applications in neuroinformatics, bioinformatics, medicine and ecology
·         SNN in BCI
·         SNN in neuro-robotic
·         Any other topics relating to Spiking Neural Networks, their theory, or applications
Thus, the special issue reports state-of-the-art approaches, recent advances and the potential of SNN.
Editors

Professor Nikola Kasabov, Knowledge Engineering & Discovery Research Institute (KEDRI), Auckland University of Technology, New Zealand (nkasabov@aut.ac.nz).
N. Kasabov received MSc degree in Electrical Eng., spec. Computer Science, in 1971 and Ph.D. degree in Mathematical Sciences in 1975 from the Technical University in Sofia. He has published over 550 works in the areas of intelligent systems, neural networks, connectionist and hybrid connectionist systems, fuzzy systems, expert systems, bioinformatics, neuroinformatics. He is a Fellow of IEEE, Fellow of the Royal Society of New Zealand and a Distinguished Visiting Fellow of the RAE UK. He is a Past President of the International Neural Network Society (INNS) and the Asia Pacific Neural Network Assembly (APNNA) and currently - a member of the INNS and APNNA Governing Boards. Kasabov is the Director of the Knowledge Engineering and Discovery Research Institute (www.kedri.aut.ac.nz) and Personal Chair of Knowledge Engineering in Auckland University of Technology, New Zealand.

Dr Josafath I Espinosa-Ramos, Knowledge Engineering & Discovery Research Institute (KEDRI), Auckland University of Technology, New Zealand (josafath.ramos@aut.ac.nz)
Dr. Josafath Israel Espinosa Ramos holds a MSc in cybernetics from La Salle University, Mexico City, and a PhD in computer science at the Centre for Computing Research of the National Polytechnic Institute, Mexico City. His main research interests are in the areas of computational neuroscience, evolutionary algorithms and machine learning. Currently, he is working as a research officer at the Knowledge Engineering and Discovery Research Institute (KEDRI) in the Auckland University of Technology, New Zealand, applying spiking neural networks to model multisensory and multivariate streaming data. The aim of this research is analyzing the spatial and temporal relationships among the variables that describe the dynamics of a sensor network.

ASSOC Prof André Grüning, Department of Computing, University of Surrey, Guildford, Surrey (a.gruning@surrey.ac.uk).
Dr. Andre Grüning is a senior lecturer (associate professor) in the Department of Computer Science at the University of Surrey. His research focuses on learning algorithms for Spiking Neural Networks. He currently leads the Task “Functional plasticity for multi-compartment neurons” within the Human Brain Project.  Previously he held research positions in Computational Neuroscience Group at SISSA, Italy, in cognitive psychology at the University of Warwick and in Complex Systems at the Max-Planck-Institute for Mathematics in Sciences. He is a board member of the ENNS. Andre strongly believes that research into Spiking Neural Networks will both boost our understand of how the brain works and help us build more powerful computational devices.

Maryam Doborjeh, Knowledge Engineering & Discovery Research Institute (KEDRI), Auckland University of Technology, New Zealand (mgholami@aut.ac.nz)
M. G. Doborjeh obtained her MSc degree in Computer Science from Easter Mediterranean University, North Cyprus in 2012.  She is currently a PhD student and research assistant at the Knowledge Engineering and Discovery Research Institute (KEDRI) in the Auckland University of Technology, New Zealand. Her main research area is developing methods for the analysis of dynamic patterns in spatiotemporal data (such as EEG and fMRI data) using spiking neural network architecture.

Dr Joseph Chrol-Cannon, UK. (Joseph.Chrol-Cannon@surrey.ac.uk)
Dr. Joseph Chrol-Cannon received a PhD degree in Computer Science from the University of Surrey in 2016. The topic of his thesis was the application and analysis of synaptic plasticity in spiking networks for machine learning pattern recognition tasks. Since 2015, as a research fellow, he has been investigating spiking neuron encoding. Currently, he is working at the Surrey Technology Center, applying neural network learning in the area of finance.

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