Dear Moderator,
Please publish the folowing CFP in your next issue
kind regards
Nazmul Siddique
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS (Impact Factor: 4.284, Ranking in the category Computer Science, Artificial Intelligence, 4 out of 111)
Founded in 1989
Spiking Neural Networks
Editor-in-Chief:
Hojjat Adeli, The Ohio State University, Columbus, Ohio, U.S.A. (Email: Adeli.1@osu.edu)
Guest Editors:
Dr. Nazmul Siddique, School of Computing and Intelligent Systems, University of Ulster, UK,
e-mail: nh.siddique@ulster.ac.uk,
Prof. Bernard Widrow, Electrical Engineering Department, Stanford University, USA
e-mail: widrow@stanford.edu,
Prof. Liam Maguire, School of Computing and Intelligent Systems, University of Ulster, UK,
e-mail: lp.maguire@ulster.ac.uk,
Spiking neural networks (SNN) are a present trend in neural networks' research that is increasingly receiving attention in the research community as both a computationally powerful and biologically more plausible model of neural processing. Such networks so far have focused on fundamental issues like biologically plausible architectures, biological learning rules and applications considering computational complexity. Already existing models and architectures have found a broad application area such as control, speech and signal processing, pattern recognition, sensory fusion and hardware realisation.
There have been numerous models of SNN proposed: some considered complex biological realism, some supported learning algorithms, some emphasised on hardware realisation and some focused on specific application. But one important issue associated with all these models, applications and learning of SNN is the fact that they are typically computationally more intensive than traditional neural networks. Therefore, the designer has to compromise various factors in order to develop an SNN application. Undoubtedly there are a wide range of models of SNN available which simulate the behaviour of a biological neuron but the choice of which greatly depends on the specific applications. The architecture has to be rationalised in order to reduce computational cost. Like in classical NN, the learning in SNN can occur in a supervised or unsupervised manner. There are several methodologies to date for implementing supervised and unsupervised learning in SNN. But there are still much to explore about architecture, learning and implementation in SNN.
Issue 20:6, December 2010, and 22:4, August 2012, were special issues devoted to SNN. Based on the success of those issues this special issue will solicit latest development in applications, learning algorithm, computational complexity, and implementation. Topics of interest include, but are not limited to: Application specific architectures, Learning algorithms, Implementation of SNN's and Applications of SNN to Digital Signal Processing, Biomedical, Image, Speech and Audio Processing, Communication Systems and Networks, Control Systems, Robotics and Automation, Intelligent Systems and Bio-Inspired Systems.
Please inform the guest editors and the Editor-in-Chief about your intention to submit a manuscript for possible publication in the special issue as soon as possible. Please email your original contribution as a pdf file along with the attached Conscientious Reviewer form as a Word file and the statement "the manuscript is original unpublished work and has not been submitted for possible publication elsewhere previously" to one of the Guest Editors with a copy to the Editor-in-Chief by April 1, 2013. Submissions will be reviewed expeditiously. You may use the attached journal flyer to request a sample complimentary copy of the journal. Author's guidelines can be found at http://ejournals.wspc.com.sg/authors/index.shtml.
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