Log in

Login to your account

Username *
Password *
Remember Me
From Brain Science to Intelligent Machines

Evolving Spiking Neural Networks: Methods, Systems and Applications for Spatio- and Spectro-Temporal Pattern Recognition

Date: Thursday 31/10/2013
Venue: MS020
Time: 12.00 pm
Speaker: Prof. Nikola Kasabov
Affiliation: Director, Knowledge Engineering and Discovery Research Institute (KEDRI)
Auckland University of Technology



Evolving Spiking Neural Networks: Methods, Systems and Applications for Spatio- and Spectro-Temporal Pattern Recognition
By

 Prof. Nikola Kasabov

Director, Knowledge Engineering and Discovery Research Institute (KEDRI)
Auckland University of Technology




Abstract:


Spatio- and spectro-temporal data (SSTD) are the most common data in many domain areas, including: signal processing; bioinformatics; neuroinformatics; ecology; environment; medicine; economics, etc., and still there are no efficient methods to model such data and to discover complex spatio-temporal patterns from it. The talk introduces new methods for modeling and pattern recognition of SSTD based on evolving spiking neural networks (eSNN). eSNN develop their structure and functionality from streams of data in an on-line learning mode [1]. They consist of: input encoding module; 3D SNN reservoir structure; eSNN classifier. They can include also gene regulatory networks as [2]. Different eSNN models are presented, such as: probabilistic neuronal model [3], the dynamic evolving SNN (deSNN) [4], SPAN [5], reservoir SNN NeuCube [6]; quantum inspired SNN [7] and others.
Applications across domain areas are demonstrated, including: moving object recognition [4]; integrated audio-visual pattern recognition [7]; EEG data modeling [8]; design of artificial cognitive and emotional systems [10]. Challenging open problems and future directions are presented [11,12] .
Software systems for building SNN are demonstrated introduced the KEDRI Python EvoSpike simulator and the KEDRI MATLAB NeuCube simulator.

Short biography:

Professor Nikola Kasabov is Fellow of the IEEE, Fellow of the Royal Society of New Zealand and Distinguished Visiting Fellow of the Royal Academy of Engineering. He is the Director of the Knowledge Engineering and Discovery Research Institute (KEDRI), Auckland. He holds a Chair of Knowledge Engineering at the School of Computing and Mathematical Sciences at Auckland University of Technology. Kasabov is a Past President and Governors Board member of the International Neural Network Society (INNS) and also of the Asia Pacific Neural Network Assembly (APNNA). He is a member of several technical committees of IEEE Computational Intelligence Society and a Distinguished Lecturer of the IEEE CIS. He is a Co-Editor-in-Chief of the Springer journal Evolving Systems and has served as Associate Editor of Neural Networks, IEEE TrNN, IEEE TrFS, Information Science, J. Theoretical and Computational Nanosciences, Applied Soft Computing and other journals. Kasabov holds MSc and PhD from the TU Sofia, Bulgaria. His main research interests are in the areas of neural networks, intelligent information systems, soft computing, bioinformatics, neuroinformatics. He has published more than 510 publications that include 15 books, 160 journal papers, 80 book chapters, 28 patents and numerous conference papers. He has extensive academic experience at various academic and research organisations in Europe and Asia, including: TU Sofia, University of Essex, University of Otago, Guest professor at the Shanghai Jiao Tong University, Guest Professor at ETH/University of Zurich. Prof. Kasabov has received the APNNA ‘Outstanding Achievements Award’, the INNS Gabor Award for ‘Outstanding contributions to engineering applications of neural networks’, the EU Marie Curie Fellowship, the Bayer Science Innovation Award, the APNNA Excellent Service Award, the RSNZ Science and Technology Medal, and others. He has supervised to completion 35 PhD students. More information of Prof. Kasabov can be found on the KEDRI web site: http://www.kedri.aut.ac.nz.