Spiking Neuro-Genetic Networks for Spatio-
and Spectro-Temporal Data Modelling
and Pattern Recognition
Prof. Nikola Kasabov, FIEEE, FRSNZ
EU FP7 Marie Cure Visiting Prof., Institute for Neuroinformatics, ETH and U. Zurich, Director, Knowledge Engineering and Discovery Research Institute (KEDRI)
Auckland University of Technology, firstname.lastname@example.org, www.kedri.info
Abstract: Spatio- and spectro-temporal data (SSTD) are the most common data in many domain areas, including bioinformatics, neuroinformatics, ecology, environment, medicine, economics, etc., and still there are no sufficient 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 a novel evolving probabilistic spiking neural network (epSNN) architecture. epSNN are build of probabilistic neuronal models  that extent the popular integrate-and-fire spiking models with the introduction of some biologically plausible probabilistic parameters. epSNN allow to model stochastic processes, to learn noisy SSTD, and to efficiently recognize complex patterns from incoming streams of SSTD. The epSNN learn whole ‘chunks’ of input SSTD, rather than learning the data from single time frames. The epSNN are evolving structures that learn and adapt to new incoming data streams in a fast incremental way . To control the numerous parameters of the epSNN a gene regulatory network (GRN) is introduced [3,4], to obtain spiking neuro-genetic network (SNGN) models .
Applications of SNGN across domain areas are demonstrated , including: moving object recognition; sound recognition; integrated audio-visual pattern recognition; EEG data modeling; design of artificial cognitive and emotional systems. Challenging open problems and future directions are presented.
 N.Kasabov, To spike or not to spike: A probabilistic spiking neural model, Neural Networks, Volume 23, Issue 1, January 2010, Pages 16-19
 N.Kasabov (2007)Evolving Connectionist Systems: The Knowledge Engineering Approach, Springer, London (www.springer.de)
 L.Benuskova and N.Kasabov(2007)Computational Neurogenetic Modelling, Springer, New York
 N.Kasabov, R.Schliebs, H.Kojima (2011) Probabilistic Computational Neurogenetic Framework: From Modelling Cognitive Systems to Alzheimer’s Disease, IEEE Transactions of Autonomous Mental Development
 N. Kasabov (2012) Computational modelling with spiking neuro-genetic networks, Springer, Heidelberg.
Professor Nikola Kasabov is currently EU FP7 Marie Curie Visiting Professor at the Institute of Neuroinformatics, ETH and University of Zurich. 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. He is a Fellow of IEEE and Fellow of the Royal Society of New Zealand. Kasabov is the Immediate Past President of the International Neural Network Society (INNS) and a Past President 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 society .He 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 Technical University of 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 450 publications that include 15 books, 130 journal papers, 60 book chapters, 28 patents and numerous conference papers. He has extensive academic experience at various academic and research organisations in Europe and Asia. Prof. Kasabov has received the AUT VC Individual Research Excellence Award (2010), Bayer Science Innovation Award (2007), the APNNA Excellent Service Award (2005), RSNZ Science and Technology Medal (2001), and others. He is an Invited Guest Professor at the Shanghai Jiao Tong University (2010-2012). More information of Prof. Kasabov can be found on the KEDRI web site: http://www.kedri.info.