Professor 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, nkasabov@aut.ac.nz, www.kedri.info
President of the INNS (International Neural Network Society)
Title:
Evolving, Probabilistic Spiking Neural Network Reservoirs for Spatio- and Spectro-Temporal Data
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 novel evolving probabilistic spiking neural network reservoir (epSNNr) architecture. The epSNNr are build of probabilistic neurons [1] that extent the popular integrate-and-fire models with the introduction of some biologically plausible probabilistic parameters. epSNNr allow to model stochastic processes, to learn noisy SSTD, and to efficiently recognize complex patterns from incoming streams of SSTD. The epSNNr learn whole ‘chunks’ of input SSTD, rather than learning the data from single time frames. The epSNNr are evolving structures that learn and adapt to new incoming data streams in a fast incremental way [2]. To control the numerous parameters of the epSNNr a gene regulatory network (GRN) is introduced [3,4], to obtain a computational neuro-genetic model (CNGM).
Applications 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.
References
[1] N.Kasabov, To spike or not to spike: A probabilistic spiking neural model, Neural Networks, Volume 23, Issue 1, January 2010, Pages 16-19
[2] N.Kasabov (2007) Evolving Connectionist Systems: The Knowledge Engineering Approach, Springer, London (www.springer.de)
[3] L.Benuskova and N.Kasabov (2007) Computational Neurogenetic Modelling, Springer, New York
[4] 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, in print
Biosketch:
Founding Director and the Chief Scientist of the Knowledge Engineering and Discovery Research Institute (KEDRI), Auckland (www.kedri.info/). 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 the Royal Society of New Zealand, Fellow of the New Zealand Computer Society and a Senior Member of IEEE. He is the 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 the IEEE Computational Intelligence Society and of the IFIP AI TC12. Kasabov is Associate Editor of several international journals, that include Neural Networks, IEEE TrNN, IEEE TrFS, Information Science, J. Theoretical and Computational Nanosciences. He chairs a series of int. conferences ANNES/NCEI in New Zealand. Kasabov holds MSc and PhD from the Technical University of Sofia. His main research interests are in the areas of intelligent information systems, soft computing, neuro-computing, bioinformatics, brain study, speech and image processing, novel methods for data mining and knowledge discovery. He has published more than 400 publications that include 15 books, 120 journal papers, 60 book chapters, 32 patents and numerous conference papers. He has extensive academic experience at various academic and research organisations: University of Otago, New Zealand; University of Essex, UK; University of Trento, Italy; Technical University of Sofia, Bulgaria; University of California at Berkeley; RIKEN and KIT, Japan; TUniversity Kaiserslautern, Germany, and others.
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