Short Bio
Professor Nikola K Kasabov is a Life Fellow of IEEE, Fellow of the Royal Society of New Zealand, Fellow of the INNS College of Fellows, DVF of the Royal Academy of Engineering UK. He has Doctor Honoris Causa from Obuda University, Budapest. He is the Founding Director KEDRI and Professor at the School of Engineering, Computing and Mathematical Sciences at Auckland University of Technology, New Zealand. He is also George Moore Chair Professor of Data Analytics at the University of Ulster UK and a Visiting Professor at IICT Bulgarian Academy of Sciences and Dalian University, China. He is also Honorary Professor at the Teesside University UK, University of Auckland NZ and Peking University in Shenzhen. Kasabov is Past President of the Asia Pacific Neural Network Society (APNNS) and the International Neural Network Society (INNS). He has been a chair and a member of several technical committees of IEEE Computational Intelligence Society and Distinguished Lecturer of IEEE (2012-2014). He is Editor of Springer Handbook of Bio-Neuroinformatics, EiC of Springer Series of Bio-and Neuro-systems and co-EiC of the Springer journal Evolving Systems. He is Associate Editor of several other journals. Kasabov holds MSc in computer engineering and PhD in mathematics from 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 700 publications, highly cited internationally. He has extensive academic experience at various academic and research organisations in Europe and Asia, including: TU Sofia Bulgaria; University of Essex UK; University of Otago, NZ; Shanghai Jiao Tong University and CASIA Beijing, ETH/University of Zurich. Kasabov has received a number of awards, among them: INNS Ada Lovelace Meritorious Service Award; NN journal Best Paper Award for 2016; APNNA ‘Outstanding Achievements Award’; INNS Gabor Award for ‘Outstanding contributions to engineering applications of neural networks’; EU Marie Curie Fellowship; Bayer Science Innovation Award; APNNA Excellent Service Award; RSNZ Science and Technology Medal; 2015 AUT NZ Medal; Medal “Bacho Kiro” and Honorary Citizen of Pavlikeni, Bulgaria; Honorary Member of the Bulgarian-, the Greek- and the Scottish Societies for Computer Science. More information of Prof. Kasabov can be found in: https://academics.aut.ac.nz/nkasabov.
Abstract
The majority of data that is dealt with across information and data sciences, are temporal or spatio/spectro temporal, including: biological and brain signals; audio-visual; environmental; financial and economic; communication. In many cases this data is simplified just as temporal or spatial, due to lack of computational models to model both spatial and temporal components of the data in their dynamic interaction and integration.
The talk introduces first the concepts of spatio-temporal learning (STL) and spatio-temporal associative memories (STAM). These are evolvable and explainable learning systems that are first structured according to spatial-, spectral or other relevant information from temporal or spatio-temporal data and then they are trained to further evolve their structure by learning spatio-temporal associations of the data resulting in explainable models. If a STAM system is activated with a smaller proportion of input data/stimuli, the system recalls previously learned spatio-temporal patterns to classify the input data or make a prediction. The talk briefly illustrates the concepts of STL and STAM as inherent features in biology and the human brain. Mathematical foundations for STL and STAM include: spatio-temporal (ST) encoding of streaming data; ST clustering; ST searching algorithms; STL learning algorithms; ST quantum computation; algebraic ST transformations; chaotic ST systems.
The last part of the talk presents a brain-inspired neurocomputation framework NeuCube [1,2,3], its STL algorithm and its use for STAM. It demonstrates its applications for classification and prediction of biological and brain signals, audio-visual data, environmental data, financial and economic data. When compared to traditional machine learning techniques, including deep neural networks, these systems demonstrate significantly better accuracy and a clear interpretability and explainability of the dynamics of the ST data. These systems are more energy efficient, as during STL, the spatial structure of the model helps to learn data faster and to recall it associatively.
[1] N.Kasabov, NeuCube: A spiking neural network architecture for mapping, learning and understanding of spatio-temporal brain data, Neural Networks, vol. 52, pp. 62-76, 2014.
[2] NeuCube: https://kedri.aut.ac.nz/neucube
[3] N.Kasabov, Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence, Springer, 2019, https://www.springer.com/gp/book/9783662577134.
In person on Newmarket Campus in Building 902: 902-402 – 262 KHYBER PASS – Seminar Room (48)
Join online via Zoom Webinar: https://auckland.zoom.us/j/94663284875