Nikola Kirilov Kasabov is Professor of neural networks and knowledge engineering and Director of the Knowledge Engineering and Discovery Research Institute (KEDRI) at the Auckland University of Technology (AUT), New Zealand. Born in Bulgaria, he has worked previously at the TU Sofia, University of Essex and University of Otago. He is fellow of IEEE, Fellow of the Royal Society (Academy) of New Zealand (RSNZ), Distinguished Fellow of the Royal Academy of Engineering UK and Visiting Professor at several universities, including: Shanghai Jia-Tong University; ETH and University of Zurich; RGU Scotland UK; University of Trento; University of Kaiserslautern; Universities of Twente and Maastricht. Prof Kasabov originated methods and systems for intelligent information processing, including: evolving connectionist systems, hybrid neuro-fuzzy systems, evolving- and brain -inspired spiking neural network architectures, quantum-inspired methods, methods for personalised modelling in bio and neuroinformatics, published in more than 600 works. He is Past President of the International Neural Network Society (INNS) and the current President of the Asia-Pacific Neural Network Society (APNNS). Prof Kasabov has received the INNS Ada Lovelace and Gabor Awards, APNNS Outstanding Achievements Award, RSNZ Medal, AUT Medal, Honourable Fellowship of the Bulgarian and the Greek Computer Societies, Pavlikeni Honourable Citizenship and other awards. He has been the editor of the Springer Handbook of Bio-/Neuro-informatics published by Springer in 2014 and of the related book series Springer Series on Bio- and Neurosystems.

Presents background knowledge and new generic methods for spiking neural networks, evolving spiking neural networks and brain-inspired spiking neural networks

Describes new specific methods for the creation of BI-AI systems

Focuses on applications such as modeling and analysis of time-space data

Includes supplementary material: [...]

Part I. Time-Space and AI.- Part II. The Human Brain.- Part III. Spiking Neural Networks.- Part IV. SNN for Deep Learning and Deep Knowledge Representation of Brain Data.- Part V. SNN for Audio-Visual Data and Brain-Computer Interfaces.- Part VI. SNN in Bio- and Neuroinformatics.- Part VII. SNN for Deep in Time-Space Learning and Deep Knowledge Representation of Multisensory Streaming Data.- Part VIII. Future development in BI-SNN and BI-AI.