Spiking neural networks (SNN) are biologically inspired computational
models that represent and process information internally as trains of
spikes. This monograph book presents the classical theory and
applications of SNN, including original author's contribution to the
area. The book introduces for the first time not only deep learning and
deep knowledge representation in the human brain and in brain-inspired
SNN, but takes that further to develop new types of AI systems, called
in the book brain-inspired AI (BI-AI). BI-AI systems are illustrated on:
cognitive brain data, including EEG, fMRI and DTI; audio-visual data;
brain-computer interfaces; personalized modelling in
bio-neuroinformatics; multisensory streaming data modelling in finance,
environment and ecology; data compression; neuromorphic hardware
implementation. Future directions, such as the integration of multiple
modalities, such as quantum-, molecular- and brain information
processing, is presented in the last chapter. The book is a research
book for postgraduate students, researchers and practitioners across
wider areas, including computer and information sciences, engineering,
applied mathematics, bio- and neurosciences.