Leseprobe
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Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence
Nikola K. Kasabov
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Naturwissenschaften, Medizin, Informatik, Technik / Allgemeines, Lexika
Beschreibung
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.
Kundenbewertungen
Reservoir architectures, Integration of human intelligence and artificial intelligence, Transductive inference methods, Knowledge-based ANN, Evolving spatio-temporal processes, Takagi-Sugeno fuzzy inference, Convolutional ANN, Deep knowledge representation, Interactions in Time-Space, Deep learning of Time-Space data, Evolving self-organizing maps, Spike-time learning, Spike-Driven Synaptic Plasticity, Supervised learning in ANN, Neural Representation of Information, Training multilayer perceptron, Quantum-inspired computation, Evolving Fuzzy Neural Networks, Time-space in the brain, Evolving connectionist systems (ECOS)