Overview
- 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: sn.pub/extras
Part of the book series: Springer Series on Bio- and Neurosystems (SSBN, volume 7)
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Table of contents (22 chapters)
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Time-Space and AI. Artificial Neural Networks
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The Human Brain
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Spiking Neural Networks
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Deep Learning and Deep Knowledge Representation of Brain Data
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SNN for Audio-Visual Data and Brain-Computer Interfaces
Keywords
- Deep knowledge representation
- Integration of human intelligence and artificial intelligence
- Deep learning of Time-Space data
- Spike-time learning
- Evolving spatio-temporal processes
- Interactions in Time-Space
- Evolving connectionist systems (ECOS)
- Transductive inference methods
- Knowledge-based ANN
- Evolving Fuzzy Neural Networks
- Supervised learning in ANN
- Convolutional ANN
- Training multilayer perceptron
- Evolving self-organizing maps
- Takagi-Sugeno fuzzy inference
- Neural Representation of Information
- Time-space in the brain
- Spike-Driven Synaptic Plasticity
- Reservoir architectures
- Quantum-inspired computation
About this book
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.
Authors and Affiliations
About the author
Bibliographic Information
Book Title: Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence
Authors: Nikola K. Kasabov
Series Title: Springer Series on Bio- and Neurosystems
DOI: https://doi.org/10.1007/978-3-662-57715-8
Publisher: Springer Berlin, Heidelberg
eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)
Copyright Information: Springer-Verlag GmbH Germany, part of Springer Nature 2019
Hardcover ISBN: 978-3-662-57713-4Published: 06 September 2018
Softcover ISBN: 978-3-662-58607-5Published: 19 January 2019
eBook ISBN: 978-3-662-57715-8Published: 29 August 2018
Series ISSN: 2520-8535
Series E-ISSN: 2520-8543
Edition Number: 1
Number of Pages: XXXIV, 738
Number of Illustrations: 84 b/w illustrations, 256 illustrations in colour
Topics: Computational Intelligence, Computational Biology/Bioinformatics, Neurosciences, Robotics and Automation, Pattern Recognition