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Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence / by Nikola K. Kasabov.

By: Kasabov, Nikola K [author.].
Series: Springer Series on Bio- and Neurosystems: 7Edition: 1st ed. 2019.Description: 1 online resource (XXXIV, 738 pages 340 illustrations, 256 illustrations in color.).ISBN: 9783662577134.Subject(s): Automation | Bioinformatics | Computational intelligence | Neurosciences | Pattern recognition | Robotics | Computational Intelligence | Computational Biology/Bioinformatics | Neurosciences | Pattern Recognition | Robotics and AutomationAdditional physical formats: Print version:: Time-space, spiking neural networks and brain-inspired artificial intelligence.; Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification: 006.3 Online resources: Full-text here
Contents:
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.
Summary: 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.
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Item type Current location Call number Status Date due Barcode Item holds
Book Skoltech library
Shelves 		Q335 .K221 2019 (Browse shelf) Available 2000007926
E-Book Skoltech library
Shelves 		Available
Total holds: 0

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.

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.

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