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Time-space, spiking neural networks ...

Kasabov, Nikola K.

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Time-space, spiking neural networks and brain-inspired artificial intelligence

Record Type:	Electronic resources : Monograph/item
Title/Author:	Time-space, spiking neural networks and brain-inspired artificial intelligence/ by Nikola K. Kasabov.
Author:	Kasabov, Nikola K.
Published:	Berlin, Heidelberg :Springer Berlin Heidelberg : : 2019.,
Description:	xxxiv, 738 p. :ill., digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer eBooks
Subject:	Object-oriented methods (Computer science) -
Online resource:	https://doi.org/10.1007/978-3-662-57715-8
ISBN:	9783662577158

Time-space, spiking neural networks and brain-inspired artificial intelligence
Kasabov, Nikola K.

Time-space, spiking neural networks and brain-inspired artificial intelligence[electronic resource] /by Nikola K. Kasabov. - Berlin, Heidelberg :Springer Berlin Heidelberg :2019. - xxxiv, 738 p. :ill., digital ;24 cm. - Springer series on bio- and neurosystems,v.72520-8535 ;. - Springer series on bio- and neurosystems ;v.7..

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.

ISBN: 9783662577158

Standard No.: 10.1007/978-3-662-57715-8doiSubjects--Topical Terms:

572510
Object-oriented methods (Computer science)

LC Class. No.: QA76.9.O35 / K373 2019

Dewey Class. No.: 005.117

Time-space, spiking neural networks and brain-inspired artificial intelligence
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520 $a 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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