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710	2		_aSpringerLink (Online service) _9106996
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008			180829s2019 gw a o |||| 0|eng d
020			_a9783662577158
024	7		_a10.1007/978-3-662-57715-8 _2doi
040			_bspa _dES-MaUEC _cES-MaUEC
050		4	_aQH324.2 _b2019 EB
100	1		_aKasabov, Nikola _eautor _996822
245	1	0	_aTime-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence _cby Nikola K. Kasabov.
264		1	_aBerlin, Heidelberg _bImprint: Springer _c2019
300			_a1 recurso en línea (XXXIV, 738 páginas) _b340 ilustraciones., 256 ilustraciones en color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF _2rda
490	0		_aSpringer Series on Bio- and Neurosystems _x2520-8535 _v7
490	0		_aIntelligent Technologies and Robotics (Springer-42732)
505	0		_aPart 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.
520	3		_aSpiking 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.
988			_aPrimersemestre_2019_Robotics
650		7	_2embne _aBioinformática _9160489
650		7	_2embne _aNeurociencias _9158907
776	0	8	_iPrinted edition: _z9783662577134
776	0	8	_iPrinted edition: _z9783662577141
776	0	8	_iPrinted edition: _z9783662586075
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-662-57715-8 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_dz _feng _ggw _h0 _b10/2019 _ek _zSI