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008			220601s2017 sz | s |||| 0|eng d
020			_a9783031017544
024	7		_a10.1007/978-3-031-01754-4 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.87 _b2017 EB
100	1		_aSmith, James E. _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687880 _d1950- _q(James Edward),
245	1	0	_aSpace-Time Computing with Temporal Neural Networks _cby James E. Smith
250			_a1st edition 2017
264		1	_aCham _bSpringer International Publishing _c2017
300			_a1 recurso en línea (XXIV, 220 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSynthesis Lectures on Computer Architecture _x1935-3243
505	0		_aPreface -- Acknowledgments -- Introduction -- Space-Time Computing -- Biological Overview -- Connecting TNNs with Biology -- Neuron Modeling -- Computing with Excitatory Neurons -- System Architecture -- Simulator Implementation -- Clustering the MNIST Dataset -- Summary and Conclusions -- References -- Author Biography.
520			_aUnderstanding and implementing the brain's computational paradigm is the one true grand challenge facing computer researchers. Not only are the brain's computational capabilities far beyond those of conventional computers, its energy efficiency is truly remarkable. This book, written from the perspective of a computer designer and targeted at computer researchers, is intended to give both background and lay out a course of action for studying the brain's computational paradigm. It contains a mix of concepts and ideas drawn from computational neuroscience, combined with those of the author. As background, relevant biological features are described in terms of their computational and communication properties. The brain's neocortex is constructed of massively interconnected neurons that compute and communicate via voltage spikes, and a strong argument can be made that precise spike timing is an essential element of the paradigm. Drawing from the biological features, a mathematics-based computational paradigm is constructed. The key feature is spiking neurons that perform communication and processing in space-time, with emphasis on time. In these paradigms, time is used as a freely available resource for both communication and computation. Neuron models are first discussed in general, and one is chosen for detailed development. Using the model, single-neuron computation is first explored. Neuron inputs are encoded as spike patterns, and the neuron is trained to identify input pattern similarities. Individual neurons are building blocks for constructing larger ensembles, referred to as "columns". These columns are trained in an unsupervised manner and operate collectively to perform the basic cognitive function of pattern clustering. Similar input patterns are mapped to a much smaller set of similar output patterns, thereby dividing the input patterns into identifiable clusters. Larger cognitive systems are formed by combining columns into a hierarchical architecture. These higher level architectures are the subject of ongoing study, and progress to date is described in detail in later chapters. Simulation plays a major role in model development, and the simulation infrastructure developed by the author is described.
988			_aSynthesis Collection of Technology_2017
650		7	_2embne _9678664 _aRedes neuronales artificiales
650		7	_2embne _9481390 _aNeurociencia computacional
650		7	_2embne _9138966 _aBases de datos
776	0	8	_iPrinted edition: _z9783031006265
776	0	8	_iPrinted edition: _z9783031028823
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-01754-4 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b03/2023 _dz _esc _zSI