000 04356nam a22004935i 4500
001 978-3-031-01754-4
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008 220601s2017 sz | s |||| 0|eng d
020 _a9783031017544
_9978-3-031-01754-4
024 7 _a10.1007/978-3-031-01754-4
_2doi
050 4 _aTK7867-7867.5
072 7 _aTJFC
_2bicssc
072 7 _aTEC008010
_2bisacsh
072 7 _aTJFC
_2thema
082 0 4 _a621.3815
_223
100 1 _aSmith, James E.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_982116
245 1 0 _aSpace-Time Computing with Temporal Neural Networks
_h[electronic resource] /
_cby James E. Smith.
250 _a1st ed. 2017.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2017.
300 _aXXIV, 220 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _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.
650 0 _aElectronic circuits.
_919581
650 0 _aMicroprocessors.
_982117
650 0 _aComputer architecture.
_93513
650 1 4 _aElectronic Circuits and Systems.
_982118
650 2 4 _aProcessor Architectures.
_982119
710 2 _aSpringerLink (Online service)
_982120
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031006265
776 0 8 _iPrinted edition:
_z9783031028823
830 0 _aSynthesis Lectures on Computer Architecture,
_x1935-3243
_982121
856 4 0 _uhttps://doi.org/10.1007/978-3-031-01754-4
912 _aZDB-2-SXSC
942 _cEBK
999 _c85300
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