Space-Time Computing with Temporal Neural Networks (Record no. 85300)

000 -LEADER
fixed length control field 04356nam a22004935i 4500
001 - CONTROL NUMBER
control field 978-3-031-01754-4
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20240730164111.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 220601s2017 sz | s |||| 0|eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9783031017544
-- 978-3-031-01754-4
082 04 - CLASSIFICATION NUMBER
Call Number 621.3815
100 1# - AUTHOR NAME
Author Smith, James E.
245 10 - TITLE STATEMENT
Title Space-Time Computing with Temporal Neural Networks
250 ## - EDITION STATEMENT
Edition statement 1st ed. 2017.
300 ## - PHYSICAL DESCRIPTION
Number of Pages XXIV, 220 p.
490 1# - SERIES STATEMENT
Series statement Synthesis Lectures on Computer Architecture,
505 0# - FORMATTED CONTENTS NOTE
Remark 2 Preface -- 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 ## - SUMMARY, ETC.
Summary, etc Understanding 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.
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier https://doi.org/10.1007/978-3-031-01754-4
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type eBooks
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-- Cham :
-- Springer International Publishing :
-- Imprint: Springer,
-- 2017.
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-- computer
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-- rdamedia
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-- online resource
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-- text file
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650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Electronic circuits.
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-- Microprocessors.
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-- Computer architecture.
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-- Electronic Circuits and Systems.
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-- Processor Architectures.
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE
-- 1935-3243
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-- ZDB-2-SXSC

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