| 000 -LEADER |
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| fixed length control field |
10117nam a2200745 i 4500 |
| 001 - CONTROL NUMBER |
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| control field |
7933302 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
IEEE |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20200413152924.0 |
| 006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS |
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| fixed length control field |
m eo d |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION |
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| fixed length control field |
cr cn |||m|||a |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
170626s2017 caua foab 000 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
9781627058902 |
| Qualifying information |
ebook |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| Canceled/invalid ISBN |
9781627059480 |
| Qualifying information |
print |
| 024 7# - OTHER STANDARD IDENTIFIER |
|---|
| Standard number or code |
10.2200/S00771ED1V01Y201704CAC039 |
| Source of number or code |
doi |
| 035 ## - SYSTEM CONTROL NUMBER |
|---|
| System control number |
(CaBNVSL)swl00407520 |
| 035 ## - SYSTEM CONTROL NUMBER |
|---|
| System control number |
(OCoLC)991869997 |
| 040 ## - CATALOGING SOURCE |
|---|
| Original cataloging agency |
CaBNVSL |
| Language of cataloging |
eng |
| Description conventions |
rda |
| Transcribing agency |
CaBNVSL |
| Modifying agency |
CaBNVSL |
| 050 #4 - LIBRARY OF CONGRESS CALL NUMBER |
|---|
| Classification number |
QA76.87 |
| Item number |
.S657 2017 |
| 082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
006.32 |
| Edition number |
23 |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Smith, James E. |
| Fuller form of name |
(James Edward), |
| Dates associated with a name |
1950-, |
| Relator term |
author. |
| 245 10 - TITLE STATEMENT |
|---|
| Title |
Space-time computing with temporal neural networks / |
| Statement of responsibility, etc. |
James E. Smith. |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Place of production, publication, distribution, manufacture |
[San Rafael, California] : |
| Name of producer, publisher, distributor, manufacturer |
Morgan & Claypool, |
| Date of production, publication, distribution, manufacture, or copyright notice |
2017. |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
1 PDF (xxv, 215 pages) : |
| Other physical details |
illustrations. |
| 336 ## - CONTENT TYPE |
|---|
| Content type term |
text |
| Source |
rdacontent |
| 337 ## - MEDIA TYPE |
|---|
| Media type term |
electronic |
| Source |
isbdmedia |
| 338 ## - CARRIER TYPE |
|---|
| Carrier type term |
online resource |
| Source |
rdacarrier |
| 490 1# - SERIES STATEMENT |
|---|
| Series statement |
Synthesis lectures on computer architecture, |
| International Standard Serial Number |
1935-3243 ; |
| Volume/sequential designation |
# 39 |
| 538 ## - SYSTEM DETAILS NOTE |
|---|
| System details note |
Mode of access: World Wide Web. |
| 538 ## - SYSTEM DETAILS NOTE |
|---|
| System details note |
System requirements: Adobe Acrobat Reader. |
| 500 ## - GENERAL NOTE |
|---|
| General note |
Part of: Synthesis digital library of engineering and computer science. |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc. note |
Includes bibliographical references (pages 205-214). |
| 505 0# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
Part I. Introduction to space-time computing and temporal neural networks -- 1. Introduction -- 1.1 Basics of neuron operation -- 1.2 Space-time communication and computation -- 1.2.1 Communication -- 1.2.2 Computation -- 1.2.3 Discussion -- 1.3 Background: neural network models -- 1.3.1 Rate coding -- 1.3.2 Temporal coding -- 1.3.3 Rate processing -- 1.3.4 Spike processing -- 1.3.5 Summary and taxonomy -- 1.4 Background: machine learning -- 1.5 Approach: interaction of computer engineering and neuroscience -- 1.6 Bottom-up analysis: a guiding analogy -- 1.7 Overview -- |
| 505 8# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
2. Space-time computing -- 2.1 Definition of terms -- 2.2 Feedforward computing networks -- 2.3 General TNN model -- 2.4 Space-time computing systems -- 2.5 Implications of invariance -- 2.6 TNN system architecture -- 2.6.1 Training -- 2.6.2 Computation (evaluation) -- 2.6.3 Encoding -- 2.6.4 Decoding -- 2.7 Summary: meta-architecture -- 2.7.1 Simulation -- 2.7.2 Implied functions -- 2.8 Special case: feedforward McCulloch-Pitts networks -- 2.9 Race logic -- |
| 505 8# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
3. Biological overview -- 3.1 Overall brain structure (very brief ) -- 3.2 Neurons -- 3.2.1 Synapses -- 3.2.2 Synaptic plasticity -- 3.2.3 Frequency-current relationship -- 3.2.4 Inhibition -- 3.3 Hierarchy and columnar organization -- 3.3.1 Neurons -- 3.3.2 Columns (micro-columns) -- 3.3.3 Macro-columns -- 3.3.4 Regions -- 3.3.5 Lobes -- 3.3.6 Uniformity -- 3.4 Inter-neuron connections -- 3.4.1 Path distances -- 3.4.2 Propagation velocities -- 3.4.3 Transmission delays -- 3.4.4 Numbers of connections -- 3.4.5 Attenuation of excitatory responses -- 3.4.6 Connections summary -- 3.5 Sensory processing -- 3.5.1 Receptive fields -- 3.5.2 Saccades and whisks -- 3.5.3 Vision pathway -- 3.5.4 Waves of spikes -- 3.5.5 Feedforward processing path -- 3.5.6 Precision -- 3.5.7 Information content -- 3.5.8 Neural processing -- 3.6 Oscillations -- 3.6.1 Theta oscillations -- 3.6.2 Gamma oscillations -- |
| 505 8# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
Part II. Modeling temporal neural networks -- 4. Connecting TNNs with biology -- 4.1 Communication via voltage spikes -- 4.2 Columns and spike bundles -- 4.3 Spike synchronization -- 4.3.1 Aperiodic synchronization: saccades, whisks, and sniffs -- 4.3.2 Periodic synchronization -- 4.4 First spikes carry information -- 4.5 Feedforward processing -- 4.6 Simplifications summary -- 4.7 Plasticity and training -- 4.8 Fault tolerance and temporal stability -- 4.8.1 Interwoven fault tolerance -- 4.8.2 Temporal stability -- 4.8.3 Noise (or lack thereof ) -- 4.9 Discussion: reconciling biological complexity with model simplicity -- 4.10 Prototype architecture overview -- |
| 505 8# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
5. Neuron modeling -- 5.1 Basic models -- 5.1.1 Hodgkin Huxley neuron model -- 5.1.2 Derivation of the leaky integrate and fire (LIF) model -- 5.1.3 Spike response model (SRM0) -- 5.2 Modeling synaptic connections -- 5.3 Excitatory neuron implementation -- 5.4 The menagerie of LIF neurons -- 5.4.1 Synaptic conductance model -- 5.4.2 Biexponential SRM0 model -- 5.4.3 Single stage SRM0 -- 5.4.4 Linear leak integrate and fire (LLIF) -- 5.5 Other neuron models -- 5.5.1 Alpha function -- 5.5.2 Quadratic integrate-and-fire -- 5.6 Synaptic plasticity and training -- |
| 505 8# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
6. Computing with excitatory neurons -- 6.1 Single neuron clustering -- 6.1.1 Definitions -- 6.1.2 Excitatory neuron function, approximate description -- 6.1.3 Looking ahead -- 6.2 Spike coding -- 6.2.1 Volleys -- 6.2.2 Nonlinear mappings -- 6.2.3 Distance functions -- 6.3 Prior work: radial basis function (RBF) neurons -- 6.4 Excitatory neuron I: training mode -- 6.4.1 Modeling excitatory response functions -- 6.4.2 Training set -- 6.4.3 STDP update rule -- 6.4.4 Weight stabilization -- 6.5 Excitatory neuron I: compound response functions -- 6.6 Excitatory neuron model II -- 6.6.1 Neuron model derivation -- 6.6.2 Training mode -- 6.6.3 Evaluation mode -- 6.7 Attenuation of excitatory responses -- 6.8 Threshold detection -- 6.9 Excitatory neuron model II summary -- |
| 505 8# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
7. System architecture -- 7.1 Overview -- 7.2 Interconnection structure -- 7.3 Input encoding -- 7.4 Excitatory column operation -- 7.4.1 Evaluation -- 7.4.2 Training -- 7.4.3 Unsupervised synaptic weight training -- 7.4.4 Supervised weight training -- 7.5 Inhibition -- 7.5.1 Feedback inhibition -- 7.5.2 Lateral inhibition -- 7.5.3 Feedforward inhibition -- 7.6 Volley decoding and analysis -- 7.6.1 Temporal flattening -- 7.6.2 Decoding to estimate clustering quality -- 7.6.3 Decoding for classification -- 7.7 Training inhibition -- 7.7.1 FFI: establishing tF and kF -- 7.7.2 LI: establishing tL and kL -- 7.7.3 Excitatory neuron training in the presence of inhibition -- Part III: extended design study: clustering the MNIST dataset -- |
| 505 8# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
8. Simulator implementation -- 8.1 Simulator overview -- 8.2 Inter-unit communication -- 8.3 Simulating time -- 8.4 Synaptic weight training -- 8.5 Evaluation -- 8.5.1 EC block -- 8.5.2 IC block -- 8.5.3 VA block -- 8.6 Design methodology -- |
| 505 8# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
9. Clustering the MNIST dataset -- 9.1 MNIST workload -- 9.2 Prototype clustering architecture -- 9.3 OnOff encoding -- 9.4 Intra-CC network -- 9.5 Excitatory column (EC) -- 9.6 Lateral inhibition -- 9.7 144 RFs -- 9.8 Feedforward inhibition -- 9.9 Layer 1 result summary -- 9.10 Related work -- 9.11 Considering layer 2 -- |
| 505 8# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
10. Summary and conclusions -- References -- Author biography. |
| 506 ## - RESTRICTIONS ON ACCESS NOTE |
|---|
| Terms governing access |
Abstract freely available; full-text restricted to subscribers or individual document purchasers. |
| 510 0# - CITATION/REFERENCES NOTE |
|---|
| Name of source |
Compendex |
| 510 0# - CITATION/REFERENCES NOTE |
|---|
| Name of source |
INSPEC |
| 510 0# - CITATION/REFERENCES NOTE |
|---|
| Name of source |
Google scholar |
| 510 0# - CITATION/REFERENCES NOTE |
|---|
| Name of source |
Google book search |
| 520 3# - 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. |
| 530 ## - ADDITIONAL PHYSICAL FORM AVAILABLE NOTE |
|---|
| Additional physical form available note |
Also available in print. |
| 588 ## - SOURCE OF DESCRIPTION NOTE |
|---|
| Source of description note |
Title from PDF title page (viewed on June 26, 2017). |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Neural networks (Computer science) |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Computational neuroscience. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Space and time |
| General subdivision |
Data processing. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Temporal databases. |
| 653 ## - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
spiking neural networks |
| 653 ## - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
temporal models |
| 653 ## - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
unsupervised learning |
| 653 ## - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
classification |
| 653 ## - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
neuron models |
| 653 ## - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
computing theory |
| 655 #0 - INDEX TERM--GENRE/FORM |
|---|
| Genre/form data or focus term |
Electronic books. |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY |
|---|
| Relationship information |
Print version: |
| International Standard Book Number |
9781627059480 |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE |
|---|
| Uniform title |
Synthesis digital library of engineering and computer science. |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE |
|---|
| Uniform title |
Synthesis lectures in computer architecture ; |
| Volume/sequential designation |
# 39. |
| International Standard Serial Number |
1935-3243 |
| 856 42 - ELECTRONIC LOCATION AND ACCESS |
|---|
| Materials specified |
Abstract with links to resource |
| Uniform Resource Identifier |
http://ieeexplore.ieee.org/servlet/opac?bknumber=7933302 |
