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The Resource New directions in statistical signal processing : from systems to brain, edited by Simon Haykin ... [et al.], (electronic book)

New directions in statistical signal processing : from systems to brain, edited by Simon Haykin ... [et al.], (electronic book)

Contributor
Summary
Signal processing and neural computation have separately and significantly influenced many disciplines, but the cross-fertilization of the two fields has begun only recently. Research now shows that each has much to teach the other, as we see highly sophisticated kinds of signal processing and elaborate hierachical levels of neural computation performed side by side in the brain. In New Directions in Statistical Signal Processing, leading researchers from both signal processing and neural computation present new work that aims to promote interaction between the two disciplines. The book's 14 chapters, almost evenly divided between signal processing and neural computation, begin with the brain and move on to communication, signal processing, and learning systems. They examine such topics as how computational models help us understand the brain's information processing, how an intelligent machine could solve the "cocktail party problem" with "active audition" in a noisy environment, graphical and network structure modeling approaches, uncertainty in network communications, the geometric approach to blind signal processing, game-theoretic learning algorithms, and observable operator models (OOMs) as an alternative to hidden Markov models (HMMs)
Language
eng
Work
Publication
Extent
1 online resource (vi, 514 p.)
Contents
  • Spin diffusion : a new perspective in magnetic resonance imaging
  • Timothy R. Field
  • What makes a dynamical system computationally powerful?
  • Robert Legenstein, Wolfgang Maass
  • A variational principle for graphical models
  • Martin J. Wainwright, Michael I. Jordan
  • Modeling large dynamical systems with dynamical consistent neural networks
  • Hans-Georg Zimmermann ... [et al.]
  • Diversity in communication : from source coding to wireless networks
  • Suhas N. Diggavi
  • Modeling the mind : from circuits to systems
  • Designing patterns for easy recognition : information transmission with low-density parity-check codes
  • Frank R. Kschischang, Masoud Ardakani
  • Turbo processing
  • Claude Berrou, Charlotte Langlais, Fabrice Seguin
  • Blind signal processing based on data geometric properties
  • Konstantinos Diamantaras
  • Game-theoretic learning
  • Geoffrey J. Gordon
  • Learning observable operator models via the efficient sharpening algorithm
  • Herbert Jaeger ... [et al.]
  • Suzanna Becker
  • Empirical statistics and stochastic models for visual signals
  • David Mumford
  • The machine cocktail party problem
  • Simon Haykin, Zhe Chen
  • Sensor adaptive signal processing of biological nanotubes (ion channels) at macroscopic and nano scales
  • Vikram Krishnamurthy
Isbn
9780262256315
Instance
Label
New directions in statistical signal processing : from systems to brain
Title
New directions in statistical signal processing
Title remainder
from systems to brain
Statement of responsibility
edited by Simon Haykin ... [et al.]
Contributor
Subject
Language
eng
Summary
Signal processing and neural computation have separately and significantly influenced many disciplines, but the cross-fertilization of the two fields has begun only recently. Research now shows that each has much to teach the other, as we see highly sophisticated kinds of signal processing and elaborate hierachical levels of neural computation performed side by side in the brain. In New Directions in Statistical Signal Processing, leading researchers from both signal processing and neural computation present new work that aims to promote interaction between the two disciplines. The book's 14 chapters, almost evenly divided between signal processing and neural computation, begin with the brain and move on to communication, signal processing, and learning systems. They examine such topics as how computational models help us understand the brain's information processing, how an intelligent machine could solve the "cocktail party problem" with "active audition" in a noisy environment, graphical and network structure modeling approaches, uncertainty in network communications, the geometric approach to blind signal processing, game-theoretic learning algorithms, and observable operator models (OOMs) as an alternative to hidden Markov models (HMMs)
Member of
Cataloging source
N$T
Dewey number
612.8/2
Illustrations
illustrations
Index
index present
LC call number
QP363.3
LC item number
.N52 2007eb
Literary form
non fiction
Nature of contents
  • dictionaries
  • bibliography
http://library.link/vocab/relatedWorkOrContributorDate
1931-
http://library.link/vocab/relatedWorkOrContributorName
Haykin, Simon S.
Series statement
Neural information processing series
http://library.link/vocab/subjectName
  • Neural networks (Neurobiology)
  • Neural networks (Computer science)
  • Signal processing
  • Neural computers
  • Neural Networks (Computer)
  • Algorithms
  • Nerve Net
  • Statistics as Topic
  • Electronic books
Label
New directions in statistical signal processing : from systems to brain, edited by Simon Haykin ... [et al.], (electronic book)
Instantiates
Publication
Antecedent source
unknown
Bibliography note
Includes bibliographical references (p. [465]-508) and index
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
  • Spin diffusion : a new perspective in magnetic resonance imaging
  • Timothy R. Field
  • What makes a dynamical system computationally powerful?
  • Robert Legenstein, Wolfgang Maass
  • A variational principle for graphical models
  • Martin J. Wainwright, Michael I. Jordan
  • Modeling large dynamical systems with dynamical consistent neural networks
  • Hans-Georg Zimmermann ... [et al.]
  • Diversity in communication : from source coding to wireless networks
  • Suhas N. Diggavi
  • Modeling the mind : from circuits to systems
  • Designing patterns for easy recognition : information transmission with low-density parity-check codes
  • Frank R. Kschischang, Masoud Ardakani
  • Turbo processing
  • Claude Berrou, Charlotte Langlais, Fabrice Seguin
  • Blind signal processing based on data geometric properties
  • Konstantinos Diamantaras
  • Game-theoretic learning
  • Geoffrey J. Gordon
  • Learning observable operator models via the efficient sharpening algorithm
  • Herbert Jaeger ... [et al.]
  • Suzanna Becker
  • Empirical statistics and stochastic models for visual signals
  • David Mumford
  • The machine cocktail party problem
  • Simon Haykin, Zhe Chen
  • Sensor adaptive signal processing of biological nanotubes (ion channels) at macroscopic and nano scales
  • Vikram Krishnamurthy
Control code
IEEEMIT77521428
Dimensions
unknown
Extent
1 online resource (vi, 514 p.)
File format
unknown
Form of item
online
Isbn
9780262256315
Level of compression
unknown
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Other physical details
ill.
Quality assurance targets
not applicable
Reformatting quality
unknown
Reproduction note
Electronic resource.
Sound
unknown sound
Specific material designation
remote
System control number
ocm77521428
Label
New directions in statistical signal processing : from systems to brain, edited by Simon Haykin ... [et al.], (electronic book)
Publication
Antecedent source
unknown
Bibliography note
Includes bibliographical references (p. [465]-508) and index
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
  • Spin diffusion : a new perspective in magnetic resonance imaging
  • Timothy R. Field
  • What makes a dynamical system computationally powerful?
  • Robert Legenstein, Wolfgang Maass
  • A variational principle for graphical models
  • Martin J. Wainwright, Michael I. Jordan
  • Modeling large dynamical systems with dynamical consistent neural networks
  • Hans-Georg Zimmermann ... [et al.]
  • Diversity in communication : from source coding to wireless networks
  • Suhas N. Diggavi
  • Modeling the mind : from circuits to systems
  • Designing patterns for easy recognition : information transmission with low-density parity-check codes
  • Frank R. Kschischang, Masoud Ardakani
  • Turbo processing
  • Claude Berrou, Charlotte Langlais, Fabrice Seguin
  • Blind signal processing based on data geometric properties
  • Konstantinos Diamantaras
  • Game-theoretic learning
  • Geoffrey J. Gordon
  • Learning observable operator models via the efficient sharpening algorithm
  • Herbert Jaeger ... [et al.]
  • Suzanna Becker
  • Empirical statistics and stochastic models for visual signals
  • David Mumford
  • The machine cocktail party problem
  • Simon Haykin, Zhe Chen
  • Sensor adaptive signal processing of biological nanotubes (ion channels) at macroscopic and nano scales
  • Vikram Krishnamurthy
Control code
IEEEMIT77521428
Dimensions
unknown
Extent
1 online resource (vi, 514 p.)
File format
unknown
Form of item
online
Isbn
9780262256315
Level of compression
unknown
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Other physical details
ill.
Quality assurance targets
not applicable
Reformatting quality
unknown
Reproduction note
Electronic resource.
Sound
unknown sound
Specific material designation
remote
System control number
ocm77521428

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