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)
Resource Information
The item New directions in statistical signal processing : from systems to brain, edited by Simon Haykin ... [et al.], (electronic book) represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of Liverpool.This item is available to borrow from 1 library branch.
Resource Information
The item New directions in statistical signal processing : from systems to brain, edited by Simon Haykin ... [et al.], (electronic book) represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of Liverpool.
This item is available to borrow from 1 library branch.
 Summary
 Signal processing and neural computation have separately and significantly influenced many disciplines, but the crossfertilization 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, gametheoretic learning algorithms, and observable operator models (OOMs) as an alternative to hidden Markov models (HMMs)
 Language
 eng
 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
 HansGeorg 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 lowdensity paritycheck codes
 Frank R. Kschischang, Masoud Ardakani
 Turbo processing
 Claude Berrou, Charlotte Langlais, Fabrice Seguin
 Blind signal processing based on data geometric properties
 Konstantinos Diamantaras
 Gametheoretic 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
 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.]
 Language
 eng
 Summary
 Signal processing and neural computation have separately and significantly influenced many disciplines, but the crossfertilization 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, gametheoretic learning algorithms, and observable operator models (OOMs) as an alternative to hidden Markov models (HMMs)
 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)
 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
 HansGeorg 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 lowdensity paritycheck codes
 Frank R. Kschischang, Masoud Ardakani
 Turbo processing
 Claude Berrou, Charlotte Langlais, Fabrice Seguin
 Blind signal processing based on data geometric properties
 Konstantinos Diamantaras
 Gametheoretic 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)
 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
 HansGeorg 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 lowdensity paritycheck codes
 Frank R. Kschischang, Masoud Ardakani
 Turbo processing
 Claude Berrou, Charlotte Langlais, Fabrice Seguin
 Blind signal processing based on data geometric properties
 Konstantinos Diamantaras
 Gametheoretic 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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<div class="citation" vocab="http://schema.org/"><i class="fa faexternallinksquare fafw"></i> Data from <span resource="http://link.liverpool.ac.uk/portal/Newdirectionsinstatisticalsignalprocessing/WPEpYMs7jJI/" typeof="Book http://bibfra.me/vocab/lite/Item"><span property="name http://bibfra.me/vocab/lite/label"><a href="http://link.liverpool.ac.uk/portal/Newdirectionsinstatisticalsignalprocessing/WPEpYMs7jJI/">New directions in statistical signal processing : from systems to brain, edited by Simon Haykin ... [et al.], (electronic book)</a></span>  <span property="potentialAction" typeOf="OrganizeAction"><span property="agent" typeof="LibrarySystem http://library.link/vocab/LibrarySystem" resource="http://link.liverpool.ac.uk/"><span property="name http://bibfra.me/vocab/lite/label"><a property="url" href="http://link.liverpool.ac.uk/">University of Liverpool</a></span></span></span></span></div>