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E-book

Title New directions in statistical signal processing : from systems to brain / edited by Simon Haykin [and others]
Published Cambridge, Mass. : MIT Press, ©2007

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Description 1 online resource (vi, 514 pages) : illustrations
Series Neural information processing series
Neural information processing series.
Contents Modeling the mind : from circuits to systems / 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 -- 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 [and others] -- Diversity in communication : from source coding to wireless networks / Suhas N. Diggavi -- 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 [and others]
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)
Analysis COMPUTER SCIENCE/Machine Learning & Neural Networks
NEUROSCIENCE/General
Bibliography Includes bibliographical references (pages 465-508) and index
Notes Master and use copy. Digital master created according to Benchmark for Faithful Digital Reproductions of Monographs and Serials, Version 1. Digital Library Federation, December 2002. http://purl.oclc.org/DLF/benchrepro0212 MiAaHDL
English
Print version record
digitized 2010 HathiTrust Digital Library committed to preserve pda MiAaHDL
Subject Neural networks (Neurobiology)
Neural networks (Computer science)
Signal processing -- Statistical methods
Neural computers.
Neural circuitry.
Statistics.
Neural Networks, Computer
Algorithms
Nerve Net
Statistics as Topic
algorithms.
statistics.
MEDICAL -- Neuroscience.
PSYCHOLOGY -- Neuropsychology.
Statistics
Neural circuitry
Neural computers
Neural networks (Computer science)
Neural networks (Neurobiology)
Signal processing -- Statistical methods
Form Electronic book
Author Haykin, Simon S., 1931-
ISBN 9780262256315
0262256312
1429418737
9781429418737
0262292793
9780262292795
9786612096372
6612096373
1282096370
9781282096370