New Directions in Statistical Signal Processing

From Systems to Brains

Simon Haykin, José C. Principe, Terrence J. Sejnowski, John McWhirter

540 pages, parution le 04/12/2006

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Résumé

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).

L'auteur - Simon Haykin

is University Professor and Director of the Adaptive Systems Laboratory at McMaster University (Ontario, Canada)

L'auteur - José C. Principe

José C. Príncipe is Distinguished Professor of Electrical and Biomedical Engineering at the University of Florida, Gainesville, where he is BellSouth Professor and Founder and Director of the Computational NeuroEngineering Laboratory.

L'auteur - Terrence J. Sejnowski

Terrence J. Sejnowski is Francis Crick Professor, Director of the Computational Neurobiology Laboratory, and a Howard Hughes Medical Institute Investigator at the Salk Institute for Biological Studies and Professor of Biology at the University of California, San Diego.

L'auteur - John McWhirter

John McWhirter is Senior Fellow at QinetiQ Ltd., Malvern, Associate Professor at the Cardiff School of Engineering, and Honorary Visiting Professor at Queen's University, Belfast.

Sommaire

Modeling the Mind: From Circuits to Systems
Empirical Statistics and Stochastic Models for Visual Signals
The Machine Cocktail Party Problem
Sensor Adaptive Signal Processing of Biological Nanotubes (Ion Channels) at Macroscopic and Nano Scales
Spin Diffusion: A New Perspective in Magnetic Resonance Imaging
What Makes a Dynamical System Computationally Powerful?
A Variational Principle for Graphical Models
Modeling Large Dynamical Systems with Dynamical Consistent Neural Networks
Diversity in Communication: From Source Coding to Wireless Networks
Designing Patterns for Easy Recognition: Information Transmission with Low-Density Parity-Check Codes
Turbo Processing
Blind Signal Processing Based on Data Geometric Properties
Game-Theoretic Learning
Learning Observable Operator Models via the Efficient Sharpening Algorithm

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Caractéristiques techniques

	PAPIER
Éditeur(s)	The MIT Press
Auteur(s)	Simon Haykin, José C. Principe, Terrence J. Sejnowski, John McWhirter
Parution	04/12/2006
Nb. de pages	540
Format	21,5 x 26,5
Couverture	Relié
Poids	1280g
Intérieur	Noir et Blanc
EAN13	9780262083485
ISBN13	978-0-262-08348-5