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New Directions in Statistical Signal Processing From Systems to Brains

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ISBN-10: 0262083485

ISBN-13: 9780262083485

Edition: 2007

Authors: John McWhirter, Terrence J. Sejnowski, Simon Haykin, Jose C. Principe

List price: $11.75
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Description:

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…    
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Book details

List price: $11.75
Copyright year: 2007
Publisher: MIT Press
Publication date: 10/13/2006
Binding: Hardcover
Pages: 544
Size: 8.25" wide x 10.25" long x 1.25" tall
Weight: 2.838
Language: English

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.

Jos� C. Pr�ncipe 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.

Series Foreword
Preface
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
References
Contributors
Index