New Directions in Statistical Signal Processing: From Systems to Brains

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Leading researchers in signal processing and neural computation present work aimed at promoting the interaction and cross-fertilization between the two fields.

Author(s): Simon Haykin, José C. Príncipe, Terrence J. Sejnowski, John McWhirter
Series: Neural Information Processing
Publisher: The MIT Press
Year: 2006

Language: English
Pages: 525

Contents......Page 6
Series Foreword......Page 8
Preface......Page 10
1 Modeling the Mind: From Circuits to Systems......Page 12
2 Empirical Statistics and Stochastic Models for Visual Signals......Page 34
3 The Machine Cocktail Party Problem......Page 62
4 Sensor Adaptive Signal Processing of Biological Nanotubes (Ion Channels) at Macroscopic and Nano Scales......Page 88
5 Spin Di.usion: A New Perspective in Magnetic Resonance Imaging......Page 130
6 What Makes a Dynamical System Computationally Powerful?......Page 138
7 A Variational Principle for Graphical Models......Page 166
8 Modeling Large Dynamical Systems with Dynamical Consistent Neural Networks......Page 214
9 Diversity in Communication: From Source Coding to Wireless Networks......Page 254
10 Designing Patterns for Easy Recognition: Information Transmission with Low-Density Parity-Check Codes......Page 298
11 Turbo Processing......Page 318
12 Blind Signal Processing Based on Data Geometric Properties......Page 348
13 Game-Theoretic Learning......Page 390
14 Learning Observable Operator Models via the Efficient Sharpening Algorithm......Page 428
References......Page 476
Contributors......Page 520
Index......Page 524