New Directions in Statistical Signal Processing: From Systems to Brains

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

  • 出版商: MIT
  • 出版日期: 2006-10-13
  • 售價: $1,390
  • 語言: 英文
  • 頁數: 544
  • 裝訂: Hardcover
  • ISBN: 0262083485
  • ISBN-13: 9780262083485
  • 立即出貨(限量) (庫存=3)

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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 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).

Simon Haykin is University Professor and Director of the Adaptive Systems Laboratory at McMaster University.

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.

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.

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.
 
 

Table of Contents

Series Foreword vii
 
1. Modeling the Mind: From Circuits to Systems
Suzanna Becker 1
 
2. Empirical Statistics and Stochastic Models for Visual Signals
David Mumford 23
 
3. The Machine Cocktail Party Problem
Simon Haykin and Zhe Chen 51
 
4. Sensor Adaptive Signal Processing of Biological Nanotubes (Ion Channels) at Macroscopic and Nano Scales
Vikram Krishnamurthy 77
 
5. Spin Diffusion: A New Perspective in Magnetic Resonance Imaging
Timothy R. Field 119
 
6. What Makes a Dynamical System Computationally Powerful?
Robert Legenstein and Wolfgang Maass 127
 
7. A Variational Principle for Graphical Models
Martin J. Wainwright and Michael I. Jordan 155
 
8. Modeling Large Dynamical Systems with Dynamical Consistent Neural Networks
Hans-Georg Zimmermann, Ralph Grothmann, Anton Maximilian Schäfer and Christoph Tietz 203
 
9. Diversity in Communication: From Source Coding to Wireless Networks
Suhas N. Diggavi 243
 
10. Designing Patterns for Easy Recognition: Information Transmission with Low-Density Parity-Check Codes
Frank R. Kschischang and Masoud Ardakani 287
 
11. Turbo Processing
Claude Berrou, Charlotte Langlais and Fabrice Seguin 307
 
12. Blind Signal Processing Based on Data Geometric Properties
Konstantinos Diamantaras 379
 
13. Game-Theoretic Learning
Geoffrey J. Gordon 379
 
14. Learning Observable Operator Models via the Efficient Sharpening Algorithm
Herbert Jaeger, Mingjie Zhao, Klaus Kretzschmar, Tobias Oberstein, Dan Popovici and Andreas Kolling 417
 
 References 465
 
 Contributors 509
 
 Index 513
 

商品描述(中文翻譯)

描述

信號處理和神經計算分別對許多學科產生了重大影響,但兩個領域的交叉融合直到最近才開始。研究現在表明,它們彼此有很多可以互相學習的地方,因為我們在大腦中同時看到高度複雜的信號處理和精細的神經計算層次。在《統計信號處理的新方向》中,來自信號處理和神經計算領域的領先研究人員提出了旨在促進兩個學科之間互動的新工作。

該書的14章幾乎平均分為信號處理和神經計算兩部分,從大腦開始,然後涉及通信、信號處理和學習系統。它們探討了計算模型如何幫助我們理解大腦的信息處理、智能機器如何在嘈雜的環境中通過“主動聽力”解決“雞尾酒會問題”、圖形和網絡結構建模方法、網絡通信中的不確定性、盲信號處理的幾何方法、博弈論學習算法以及可觀察算子模型(OOM)作為隱馬爾可夫模型(HMM)的替代方法。

Simon Haykin是麥克馬斯特大學的大學教授和自適應系統實驗室主任。

José C. Príncipe是佛羅里達大學的電氣和生物醫學工程杰出教授,他是貝爾南方教授、計算神經工程實驗室的創始人和主任。

Terrence J. Sejnowski是索爾克生物研究所的弗朗西斯·克里克教授、計算神經生物學實驗室主任,以及加州大學聖地亞哥分校的生物學教授。

John McWhirter是QinetiQ有限公司的高級研究員,卡迪夫工程學院的副教授,以及貝爾法斯特女王大學的名譽客座教授。

目錄

系列前言 vii

1. 從電路到系統的心智建模
Suzanna Becker 1

2. 視覺信號的實證統計和隨機模型
David Mumford 23

3. 機器雞尾酒會問題
Simon Haykin和Zhe Chen 51

4. 生物納米管(離子通道)的傳感器自適應信號處理
Vikram Krishnamurthy 77

5. 自旋擴散:磁共振成像的新視角
Timothy R. Field 119

6. 什麼使動態系統具有計算能力?
Robert Legenstein和Wolfgang Maass 127

7. 圖形模型的變分原理
Martin J. Wainwright和Michael I. Jordan 155

8. 使用動態一致的神經網絡建模大型動態系統
Hans-Georg Zimmermann、Ralph Grothmann、Anton Maximilian Schäfer和Christoph Tietz 203

9. 通信中的多樣性:從源編碼到無線網絡
Suhas N. Diggavi 243

10. 設計易於識別的模式:低密度奇偶校驗碼的信息傳輸
Frank R. Kschischang和Masoud