Information Dynamics

1. Front Matter

   Pages i-xvi

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2. Introduction

   • Gustavo Deco, Bernd Schürmann

   Pages 1-6

3. Dynamical Systems: An Overview

   • Gustavo Deco, Bernd Schürmann

   Pages 7-37

4. Statistical Structure Extraction in Dynamical Systems: Parametric Formulation

   • Gustavo Deco, Bernd Schürmann

   Pages 39-69

5. Applications: Parametric Characterization of Time Series

   • Gustavo Deco, Bernd Schürmann

   Pages 71-90

6. Statistical Structure Extraction in Dynamical Systems: Nonparametric Formulation

   • Gustavo Deco, Bernd Schürmann

   Pages 91-125

7. Applications: Nonparametric Characterization of Time Series

   • Gustavo Deco, Bernd Schürmann

   Pages 127-163

8. Statistical Structure Extraction in Dynamical Systems: Semiparametric Formulation

   • Gustavo Deco, Bernd Schürmann

   Pages 165-180

9. Applications: Semiparametric Characterization of Time Series

   • Gustavo Deco, Bernd Schürmann

   Pages 181-203

10. Information Processing and Coding in Spatiotemporal Dynamical Systems: Spiking Networks

    • Gustavo Deco, Bernd Schürmann

    Pages 205-225

11. Applications: Information Processing and Coding in Spatiotemporal Dynamical Systems

    • Gustavo Deco, Bernd Schürmann

    Pages 227-237

12. Epilogue

    • Gustavo Deco, Bernd Schürmann

    Pages 239-240

13. Back Matter

    Pages 239-281

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This book originated from a forefront R&D project pursued at Siemens Corporate Technology over the past several years. As a name for this project, we chose "Information Dynamics", which stands for information processing in complex dynamical systems. In the project, we wanted to grasp the flow of information in such systems in a quantitative manner, on the one hand by making use of an existing arsenal of methods and techniques from areas such as information theory, mathematical statistics, neural networks, nonlinear dynamics, probability theory, and statistical physics, and on the other hand by deriving new methods and techniques if required. The book contains only those contributions to the above-mentioned project which lend themselves to a unifying theoretical framework. Other important results obtained in the project, such as the extension of transport-theoretic techniques and their application to optimizing traffic flow, or the design of new neural network architectures for treating systems at the edge of chaos with applications in economics, are left out. This certainly is a sacrifice, but we think it is of benefit to the reader that we tried to be as focused and self­ contained as possible.

• Dynamics
• complex system
• complex systems
• information
• information theory
• modeling
• algorithm analysis and problem complexity

• Siemens Corporate Technology, Neural Computing, Munich, Germany

  Gustavo Deco, Bernd Schürmann

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