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Information Dynamics Foundations and Applications

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 t...

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Bibliographic Details
Main Authors: Deco, Gustavo, Schürmann, Bernd (Author)
Format: eBook
Language:English
Published: New York, NY Springer New York 2001, 2001
Edition:1st ed. 2001
Subjects:
Coding And Information Theory
Coding Theory
Algorithms
Information Theory
Online Access:
Collection: Springer Book Archives -2004 - Collection details see MPG.ReNa
Table of Contents:
  • Appendix D Generalized Discriminability by the Spike Response Model ofa Single Spiking Neuron: Analytical Results
  • References
  • 5.1 Nonparametric Detection ofStatistical Dependencies in Time Series
  • 5.2 Nonparametric Characterization of Dynamics: The Information Flow Concept
  • 5.3 Information Flow and Coarse Graining
  • 6 Applications: Nonparametric Characterization of Time Series
  • 6.1 Detecting Nonlinear Correlations in Time Series
  • 6.2 Nonparametric Analysis of Time Series : Optimal Delay Selection
  • 6.3 Determining the Information Flow ofDynamical Systems from Continuous Probability Distributions
  • 6.4 Dynamical Characterization ofTime Signals: The Integrated Information Flow
  • 6.5 Information Flow and Coarse Graining: Numerical Experiments
  • 7 Statistical Structure Extraction in Dynamical Systems: Semiparametric Formulation
  • 7.1 Markovian Characterization of Univariate Time Series
  • 7.2 Markovian Characterization of Multivariate Time Series
  • 8 Applications: Semiparametric Characterization of Time Series
  • 8.1 Univariate Time Series : Artificial Data
  • 8.2 Univariate Time Series: Real-World Data
  • 8.3 Multivariate Time Series: Artificial Data
  • 8.4 Multivariate Time Series : Tumor Detection in EEG Time Series
  • 9 Information Processing and Coding in Spatiotemporal Dynamical Systems: Spiking Networks
  • 9.1 Spiking Neurons
  • 9.2 Information Processing and Coding in Single Spiking Neurons
  • 9.3 Information Processing and Coding in Networks of Spiking Neurons
  • 9.4 The Processing and Coding ofDynamical Systems
  • 10 Applications: Information Processing and Coding in Spatiotemporal Dynamical Systems
  • 10.1 The Binding Problem
  • 10.2 Discrimination of Stimulus by Spiking Neural Networks
  • 10.3 Numerical Experiments
  • Epilogue
  • Appendix A Chain Rules, Inequalities and Other Useful Theorems in Information Theory
  • A.1 Chain Rules
  • A.2 Fundamental Inequalities ofInformation Theory
  • Appendix B Univariate and Multivariate Cumulants
  • Appendix C Information Flow of Chaotic Systems: Thermodynamical Formulation
  • l Introduction
  • 2 Dynamical Systems: An Overview 7
  • 2.1 Deterministic Dynamical Systems
  • 2.3 Statistical Time-Series Analysis
  • 3 Statistical Structure Extraction in Dynamical Systems: Parametric Formulation
  • 3.1 Basic Concepts of Information Theory
  • 3.2 Parametric Estimation : Maximum-Likelihood Principle
  • 3.3 Linear Models
  • 3.4 Nonlinear Models
  • 3.5 Density Estimation
  • 3.6 Information-Theoretic Approach to Time-Series Modeling: Redundancy Extraction
  • 4 Applications: Parametric Characterization of Time Series
  • 4.1 Feedforward Learning : Chaotic Dynamics
  • 4.2 Recurrent Learning : Chaotic Dynamics
  • 4.3 Dynamical Overtraining and Lyapunov Penalty Term
  • 4.4 Feedforward and Recurrent Learning of Biomedical Data
  • 4.5 Unsupervised Redundancy-Extraction-Based Modeling: Chaotic Dynamics
  • 4.6 Unsupervised Redundancy Extraction Modeling: Biomedical Data
  • 5 Statistical Structure Extraction in Dynamical Systems: Nonparametric Formulation

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