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Synergy, redundancy, and multivariate information measures: an experimentalist's perspective.
Nicholas Timme1, Wesley Alford, Benjamin Flecker
1Department of Physics, Indiana University,, Bloomington, IN, 47405-7105, USA, nmtimme@umail.iu.edu.
Journal of Computational Neuroscience
|July 4, 2013
Summary
This study reviews multivariate information measures for complex systems, highlighting synergy and redundancy. It provides a comparative analysis and software to help researchers choose the best measure for their data.
Area of Science:
- Complex Systems Research
- Information Theory
- Computational Neuroscience
Background:
- Bivariate information measures are well-established for quantifying interactions.
- Multivariate information measures are crucial for analyzing complex systems with three or more variables.
- Existing multivariate measures vary significantly, necessitating a clear comparison.
Purpose of the Study:
- To review and compare various multivariate information measures.
- To emphasize the relationship between these measures and synergistic/redundant interactions.
- To provide practical guidance for selecting appropriate measures and offer accessible software.
Main Methods:
- Comparative analysis of different multivariate information measures.
- Application of measures to simple model systems.
- Analysis of neural spiking data from developing dissociated cultures.
Main Results:
- Demonstration of how different multivariate measures capture synergy and redundancy distinctly.
- Illustration of measure performance across model systems and real neural data.
- Validation of the utility of these measures in analyzing complex biological systems.
Conclusions:
- The choice of multivariate information measure impacts the understanding of complex system interactions.
- A systematic comparison aids researchers in selecting the most suitable measure for their specific goals.
- Accessible software is provided to facilitate the application of these advanced information-theoretic tools.
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