Estimating the Unique Information of Continuous Variables

Understand

The integration and transfer of information from multiple sources to multiple targets is a core motive of neural systems.

• The emerging field of partial information decomposition (PID) provides a novel information-theoretic lens into these mechanisms by identifying synergistic, redundant, and unique contributions to the mutual information between one and several variables.
• While many works have studied aspects of PID for Gaussian and discrete distributions, the case of general continuous distributions is still uncharted territory.
• In this work we present a method for estimating the unique information in continuous distributions, for the case of one versus two variables.