AI Article Synopsis
- The integration of information from various sources to multiple targets is fundamental to neural systems, with partial information decomposition (PID) providing insights into mutual information contributions.
- Despite progress in studying PID for Gaussian and discrete distributions, this work introduces a method to estimate unique information in general continuous distributions involving one and two variables.
- By employing copula decompositions and variational autoencoder techniques, the method achieves strong results with Gaussian distributions and demonstrates effectiveness in neural models, revealing intricate relationships between redundancy, synergy, and unique information in networked neurons.
Article Abstract
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. Our method solves the associated optimization problem over the space of distributions with fixed bivariate marginals by combining copula decompositions and techniques developed to optimize variational autoencoders. We obtain excellent agreement with known analytic results for Gaussians, and illustrate the power of our new approach in several brain-inspired neural models. Our method is capable of recovering the effective connectivity of a chaotic network of rate neurons, and uncovers a complex trade-off between redundancy, synergy and unique information in recurrent networks trained to solve a generalized XOR task.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9137417 | PMC |
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