Data-Efficient Neural Mutual Information Estimation for Capturing Brain-to-Brain Communication
Abstract
Measuring Mutual Information (MI) between high-dimensional, continuous, random variables from observed samples has wide theoretical and practical applications. Traditional MI methods, capable of capturing MI between low-dimensional signals, fall short when dimensionality increases and are not scalable. Existing neural approaches search for a d-dimensional neural network that maximizes a variational lower bound for mutual information estimation; however, this requires O(d log d) observed samples to prevent the neural network from overfitting. For practical mutual information estimation in real world applications, data is not always available at a surplus, especially in cases where acquisition of the data is prohibitively expensive, for example in fMRI analysis. This effort introduces a scalable, data-efficient mutual information estimator. BY coupling a learning-based view of the MI lower bound with meta-learning, NeuralMI achieves high-confidence estimations irrespective of network size and with improved accuracy at practical dataset sizes. The effectiveness has been demonstrated on synthetic benchmarks as well as a real world application of fMRI inter-subject correlation analysis.
Document Details
- Document Type
- Technical Report
- Publication Date
- Sep 27, 2019
- Accession Number
- AD1081493
Entities
People
- Ajay Divakaran
- Indranil Sur
- Mohamed Amer
- Sam Nastase
- Uri Hasson
- Xiao Lin
Organizations
- SRI International