Coupled Hidden Markov Models for Estimating Multivariate Transfer Entropy in Neural Mass Models: A Simulation Study
Published in 29th Iranian Conference on Biomedical Engineering (ICBME), 2022
This paper presents a comprehensive simulation study investigating the use of Coupled Hidden Markov Models (CHMMs) for estimating multivariate transfer entropy in neural mass models. The work addresses the challenge of quantifying information flow in complex neural networks using advanced statistical modeling techniques.
The study demonstrates the effectiveness of CHMMs in capturing the dynamic interactions between neural populations and provides insights into information transfer patterns in brain networks. This research received the 1st Rank Oral Presentation Award at the conference, highlighting its significance in the biomedical engineering community. The findings have important implications for understanding neural connectivity and information processing in the brain.
Recommended citation: Karimi, S., & Shamsollahi, M. B. (2022). "Coupled Hidden Markov Models for Estimating Multivariate Transfer Entropy in Neural Mass Models: A Simulation Study." In 29th Iranian Conference on Biomedical Engineering (ICBME).
Download Paper