Summary of Which Bits Went Where? Past and Future Transfer Entropy Decomposition with the Information Bottleneck, by Kieran A. Murphy et al.
Which bits went where? Past and future transfer entropy decomposition with the information bottleneck
by Kieran A. Murphy, Zhuowen Yin, Dani S. Bassett
First submitted to arxiv on: 7 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Information Theory (cs.IT)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces a novel method to decompose transfer entropy, a measure that detects causal relationships between time series, by localizing the bits of variation on both sides of information flow. The proposed approach employs the information bottleneck (IB) to compress the time series and identify the transferred entropy. The authors demonstrate their method’s effectiveness in several synthetic recurrent processes and an experimental mouse dataset of concurrent behavioral and neural activity, highlighting nuanced dynamics within information flow. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand how different parts of a complex system work together. It’s like trying to figure out what one person is telling another, even if they’re not saying the same thing. The researchers developed a new way to analyze this “information flow” by breaking it down into smaller pieces and seeing where the information comes from. They tested their method on some pretend data and real data about mice, and found that it helped them see more details about how these systems work. |
Keywords
» Artificial intelligence » Time series
