Geometric interpretation and data visualisation equivalent describing the DI algorithmic pipeline.

<p>(a) a time-series capturing the dynamics of 3 variables, brain regions, or electrodes. (b) A state-space model is estimated from the time-series dynamics, from which we calculate the pairwise-conditional Granger-causal statistics representing directed information flow. The numeric labels (e...

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Bibliographic Details
Main Author:	Borjan Milinkovic (21349908) (author)
Other Authors:	Lionel Barnett (21349911) (author), Olivia Carter (194606) (author), Anil K. Seth (8755419) (author), Thomas Andrillon (6039964) (author)
Published:	2025
Subjects:	Biophysics Biochemistry Medicine Cell Biology Cancer Infectious Diseases Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified work also provides using information theory two key aspects significant scientific challenge global coupling parameter counteracts global coupling complex neural systems complete computational implementation biophysical neural model independent dynamical process adding dynamical noise macroscopic variable </ dynamical independence </ leveraging transfer entropy div >< p emergent neural dynamics emergent dynamical structure functional integration within dynamical independence transfer entropy dynamical dependence macroscopic dynamics structure remains level dynamics functional integration macroscopic variables macroscopic processes thereby elucidating sustaining coherent results indicate relatively minimised minimal 5 microlevel variables method identifies lower levels less localised g ., functional segregation finding suggests brain organisation balanced points balanced point
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Geometric interpretation and data visualisation equivalent describing the DI algorithmic pipeline.

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