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Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between...
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Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
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Emergence of functional information from multivariate correlations.

Christoph Adami1,2,3,4, Nitash C G2,5

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Researchers developed a new information score from complex correlations in symbolic sequences. This score predicts observable properties of new sequences, demonstrating how functional information arises from hierarchical correlations.

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Area of Science:

  • Complex systems
  • Information theory
  • Computational biology

Background:

  • Information content in symbolic sequences (e.g., DNA, proteins, neural signals) is calculable from ensembles but not assignable to individual sequences.
  • This limitation prevents direct correlation of information with sequence-specific observables.

Purpose of the Study:

  • To develop a method for assigning information content to individual sequences.
  • To demonstrate that functional information emerges from multivariate correlations within sequences.
  • To predict observables of new sequences using an information score derived from a training ensemble.

Main Methods:

  • Calculated an information score using multivariate (multiple-variable) correlations within a training ensemble of symbolic sequences.
  • Applied this score to predict observables of out-of-sample sequences.
  • Analyzed the scaling of prediction accuracy with the complexity of correlations.

Main Results:

  • The information score derived from multivariate correlations accurately predicts observables in new sequences.
  • Prediction accuracy scales with the complexity of the identified correlations.
  • This indicates that functional information emerges from a hierarchy of multi-variable correlations.

Conclusions:

  • A novel information score based on multivariate correlations can predict sequence observables.
  • Functional information in symbolic sequences emerges hierarchically from complex correlations.
  • This approach offers a new way to link information content to biological and other complex systems.