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Decomposing past and future: Integrated information decomposition based on shared probability mass exclusions.

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Summary

A new method, integrated information decomposition (Iτsx), analyzes complex systems by breaking down temporal interactions. This reveals emergent dynamics in neural activity and neuronal avalanches.

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Area of Science:

  • Complex Systems Science
  • Information Theory
  • Computational Neuroscience

Background:

  • Complex systems exhibit causal interactions shaping future states.
  • Modeling these interactions requires decomposing information flow over time.
  • Existing measures often focus on single dependency types.

Purpose of the Study:

  • To propose a novel information-theoretic measure for temporal dependency (Iτsx).
  • To demonstrate how integrated information decomposition can reveal emergent and higher-order interactions.
  • To analyze information processing in neural systems and neuronal avalanches.

Main Methods:

  • Developed an information-theoretic measure (Iτsx) based on local probability mass exclusions.
  • Applied the decomposition to spontaneous spiking activity in rat cerebral cortex neural cultures.
  • Analyzed the time-resolved computational structure of neuronal avalanches.

Main Results:

  • The Iτsx framework reveals emergent and higher-order interactions in complex systems.
  • Information processing modes are distributed across neural cultures.
  • Distinct temporal profiles of information dynamics were observed during neuronal avalanches, with most activity preceding the midpoint of the cascade.

Conclusions:

  • Integrated information decomposition offers a more comprehensive view of system dynamics than single measures.
  • Iτsx provides insights into the computational structure of complex systems at different scales.
  • The method is applicable to understanding information processing in neural networks and other complex systems.