ArticleEntropy (Basel, Switzerland)2020
Computing Integrated Information (
Article in Entropy (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- The integrated information Φ of an integrate and fire network.PLoS computational biology · 2026Article
- Reply to Rourk, C. Comment on "Albantakis et al. Computing the Integrated Information of a Quantum Mechanism.Entropy (Basel, Switzerland) · 2023Article
- Phi fluctuates with surprisal: An empirical pre-study for the synthesis of the free energy principle and integrated information theory.PLoS computational biology · 2023Article
- An implementation of integrated information theory in resting-state fMRI.Communications biology · 2023Article
- Computing the Integrated Information of a Quantum Mechanism.Entropy (Basel, Switzerland) · 2023Article
- On the non-uniqueness problem in integrated information theory.Neuroscience of consciousness · 2023Article
- Emergence of Integrated Information at Macro Timescales in Real Neural Recordings.Entropy (Basel, Switzerland) · 2022Article
- Causal reductionism and causal structures.Nature neuroscience · 2021Review
- Mechanism Integrated Information.Entropy (Basel, Switzerland) · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Integrated information theory (IIT) provides a mathematical framework to characterize the cause-effect structure of a physical system and its amount of integrated information (Φ). An accompanying Python software package ("PyPhi") was recently introduced to implement this framework for the causal analysis of discrete dynamical systems of binary elements. Here, we present an update to PyPhi that extends its applicability to systems constituted of discrete, but multi-valued elements. This allows us to analyze and compare general causal properties of random networks made up of binary, ternary, quaternary, and mixed nodes. Moreover, we apply the developed tools for causal analysis to a simple non-binary regulatory network model (p53-Mdm2) and discuss commonly used binarization methods in light of their capacity to preserve the causal structure of the original system with multi-valued elements.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.