ReviewEntropy (Basel, Switzerland)2022
Revealing the Dynamics of Neural Information Processing with Multivariate Information Decomposition.
Review in Entropy (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
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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.
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Who cites it
20 citing papers in PubMed, 37 citations in OpenAlex.
- Charting higher-order models of brain function beyond pairwise interactions.Nature communications · 2026Article
- Cholinergic Modulation of Proteinoid Microsphere Networks as Prebiotic Depression Models.ACS physical chemistry Au · 2026Article
- Convergent transcriptomic and connectomic controllers of information integration and its anaesthetic breakdown across mammalian brains.Nature human behaviour · 2026Article
- THOI: An efficient and accessible library for computing higher-order interactions enhanced by batch-processing.PloS one · 2026Article
- Neurodatascience: Past, Present, and Future.Data science in science · 2026Article
- Emergence of a synergistic scaffold in the brains of human infants.Communications biology · 2025Article
- Neural network properties of hydrophilic polymers as a key for development of the general theory of evolution.Royal Society open science · 2025Review
- Revealing non-trivial information structures in aneural biological tissues via functional connectivity.PLoS computational biology · 2025Article
- Time-varying synergy/redundancy dominance in the human cerebral cortex.Journal of physics. Complexity · 2025Article
- A Synergistic Perspective on Multivariate Computation and Causality in Complex Systems.Entropy (Basel, Switzerland) · 2024Article
- A synergistic workspace for human consciousness revealed by Integrated Information Decomposition.eLife · 2024Article
- A Partial Information Decomposition for Multivariate Gaussian Systems Based on Information Geometry.Entropy (Basel, Switzerland) · 2024Article
- Response inhibition in premotor cortex corresponds to a complex reshuffle of the mesoscopic information network.Network neuroscience (Cambridge, Mass.) · 2024Article
- Generalized decomposition of multivariate information.PloS one · 2024Article
- The serotonergic psychedelic N,N-dipropyltryptamine alters information-processing dynamics in in vitro cortical neural circuits.Network neuroscience (Cambridge, Mass.) · 2024Article
- Behavioural relevance of redundant and synergistic stimulus information between functionally connected neurons in mouse auditory cortex.Brain informatics · 2023Article
- Partial entropy decomposition reveals higher-order information structures in human brain activity.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Multivariate information theory uncovers synergistic subsystems of the human cerebral cortex.Communications biology · 2023Article
- Information-processing dynamics in neural networks of macaque cerebral cortex reflect cognitive state and behavior.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Untangling Synergistic Effects of Intersecting Social Identities with Partial Information Decomposition.Entropy (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
Abstract
The varied cognitive abilities and rich adaptive behaviors enabled by the animal nervous system are often described in terms of information processing. This framing raises the issue of how biological neural circuits actually process information, and some of the most fundamental outstanding questions in neuroscience center on understanding the mechanisms of neural information processing. Classical information theory has long been understood to be a natural framework within which information processing can be understood, and recent advances in the field of multivariate information theory offer new insights into the structure of computation in complex systems. In this review, we provide an introduction to the conceptual and practical issues associated with using multivariate information theory to analyze information processing in neural circuits, as well as discussing recent empirical work in this vein. Specifically, we provide an accessible introduction to the
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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.