ArticleFrontiers in physiology2020
Quantifying Dynamical High-Order Interdependencies From the O-Information: An Application to Neural Spiking Dynamics.
Article in Frontiers in physiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Charting higher-order models of brain function beyond pairwise interactions.Nature communications · 2026Article
- Machine-enhanced reconstruction of functional connectomes unravels discriminative brain sub-systems in health and disease.Scientific reports · 2026Article
- THOI: An efficient and accessible library for computing higher-order interactions enhanced by batch-processing.PloS one · 2026Article
- Emergent multiscale organisation of neural dynamics fragments in anaesthesia.Imaging neuroscience (Cambridge, Mass.) · 2026Article
- Mining higher-order triadic interactions.Nature communications · 2025Article
- High-Order Information Analysis of Epileptogenesis in the Pilocarpine Rat Model of Temporal Lobe Epilepsy.eNeuro · 2025Article
- Characterising high-order interdependence via entropic conjugation.Communications physics · 2025Article
- Temporal interaction information and laser evoked responses: preliminary results in fibromyalgia patients with small fibers pathology.Frontiers in network physiology · 2025Article
- Higher-order connectomics of human brain function reveals local topological signatures of task decoding, individual identification, and behavior.Nature communications · 2024Article
- Efficient Search Algorithms for Identifying Synergistic Associations in High-Dimensional Datasets.Entropy (Basel, Switzerland) · 2024Article
- Bias in O-Information Estimation.Entropy (Basel, Switzerland) · 2024Article
- Transcranial ultrasound stimulation effect in the redundant and synergistic networks consistent across macaques.Network neuroscience (Cambridge, Mass.) · 2024Article
- Statistical Approaches to Identify Pairwise and High-Order Brain Functional Connectivity Signatures on a Single-Subject Basis.Life (Basel, Switzerland) · 2023Article
- Multivariate information theory uncovers synergistic subsystems of the human cerebral cortex.Communications biology · 2023Article
- Connectivity Analysis in EEG Data: A Tutorial Review of the State of the Art and Emerging Trends.Bioengineering (Basel, Switzerland) · 2023Review
- Decomposing past and future: Integrated information decomposition based on shared probability mass exclusions.PloS one · 2023Article
- Gradients of O-information highlight synergy and redundancy in physiological applications.Frontiers in network physiology · 2023Article
- Pairwise and high-order dependencies in the cryptocurrency trading network.Scientific reports · 2022Article
- Revealing the Dynamics of Neural Information Processing with Multivariate Information Decomposition.Entropy (Basel, Switzerland) · 2022Review
- The New Field of Network Physiology: Building the Human Physiolome.Frontiers in network physiology · 2021Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We address the problem of efficiently and informatively quantifying how multiplets of variables carry information about the future of the dynamical system they belong to. In particular we want to identify groups of variables carrying redundant or synergistic information, and track how the size and the composition of these multiplets changes as the collective behavior of the system evolves. In order to afford a parsimonious expansion of shared information, and at the same time control for lagged interactions and common effect, we develop a dynamical, conditioned version of the O-information, a framework recently proposed to quantify high-order interdependencies via multivariate extension of the mutual information. The dynamic O-information, here introduced, allows to separate multiplets of variables which influence synergistically the future of the system from redundant multiplets. We apply this framework to a dataset of spiking neurons from a monkey performing a perceptual discrimination task. The method identifies synergistic multiplets that include neurons previously categorized as containing little relevant information individually.
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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.