ArticleScientific reports2021
Convergent cross sorting for estimating dynamic coupling.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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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
12 citing papers in PubMed.
- A Multiscale Spatiotemporal Causal Mapping Algorithm for Revealing Neural Network Mechanisms of Transcutaneous Auricular Vagus Nerve Stimulation.Human brain mapping · 2026Article
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- Massively Parallel Imitation Learning of Mouse Forelimb Musculoskeletal Reaching Dynamics.ArXiv · 2025Article
- Detecting dynamical causality by intersection cardinal concavity.Fundamental research · 2025Article
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- Beyond the Nobel prizes: towards new synergies between Computational Neuroscience and Artificial Intelligence.Biological cybernetics · 2024Article
- Fractal Conditional Correlation Dimension Infers Complex Causal Networks.Entropy (Basel, Switzerland) · 2024Article
- Causalized Convergent Cross Mapping and Its Implementation in Causality Analysis.Entropy (Basel, Switzerland) · 2024Article
- Causalized convergent cross-mapping and its approximate equivalence with directed information in causality analysis.PNAS nexus · 2024Article
- Information-Theoretic Analysis of Cardio-Respiratory Interactions in Heart Failure Patients: Effects of Arrhythmias and Cardiac Resynchronization Therapy.Entropy (Basel, Switzerland) · 2023Article
- Usefulness and limitations of convergent cross sorting and continuity scaling methods for their application in simulated and real-world time series.Royal Society open science · 2023Article
- Interactive neurorobotics: Behavioral and neural dynamics of agent interactions.Frontiers in psychology · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Natural systems exhibit diverse behavior generated by complex interactions between their constituent parts. To characterize these interactions, we introduce Convergent Cross Sorting (CCS), a novel algorithm based on convergent cross mapping (CCM) for estimating dynamic coupling from time series data. CCS extends CCM by using the relative ranking of distances within state-space reconstructions to improve the prior methods' performance at identifying the existence, relative strength, and directionality of coupling across a wide range of signal and noise characteristics. In particular, relative to CCM, CCS has a large performance advantage when analyzing very short time series data and data from continuous dynamical systems with synchronous behavior. This advantage allows CCS to better uncover the temporal and directional relationships within systems that undergo frequent and short-lived switches in dynamics, such as neural systems. In this paper, we validate CCS on simulated data and demonstrate its applicability to electrophysiological recordings from interacting brain regions.
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Registered trials
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