The DOI matches its Crossref record.
Measuring Information Transfer
Open accessAbstract
An information theoretic measure is derived that quantifies the statistical coherence between systems evolving in time. The standard time delayed mutual information fails to distinguish information that is actually exchanged from shared information due to common history and input signals. In our new approach, these influences are excluded by appropriate conditioning of transition probabilities. The resulting transfer entropy is able to distinguish effectively driving and responding elements and to detect asymmetry in the interaction of subsystems.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
A Tutorial Review of Functional Connectivity Analysis Methods and Their Interpretational Pitfalls
Bastos, A. M., & Schoffelen, J.-M. (2016) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2015.00175 · Cited by 1,313
Granger causality analysis in neuroscience and neuroimaging
Seth, A. K., Barrett, A. B., & Barnett, L. (2015) · The Journal of Neuroscience · doi:10.1523/jneurosci.4399-14.2015 · Cited by 928
Detecting and quantifying causal associations in large nonlinear time series datasets
Runge, J., Nowack, P., Kretschmer, M., Flaxman, S., & Sejdinovic, D. (2019) · Science Advances · doi:10.1126/sciadv.aau4996 · Cited by 979
Modelling and analysis of local field potentials for studying the function of cortical circuits
Einevoll, G. T., Kayser, C., Logothetis, N. K., & Panzeri, S. (2013) · Nature Reviews Neuroscience · doi:10.1038/nrn3599 · Cited by 857
Can social interaction constitute social cognition?
De Jaegher, H., Di Paolo, E., & Gallagher, S. (2010) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2010.06.009 · Cited by 911
Metadata
- Authors
- Schreiber, T.
- Venue
- Physical Review Letters
- Publisher
- American Physical Society (APS)
- Published
- 2000-07-10
- Volume
- 85
- Issue
- 2
- Pages
- 461-464
- ISSN
- 0031-9007 (ISSN-L)
- 1079-7114
- OpenAlex
- W2041782669
- Cited by
- 4,530
- References
- 0
- FWCI
- 8.27
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Measuring Information Transfer. https://app.cochat.ai/papers/10.1103/physrevlett.85.461
- The paper
- Schreiber, T. (2000). Measuring Information Transfer. Physical Review Letters. https://doi.org/10.1103/physrevlett.85.461
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
