Transfer entropy—a model-free measure of effective connectivity for the neurosciences

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Vicente, Raul, et al. “Transfer entropy—a Model-Free Measure of Effective Connectivity for the Neurosciences”. Journal of Computational Neuroscience, vol. 30, no. 1, 2010, pp. 45-67, https://doi.org/10.1007/s10827-010-0262-3.
Vicente, R., Wibral, M., Lindner, M., & Pipa, G. (2010). Transfer entropy—a model-free measure of effective connectivity for the neurosciences. Journal of Computational Neuroscience, 30(1), 45-67. https://doi.org/10.1007/s10827-010-0262-3
Vicente R, Wibral M, Lindner M, Pipa G. Transfer entropy—a model-free measure of effective connectivity for the neurosciences. Journal of Computational Neuroscience. 2010;30(1):45-67.
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The category Medicine: Internal medicine: Neurosciences. Biological psychiatry. Neuropsychiatry 66 is the most frequently represented among the references in this article. It primarily includes studies from Journal of Neurophysiology The chart below illustrates the number of referenced publications per year.
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Citations Analysis
The first research to cite this article was titled TRENTOOL: A Matlab open source toolbox to analyse information flow in time series data with transfer entropy and was published in 2011. The most recent citation comes from a 2024 study titled TRENTOOL: A Matlab open source toolbox to analyse information flow in time series data with transfer entropy . This article reached its peak citation in 2021 , with 65 citations.It has been cited in 221 different journals, 26% of which are open access. Among related journals, the Entropy cited this research the most, with 43 citations. The chart below illustrates the annual citation trends for this article.
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