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Publications using IDTxl

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Patricia Wollstadt edited this page Nov 30, 2022 · 13 revisions

If you want to cite IDTxl in your publication, please use

P. Wollstadt, J. T. Lizier, R. Vicente, C. Finn, M. Martinez-Zarzuela, P. Mediano, L. Novelli, M. Wibral (2019). IDTxl: The Information Dynamics Toolkit xl: a Python package for the efficient analysis of multivariate information dynamics in networks. Journal of Open Source Software, 4(34), 1081. https://doi.org/10.21105/joss.01081

For an automatically compiled list of all citations, head over to Google Scholar or JOSS.

List of publications using IDTxl for data analysis:

  • Basse et al. (2022). Leading indicators for US house prices: New evidence and implications for EU financial risk managers. European Financial Management, 28(3), 722-743. https://doi.org/10.1111/eufm.12325
  • Rinea et al. (2022). Diversity dependence is a ubiquitous phenomenon across Phanerozoic oceans. Science Advances, 8(43), eadd9620. https://doi.org/10.1126/sciadv.add9620
  • Newman et al. (2022). Revealing the Dynamics of Neural Information Processing with Multivariate Information Decomposition. Entropy 24(7), 930. https://doi.org/10.3390/e24070930
  • Pinzuti et al. (2002). Information theoretic evidence for layer- and frequency-specific changes in cortical information processing under anesthesia. _bioRXiv. https://doi.org/10.1101/2022.07.15.500162
  • Varley et al. (2022). Information processing dynamics in neural networks of macaque cerebral cortex reflect cognitive state and behavior. bioRXiv. https://doi.org/10.1101/2021.09.05.458983
  • Fakhar et al. (2021). Systematic Perturbation of an Artificial Neural Network: A Step Towards Quantifying Causal Contributions in The Brain. bioRXiv https://doi.org/10.1101/2021.11.04.467251
  • Varley et al. (2021). Information dynamics in neuronal networks of macaque cerebral cortex reflect cognitive state and behavior. bioRXiv https://doi.org/10.1101/2021.09.05.458983
  • Wollstadt et al. (2021). Interaction-Aware Sensitivity Analysis for Aerodynamic Optimization Results using Information Theory. 2021 IEEE Symposium Series on Computational Intelligence (SSCI). https://arxiv.org/abs/2112.05609
  • Wismüller et al. (2021). Large-scale nonlinear Granger causality for inferring directed dependence from short multivariate time-series data. Scientific Reports 11, 7817. https://doi.org/10.1038/s41598-021-87316-6
  • Wollstadt et al. (2021). A Rigorous Information-Theoretic Definition of Redundancy and Relevancy in Feature Selection Based on (Partial) Information Decomposition. arXiv:2105.04187 [cs.IT]. https://arxiv.org/abs/2105.04187
  • Wollstadt et al. (2021). Quantifying the predictability of visual scanpaths using active information storage. Entropy, 23(2), 167. https://doi.org/10.3390/e23020167
  • Wiebel-Herboth et al. (2021). Measuring inter-and intra-individual differences in visual scan patterns in a driving simulator experiment using active information storage. PLoS one, 16(3), e0248166. https://doi.org/10.1371/journal.pone.0248166
  • Frontera et al. (2020). Bidirectional control of fear memories by cerebellar neurons projecting to the ventrolateral periaqueductal grey. Nature Communications, 11, 5207. https://doi.org/10.1038/s41467-020-18953-0
  • Sangati & Hofmann (2020). The Role of Co-Representations in Joint Tracking. ALIFE 2020: The 2020 Conference on Artificial Life, 526-534. https://doi.org/10.1162/isal_a_00259
  • García-Medina & Hernández C. (2020). Network Analysis of Multivariate Transfer Entropy of Cryptocurrencies in Times of Turbulence. Entropy, 22(7), 760. https://doi.org/10.3390/e22070760
  • Sych et al. (2020). Mesoscale brain dynamics reorganizes and stabilizes during learning. bioRxiv, https://doi.org/10.1101/2020.07.08.193334
  • Novelli & Lizier (2020). Inferring network properties from time series via transfer entropy and mutual information: validation of bivariate versus multivariate approaches. arXiv:2007.07500 [q-bio.NC]. https://arxiv.org/abs/2007.07500
  • Novelli et al. (2020). Deriving pairwise transfer entropy from network structure and motifs. Proc. Royal Soc. A, 476(2236), 20190779. https://doi.org/10.1098/rspa.2019.0779
  • Pinzuti et al. (2020). Measuring spectrally-resolved information transfer for sender- and receiver-specific frequencies. PLoS Comput Biol 16(12), e1008526_. https://doi.org/10.1371/journal.pcbi.1008526
  • Herzog et al. (2020). Evolving artificial neural networks with feedback. Neural Networks, 123, 153-162. https://doi.org/10.1016/j.neunet.2019.12.004
  • Harmah et a;. (2020). Measuring the Non-linear Directed Information Flow in Schizophrenia by Multivariate Transfer Entropy. Front Comp Neurosci, 13, 85. https://doi.org/10.3389/fncom.2019.00085
  • Warrick & Hamilton (2019). Information theoretic measures of perinatal cardiotocography synchronization. Math Biosci Eng, 17(3), 2179-2192. https://doi.org/10.3934/mbe.2020116
  • Novelli et al. (2019). Large-scale directed network inference with multivariate transfer entropy and hierarchical statistical testing. Network Neuroscience, 3(3), 827-847. https://doi.org/10.1162/netn_a_00092

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