Unifying pairwise interactions in complex dynamics

[1]  E. Ott,et al.  Network inference from short, noisy, low time-resolution, partial measurements: Application to C. elegans neuronal calcium dynamics , 2023, Proceedings of the National Academy of Sciences of the United States of America.

[2]  Tiago P. Peixoto,et al.  Statistical inference links data and theory in network science , 2022, Nature Communications.

[3]  Enrico Amico,et al.  The physics of higher-order interactions in complex systems , 2021, Nature Physics.

[4]  F. Helmchen,et al.  Brain mapping across 16 autism mouse models reveals a spectrum of functional connectivity subtypes , 2021, Molecular Psychiatry.

[5]  Keichi Takahashi,et al.  Experimentally testable whole brain manifolds that recapitulate behavior , 2021, 2106.10627.

[6]  Johannes Lederer.  Theory I: Prediction , 2021, Springer Texts in Statistics.

[7]  Joseph T. Lizier,et al.  Assessing the significance of directed and multivariate measures of linear dependence between time series , 2021 .

[8]  Timothy LaRock,et al.  netrd: A library for network reconstruction and graph distances , 2020, J. Open Source Softw..

[9]  Maxym Myroshnychenko,et al.  Eden-Kramer-Lab/spectral_connectivity: v0.2.5.dev0 , 2020 .

[10]  Julia A. Schmidt,et al.  HCGA: Highly comparative graph analysis for network phenotyping , 2020, bioRxiv.

[11]  Alejandro Pasos Ruiz,et al.  The great multivariate time series classification bake off: a review and experimental evaluation of recent algorithmic advances , 2020, Data Mining and Knowledge Discovery.

[12]  Johannes L. Schönberger,et al.  SciPy 1.0: fundamental algorithms for scientific computing in Python , 2019, Nature Methods.

[13]  Bernhard Schölkopf,et al.  Inferring causation from time series in Earth system sciences , 2019, Nature Communications.

[14]  Soroosh Afyouni,et al.  Effective degrees of freedom of the Pearson's correlation coefficient under autocorrelation , 2019, NeuroImage.

[15]  Andrei Novikov,et al.  PyClustering: Data Mining Library , 2019, J. Open Source Softw..

[16]  Tiago P. Peixoto.  Network Reconstruction and Community Detection from Dynamics , 2019, Physical review letters.

[17]  Olivier Goudet,et al.  Causal Discovery Toolbox: Uncover causal relationships in Python , 2019, 1903.02278.

[18]  Nick S. Jones,et al.  catch22: CAnonical Time-series CHaracteristics , 2019, Data Mining and Knowledge Discovery.

[19]  O. Sporns,et al.  Human cognition involves the dynamic integration of neural activity and neuromodulatory systems , 2019, Nature Neuroscience.

[20]  Lisa Byrge,et al.  High-accuracy individual identification using a “thin slice” of the functional connectome , 2019, Network Neuroscience.

[21]  Michael Flynn,et al.  The UEA multivariate time series classification archive, 2018 , 2018, ArXiv.

[22]  R. Lambiotte,et al.  Community detection in networks without observing edges , 2018, Science Advances.

[23]  Joseph T. Lizier,et al.  IDTxl: The Information Dynamics Toolkit xl: a Python package for the efficient analysis of multivariate information dynamics in networks , 2018, J. Open Source Softw..

[24]  Lisa Byrge,et al.  Accurate prediction of individual subject identity and task, but not autism diagnosis, from functional connectomes , 2018, Human brain mapping.

[25]  Deniz Gençaga,et al.  Transfer Entropy , 2018, Entropy.

[26]  Bernhard Schölkopf,et al.  Cause-Effect Inference by Comparing Regression Errors , 2018, AISTATS.

[27]  Brian A. Nosek,et al.  The preregistration revolution , 2018, Proceedings of the National Academy of Sciences.

[28]  Jian Kong,et al.  Maturation trajectories of cortical resting-state networks depend on the mediating frequency band , 2018, NeuroImage.

[29]  Mikhail Prokopenko,et al.  Minimising the Kullback–Leibler Divergence for Model Selection in Distributed Nonlinear Systems , 2018, Entropy.

[30]  Erik Scheme,et al.  Navigating features: a topologically informed chart of electromyographic features space , 2017, Journal of The Royal Society Interface.

[31]  Michael Breakspear,et al.  The Brain Dynamics Toolbox for Matlab , 2017, bioRxiv.

[32]  C. Priebe,et al.  From Distance Correlation to Multiscale Graph Correlation , 2017, Journal of the American Statistical Association.

[33]  Ben D. Fulcher,et al.  Feature-based time-series analysis , 2017, ArXiv.

[34]  Jörg Kliewer,et al.  Directional and Causal Information Flow in EEG for Assessing Perceived Audio Quality , 2017, IEEE Transactions on Molecular, Biological and Multi-Scale Communications.

[35]  Marco Cuturi,et al.  Soft-DTW: a Differentiable Loss Function for Time-Series , 2017, ICML.

[36]  O. Sporns,et al.  Network neuroscience , 2017, Nature Neuroscience.

[37]  David Schultz,et al.  Nonsmooth analysis and subgradient methods for averaging in dynamic time warping spaces , 2017, Pattern Recognit..

[38]  Krzysztof J. Gorgolewski,et al.  A phenome-wide examination of neural and cognitive function , 2016, Scientific Data.

[39]  C. Koch,et al.  Integrated information theory: from consciousness to its physical substrate , 2016, Nature Reviews Neuroscience.

[40]  C. Koch,et al.  Neural correlates of consciousness: progress and problems , 2016, Nature Reviews Neuroscience.

[41]  José A. R. Fonollosa.  Conditional distribution variability measures for causality detection , 2016, Cause Effect Pairs in Machine Learning.

[42]  Jan-Mathijs Schoffelen,et al.  A Tutorial Review of Functional Connectivity Analysis Methods and Their Interpretational Pitfalls , 2016, Front. Syst. Neurosci..

[43]  S. Amari,et al.  Unified framework for information integration based on information geometry , 2015, Proceedings of the National Academy of Sciences.

[44]  M. Schatz,et al.  Big Data: Astronomical or Genomical? , 2015, PLoS biology.

[45]  Toru Yanagawa,et al.  Measuring Integrated Information from the Decoding Perspective , 2015, PLoS Comput. Biol..

[46]  Nihat Ay,et al.  Information Geometry on Complexity and Stochastic Interaction , 2015, Entropy.

[47]  Joseph T. Lizier,et al.  JIDT: An Information-Theoretic Toolkit for Studying the Dynamics of Complex Systems , 2014, Front. Robot. AI.

[48]  P. Brockwell,et al.  Time Series: Theory and Methods , 2013 .

[49]  Max A. Little,et al.  Highly comparative time-series analysis: the empirical structure of time series and their methods , 2013, Journal of The Royal Society Interface.

[50]  Anind K. Dey,et al.  The Principle of Maximum Causal Entropy for Estimating Interacting Processes , 2013, IEEE Transactions on Information Theory.

[51]  Albert Y. Zomaya,et al.  The local information dynamics of distributed computation in complex systems , 2012 .

[52]  George Sugihara,et al.  Detecting Causality in Complex Ecosystems , 2012, Science.

[53]  Timothy Edward John Behrens,et al.  The Human Connectome Project: A data acquisition perspective , 2012, NeuroImage.

[54]  Bharath K. Sriperumbudur,et al.  Equivalence of distance-based and RKHS-based statistics in hypothesis testing , 2012, ArXiv.

[55]  Ying Liu,et al.  Quantification of Effective Connectivity in the Brain Using a Measure of Directed Information , 2012, Comput. Math. Methods Medicine.

[56]  M. Corbetta,et al.  Large-scale cortical correlation structure of spontaneous oscillatory activity , 2012, Nature Neuroscience.

[57]  R. Heller,et al.  A consistent multivariate test of association based on ranks of distances , 2012, 1201.3522.

[58]  Marisa O. Hollinshead,et al.  The organization of the human cerebral cortex estimated by intrinsic functional connectivity. , 2011, Journal of neurophysiology.

[59]  R. Oostenveld,et al.  An improved index of phase-synchronization for electrophysiological data in the presence of volume-conduction, noise and sample-size bias , 2011, NeuroImage.

[60]  Pierre Gançarski,et al.  A global averaging method for dynamic time warping, with applications to clustering , 2011, Pattern Recognit..

[61]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[62]  Mark W. Woolrich,et al.  Network modelling methods for FMRI , 2011, NeuroImage.

[63]  M. V. D. Heuvel,et al.  Exploring the brain network: A review on resting-state fMRI functional connectivity , 2010, European Neuropsychopharmacology.

[64]  Bernhard Schölkopf,et al.  Inferring deterministic causal relations , 2010, UAI.

[65]  Martin Vinck,et al.  The pairwise phase consistency: A bias-free measure of rhythmic neuronal synchronization , 2010, NeuroImage.

[66]  Olivier J. J. Michel,et al.  On directed information theory and Granger causality graphs , 2010, Journal of Computational Neuroscience.

[67]  Trevor Hastie,et al.  Regularization Paths for Generalized Linear Models via Coordinate Descent. , 2010, Journal of statistical software.

[68]  A. Seth,et al.  Granger causality and transfer entropy are equivalent for Gaussian variables. , 2009, Physical review letters.

[69]  Roman Borisyuk,et al.  Selective attention model with spiking elements , 2009, Neural Networks.

[70]  Alfred O. Hero,et al.  Shrinkage Algorithms for MMSE Covariance Estimation , 2009, IEEE Transactions on Signal Processing.

[71]  Sune Lehmann,et al.  Link communities reveal multiscale complexity in networks , 2009, Nature.

[72]  R. Lambiotte,et al.  Line graphs, link partitions, and overlapping communities. , 2009, Physical review. E, Statistical, nonlinear, and soft matter physics.

[73]  Bernhard Schölkopf,et al.  Nonlinear causal discovery with additive noise models , 2008, NIPS.

[74]  P. Cochat,et al.  Et al , 2008, Archives de pediatrie : organe officiel de la Societe francaise de pediatrie.

[75]  Giulio Tononi,et al.  Integrated Information in Discrete Dynamical Systems: Motivation and Theoretical Framework , 2008, PLoS Comput. Biol..

[76]  Mingzhou Ding,et al.  Analyzing information flow in brain networks with nonparametric Granger causality , 2008, NeuroImage.

[77]  Matthäus Staniek,et al.  Symbolic transfer entropy. , 2008, Physical review letters.

[78]  K. Müller,et al.  Robustly estimating the flow direction of information in complex physical systems. , 2007, Physical review letters.

[79]  Le Song,et al.  A Kernel Statistical Test of Independence , 2007, NIPS.

[80]  Maria L. Rizzo,et al.  Measuring and testing dependence by correlation of distances , 2007, 0803.4101.

[81]  Mingzhou Ding,et al.  Estimating Granger causality from fourier and wavelet transforms of time series data. , 2007, Physical review letters.

[82]  C. Stam,et al.  Phase lag index: Assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sources , 2007, Human brain mapping.

[83]  L.A. Baccald,et al.  Generalized Partial Directed Coherence , 2007, 2007 15th International Conference on Digital Signal Processing.

[84]  Monika Sharma,et al.  Chemical oscillations , 2006 .

[85]  Benjamin J. Shannon,et al.  Molecular, Structural, and Functional Characterization of Alzheimer's Disease: Evidence for a Relationship between Default Activity, Amyloid, and Memory , 2005, The Journal of Neuroscience.

[86]  G. Tononi.  An information integration theory of consciousness , 2004, BMC Neuroscience.

[87]  M. Hallett,et al.  Identifying true brain interaction from EEG data using the imaginary part of coherency , 2004, Clinical Neurophysiology.

[88]  Olivier Ledoit,et al.  A well-conditioned estimator for large-dimensional covariance matrices , 2004 .

[89]  M. Kaminski,et al.  Determination of information flow direction among brain structures by a modified directed transfer function (dDTF) method , 2003, Journal of Neuroscience Methods.

[90]  Dimitrios Gunopulos,et al.  Discovering similar multidimensional trajectories , 2002, Proceedings 18th International Conference on Data Engineering.

[91]  Thomas Wennekers,et al.  Temporal Infomax Leads to Almost Deterministic Dynamical Systems , 2002, Neurocomputing.

[92]  Luiz A. Baccalá,et al.  Partial directed coherence: a new concept in neural structure determination , 2001, Biological Cybernetics.

[93]  S. Strogatz.  From Kuramoto to Crawford: exploring the onset of synchronization in populations of coupled oscillators , 2000 .

[94]  Schreiber,et al.  Measuring information transfer , 2000, Physical review letters.

[95]  Katrien van Driessen,et al.  A Fast Algorithm for the Minimum Covariance Determinant Estimator , 1999, Technometrics.

[96]  H. Flor,et al.  A spelling device for the paralysed , 1999, Nature.

[97]  DeLiang Wang,et al.  Image Segmentation Based on Oscillatory Correlation , 1997, Neural Computation.

[98]  G. Reinsel.  Elements of Multivariate Time Series Analysis , 1995 .

[99]  A. R. Gilpin.  Table for Conversion of Kendall'S Tau to Spearman'S Rho Within the Context of Measures of Magnitude of Effect for Meta-Analysis , 1993 .

[100]  G. Kaplan,et al.  On Information Rates for Mismatched Decoders , 1993, Proceedings. IEEE International Symposium on Information Theory.

[101]  G. Wang,et al.  Directed coherence as a measure of interhemispheric correlation of EEG. , 1992, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[102]  Katarzyna J. Blinowska,et al.  A new method of the description of the information flow in the brain structures , 1991, Biological Cybernetics.

[103]  C. Granger,et al.  Co-integration and error correction: representation, estimation and testing , 1987 .

[104]  Theiler,et al.  Spurious dimension from correlation algorithms applied to limited time-series data. , 1986, Physical review. A, General physics.

[105]  Yoshiki Kuramoto,et al.  Chemical Oscillations, Waves, and Turbulence , 1984, Springer Series in Synergetics.

[106]  J. Gotman.  Measurement of small time differences between EEG channels: method and application to epileptic seizure propagation. , 1983, Electroencephalography and clinical neurophysiology.

[107]  J. Geweke,et al.  Measurement of Linear Dependence and Feedback between Multiple Time Series , 1982 .

[108]  S. Chiba,et al.  Dynamic programming algorithm optimization for spoken word recognition , 1978 .

[109]  F. Itakura,et al.  Minimum prediction residual principle applied to speech recognition , 1975 .

[110]  C. Granger.  Investigating causal relations by econometric models and cross-spectral methods , 1969 .

[111]  M. Kendall.  A NEW MEASURE OF RANK CORRELATION , 1938 .

[112]  Illtyd Trethowan.  Causality , 1938 .

[113]  Teoria Statistica Delle Classi e Calcolo Delle Probabilità , 2022, The SAGE Encyclopedia of Research Design.

[114]  Samuel Kaski,et al.  Multivariate , 2021, Encyclopedic Dictionary of Archaeology.

[115]  Marc Rußwurm,et al.  Tslearn, A Machine Learning Toolkit for Time Series Data , 2020, J. Mach. Learn. Res..

[116]  Jordi Muñoz-Marí,et al.  The Causality for Climate Competition , 2019, NeurIPS.

[117]  Zhang Liu,et al.  Interior-point methods for large-scale cone programming , 2011 .

[118]  Jeffrey T. Leek,et al.  Statistical Applications in Genetics and Molecular Biology The Joint Null Criterion for Multiple Hypothesis Tests , 2011 .

[119]  Marisa O. Hollinshead,et al.  The organization of the human cerebral cortex estimated by intrinsic functional connectivity , 2011 .

[120]  Skipper Seabold,et al.  Statsmodels: Econometric and Statistical Modeling with Python , 2010, SciPy.

[121]  Ariel Rokem,et al.  Nitime: time-series analysis for neuroimaging data , 2009 .

[122]  Kevin P. Murphy.  Information theory , 1998 .

[123]  Alois Schlögl,et al.  Analyzing event-related EEG data with multivariate autoregressive parameters. , 2006, Progress in brain research.

[124]  Michael Eichler,et al.  Abstract Journal of Neuroscience Methods xxx (2005) xxx–xxx Testing for directed influences among neural signals using partial directed coherence , 2005 .

[125]  S. Venkatesh,et al.  Online Context Recognition in Multisensor Systems using Dynamic Time Warping , 2005, 2005 International Conference on Intelligent Sensors, Sensor Networks and Information Processing.

[126]  David J. C. MacKay,et al.  Information Theory, Inference, and Learning Algorithms , 2004, IEEE Transactions on Information Theory.

[127]  Kunihiko Kaneko,et al.  Complex Systems: Chaos and Beyond , 2001 .

[128]  F. Varela,et al.  Measuring phase synchrony in brain signals , 1999, Human brain mapping.

[129]  Gerhard Kramer,et al.  Directed information for channels with feedback , 1998 .

[130]  Luiz A. Baccalá,et al.  Studying the Interaction Between Brain Structures via Directed Coherence and Granger Causality , 1998 .

[131]  M. Hasselmo,et al.  Gaussian Processes for Regression , 1995, NIPS.

[132]  Yongcheol Shin,et al.  An Autoregressive Distributed Lag Modelling Approach to Cointegration Analysis , 1995 .

[133]  DeLiang Wang,et al.  Locally excitatory globally inhibitory oscillator networks , 1995, IEEE Transactions on Neural Networks.

[134]  Mona E. Zaghloul,et al.  Silicon Implementation of Pulse Coded Neural Networks , 1994 .

[135]  J. Massey.  CAUSALITY, FEEDBACK AND DIRECTED INFORMATION , 1990 .

[136]  R. Engle,et al.  COINTEGRATION AND ERROR CORRECTION: REPRESENTATION , 1987 .

[137]  F. Takens.  Detecting strange attractors in turbulence , 1981 .

[138]  M. Bartlett.  On the Theoretical Specification and Sampling Properties of Autocorrelated Time‐Series , 1946 .