Measuring Information-Transfer Delays
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Viola Priesemann | Joseph T. Lizier | Felix Siebenhühner | Raul Vicente | Michael Wibral | Nicolae Pampu | Hannes Seiwert | Michael Lindner | M. Wibral | Raul Vicente | J. Lizier | V. Priesemann | Michael Lindner | F. Siebenhühner | Hannes Seiwert | Nicolae Pampu
[1] J. Ford,et al. Schizophrenia, myelination, and delayed corollary discharges: a hypothesis. , 2012, Schizophrenia bulletin.
[2] J. Kaiser,et al. Decomposition of working memory-related scalp ERPs: crossvalidation of fMRI-constrained source analysis and ICA. , 2008, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.
[3] S. Pethel,et al. Distinguishing anticipation from causality: anticipatory bias in the estimation of information flow. , 2011, Physical review letters.
[4] Jakob Heinzle,et al. Multivariate information-theoretic measures reveal directed information structure and task relevant changes in fMRI connectivity , 2010, Journal of Computational Neuroscience.
[5] Gordon Pipa,et al. Assessing coupling dynamics from an ensemble of time series , 2010, Entropy.
[6] A. Ledberg,et al. When two become one: the limits of causality analysis of brain dynamics. , 2012, PloS one.
[7] Daniel Polani,et al. Information Flows in Causal Networks , 2008, Adv. Complex Syst..
[8] R. Marimont,et al. Nearest Neighbour Searches and the Curse of Dimensionality , 1979 .
[9] Olivier J. J. Michel,et al. On directed information theory and Granger causality graphs , 2010, Journal of Computational Neuroscience.
[10] M. Ariel,et al. Visual-response properties of neurons in turtle basal optic nucleus in vitro. , 1990, Journal of neurophysiology.
[11] Mikhail Prokopenko,et al. Differentiating information transfer and causal effect , 2008, 0812.4373.
[12] Matthäus Staniek,et al. Symbolic transfer entropy: inferring directionality in biosignals , 2009, Biomedizinische Technik. Biomedical engineering.
[13] M. A. A. Barbosa,et al. Entropy reduction effect imposed by hydrogen bond formation on protein folding cooperativity: evidence from a hydrophobic minimalist model. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.
[14] Boris Gourévitch,et al. Evaluating information transfer between auditory cortical neurons. , 2007, Journal of neurophysiology.
[15] Michael D. Todd,et al. Dynamic system change detection using a modification of the transfer entropy , 2009 .
[16] Albert Y. Zomaya,et al. Local measures of information storage in complex distributed computation , 2012, Inf. Sci..
[17] Luca Faes,et al. Non-uniform multivariate embedding to assess the information transfer in cardiovascular and cardiorespiratory variability series , 2012, Comput. Biol. Medicine.
[18] K. Müller,et al. Robustly estimating the flow direction of information in complex physical systems. , 2007, Physical review letters.
[19] J M Nichols,et al. Detecting nonlinearity in structural systems using the transfer entropy. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.
[20] K. Tsakalis,et al. Information Flow and Application to Epileptogenic Focus Localization From Intracranial EEG , 2009, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[21] Sergio Martinoia,et al. Evaluation of the Performance of Information Theory-Based Methods and Cross-Correlation to Estimate the Functional Connectivity in Cortical Networks , 2009, PloS one.
[22] Luca Faes,et al. Bivariate nonlinear prediction to quantify the strength of complex dynamical interactions in short-term cardiovascular variability , 2006, Medical and Biological Engineering and Computing.
[23] Jochen Kaiser,et al. Transfer entropy in magnetoencephalographic data: quantifying information flow in cortical and cerebellar networks. , 2011, Progress in biophysics and molecular biology.
[24] B. Pompe,et al. Momentary information transfer as a coupling measure of time series. , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.
[25] Albert Y. Zomaya,et al. Information modification and particle collisions in distributed computation. , 2010, Chaos.
[26] Anthony Randal McIntosh,et al. Empirical and Theoretical Aspects of Generation and Transfer of Information in a Neuromagnetic Source Network , 2011, Front. Syst. Neurosci..
[27] Wolf Singer,et al. Quantifying additive evoked contributions to the event-related potential , 2012, NeuroImage.
[28] G. Barnes,et al. Assessing interactions of linear and nonlinear neuronal sources using MEG beamformers: a proof of concept , 2005, Clinical Neurophysiology.
[29] Bernhard Schölkopf,et al. Causal relationships between frequency bands of extracellular signals in visual cortex revealed by an information theoretic analysis , 2010, Journal of Computational Neuroscience.
[30] H. Kantz,et al. Nonlinear time series analysis , 1997 .
[31] Joseph T. Lizier,et al. JIDT: An Information-Theoretic Toolkit for Studying the Dynamics of Complex Systems , 2014, Front. Robot. AI.
[32] Vasily A. Vakorin,et al. Confounding effects of indirect connections on causality estimation , 2009, Journal of Neuroscience Methods.
[33] Kenneth J. Smith,et al. Conduction in Segmentally Demyelinated Mammalian Central Axons , 1997, The Journal of Neuroscience.
[34] M Palus,et al. Synchronization as adjustment of information rates: detection from bivariate time series. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.
[35] J. Pearl. Causality: Models, Reasoning and Inference , 2000 .
[36] Viola Priesemann,et al. TRENTOOL: A Matlab open source toolbox to analyse information flow in time series data with transfer entropy , 2011, BMC Neuroscience.
[37] Ingo Fischer,et al. Synchronization in simple network motifs with negligible correlation and mutual information measures. , 2012, Physical review letters.
[38] A. Kraskov,et al. Erratum: Estimating mutual information [Phys. Rev. E 69, 066138 (2004)] , 2011 .
[39] Joseph T. Lizier,et al. Multivariate construction of effective computational networks from observational data , 2012 .
[40] A. Kraskov,et al. Estimating mutual information. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.
[41] Albert Y. Zomaya,et al. Local information transfer as a spatiotemporal filter for complex systems. , 2008, Physical review. E, Statistical, nonlinear, and soft matter physics.
[42] Gordon Pipa,et al. Transfer entropy—a model-free measure of effective connectivity for the neurosciences , 2010, Journal of Computational Neuroscience.
[43] Herbert Witte,et al. Development of interaction measures based on adaptive non-linear time series analysis of biomedical signals / Entwicklung von Interaktionsmaßen auf der Grundlage adaptiver, nichtlinearer Zeitreihenanalyse von biomedizinischen Signalen , 2006, Biomedizinische Technik. Biomedical engineering.
[44] L. Horváth,et al. Limit Theorems in Change-Point Analysis , 1997 .
[45] A. N. Sharkovskiĭ. Dynamic systems and turbulence , 1989 .
[46] Michael Breakspear,et al. An improved algorithm for the detection of dynamical interdependence in bivariate time-series , 2003, Biological Cybernetics.
[47] Gustavo Deco,et al. Optimal Information Transfer in the Cortex through Synchronization , 2010, PLoS Comput. Biol..
[48] W. Singer,et al. Testing non-linearity and directedness of interactions between neural groups in the macaque inferotemporal cortex , 1999, Journal of Neuroscience Methods.
[49] L. Faes,et al. Information-based detection of nonlinear Granger causality in multivariate processes via a nonuniform embedding technique. , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.
[50] Mario Ragwitz,et al. Markov models from data by simple nonlinear time series predictors in delay embedding spaces. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.
[51] J. Victor. Binless strategies for estimation of information from neural data. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.
[52] Natasa Kovacevic,et al. Exploring transient transfer entropy based on a group-wise ICA decomposition of EEG data , 2010, NeuroImage.
[53] J. Martinerie,et al. Statistical assessment of nonlinear causality: application to epileptic EEG signals , 2003, Journal of Neuroscience Methods.
[54] E M Glaser,et al. Autapses in neocortex cerebri: synapses between a pyramidal cell's axon and its own dendrites. , 1972, Brain research.
[55] William W. Lytton,et al. Synaptic information transfer in computer models of neocortical columns , 2011, Journal of Computational Neuroscience.
[56] D G Pelli,et al. The VideoToolbox software for visual psychophysics: transforming numbers into movies. , 1997, Spatial vision.
[57] John M. Beggs,et al. Extending Transfer Entropy Improves Identification of Effective Connectivity in a Spiking Cortical Network Model , 2011, PloS one.
[58] D H Brainard,et al. The Psychophysics Toolbox. , 1997, Spatial vision.
[59] Schreiber,et al. Measuring information transfer , 2000, Physical review letters.
[60] Nikos K Logothetis,et al. Testing methodologies for the nonlinear analysis of causal relationships in neurovascular coupling. , 2010, Magnetic resonance imaging.
[61] W. Singer,et al. Impaired Gamma-Band Activity during Perceptual Organization in Adults with Autism Spectrum Disorders: Evidence for Dysfunctional Network Activity in Frontal-Posterior Cortices , 2012, The Journal of Neuroscience.
[62] A. Seth,et al. Granger causality and transfer entropy are equivalent for Gaussian variables. , 2009, Physical review letters.
[63] Voss,et al. Anticipating chaotic synchronization , 2000, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.
[64] Thomas E. Nichols,et al. Thresholding of Statistical Maps in Functional Neuroimaging Using the False Discovery Rate , 2002, NeuroImage.
[65] Michèle Basseville,et al. Detection of abrupt changes: theory and application , 1993 .