Transfer Entropy in Neuroscience
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[1] S. Pethel,et al. Distinguishing anticipation from causality: anticipatory bias in the estimation of information flow. , 2011, Physical review letters.
[2] Jakob Heinzle,et al. Multivariate information-theoretic measures reveal directed information structure and task relevant changes in fMRI connectivity , 2010, Journal of Computational Neuroscience.
[3] Mikhail Prokopenko,et al. Differentiating information transfer and causal effect , 2008, 0812.4373.
[4] J. Victor. Binless strategies for estimation of information from neural data. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.
[5] Boris Gourévitch,et al. Evaluating information transfer between auditory cortical neurons. , 2007, Journal of neurophysiology.
[6] A. Lapidoth,et al. On the entropy of the sum and of the difference of independent random variables , 2008, 2008 IEEE 25th Convention of Electrical and Electronics Engineers in Israel.
[7] Xiaoli Li,et al. Estimating coupling direction between neuronal populations with permutation conditional mutual information , 2010, NeuroImage.
[8] Gordon Pipa,et al. Assessing coupling dynamics from an ensemble of time series , 2010, Entropy.
[9] Natasa Kovacevic,et al. Exploring transient transfer entropy based on a group-wise ICA decomposition of EEG data , 2010, NeuroImage.
[10] Joseph T. Lizier,et al. Multivariate construction of effective computational networks from observational data , 2012 .
[11] A. Kraskov,et al. Estimating mutual information. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.
[12] Mikhail Prokopenko,et al. Guided Self-Organization: Inception , 2014 .
[13] 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.
[14] 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.
[15] Viola Priesemann,et al. TRENTOOL: A Matlab open source toolbox to analyse information flow in time series data with transfer entropy , 2011, BMC Neuroscience.
[16] Sungroh Yoon,et al. Entropy-Based Analysis and Bioinformatics-Inspired Integration of Global Economic Information Transfer , 2013, PloS one.
[17] Gordon Pipa,et al. Transfer entropy—a model-free measure of effective connectivity for the neurosciences , 2010, Journal of Computational Neuroscience.
[18] Shawn D. Pethel,et al. Transfer Entropy for Coupled Autoregressive Processes , 2013, Entropy.
[19] Theo Geisel,et al. Model-Free Reconstruction of Excitatory Neuronal Connectivity from Calcium Imaging Signals , 2012, PLoS Comput. Biol..
[20] G. Barnes,et al. Assessing interactions of linear and nonlinear neuronal sources using MEG beamformers: a proof of concept , 2005, Clinical Neurophysiology.
[21] 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.
[22] 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.
[23] 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.
[24] Matthäus Staniek,et al. Symbolic transfer entropy: inferring directionality in biosignals , 2009, Biomedizinische Technik. Biomedical engineering.
[25] Schreiber,et al. Measuring information transfer , 2000, Physical review letters.
[26] Daniel Polani,et al. Information Flows in Causal Networks , 2008, Adv. Complex Syst..
[27] Mikhail Prokopenko,et al. Information Dynamics in Small-World Boolean Networks , 2011, Artificial Life.
[28] Ulrich Meyer,et al. Revisiting Wiener's principle of causality — interaction-delay reconstruction using transfer entropy and multivariate analysis on delay-weighted graphs , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.
[29] Christoph Salge,et al. A Bivariate Measure of Redundant Information , 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.
[30] Albert Y. Zomaya,et al. The local information dynamics of distributed computation in complex systems , 2012 .
[31] Anthony Randal McIntosh,et al. Empirical and Theoretical Aspects of Generation and Transfer of Information in a Neuromagnetic Source Network , 2011, Front. Syst. Neurosci..
[32] Kenneth J. Smith,et al. Conduction in Segmentally Demyelinated Mammalian Central Axons , 1997, The Journal of Neuroscience.
[33] H. Marko,et al. The Bidirectional Communication Theory - A Generalization of Information Theory , 1973, IEEE Transactions on Communications.
[34] Jochen Kaiser,et al. Transfer entropy in magnetoencephalographic data: quantifying information flow in cortical and cerebellar networks. , 2011, Progress in biophysics and molecular biology.
[35] B. Pompe,et al. Momentary information transfer as a coupling measure of time series. , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.
[36] Albert Y. Zomaya,et al. Information modification and particle collisions in distributed computation. , 2010, Chaos.
[37] William W. Lytton,et al. Synaptic information transfer in computer models of neocortical columns , 2011, Journal of Computational Neuroscience.
[38] 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.
[39] Nikos K Logothetis,et al. Testing methodologies for the nonlinear analysis of causal relationships in neurovascular coupling. , 2010, Magnetic resonance imaging.
[40] F. Takens. Detecting strange attractors in turbulence , 1981 .
[41] Luca Faes,et al. Compensated Transfer Entropy as a Tool for Reliably Estimating Information Transfer in Physiological Time Series , 2013, Entropy.
[42] F. Attneave,et al. The Organization of Behavior: A Neuropsychological Theory , 1949 .
[43] 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.
[44] 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.
[45] E. Beckenbach,et al. Modern mathematics for the engineer , 1958 .
[46] Sonja Grün,et al. How local is the local field potential? , 2011, BMC Neuroscience.
[47] Parlitz,et al. Fast nearest-neighbor searching for nonlinear signal processing , 2000, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.
[48] Annette Witt,et al. Dynamic Effective Connectivity of Inter-Areal Brain Circuits , 2011, PLoS Comput. Biol..
[49] Chi K. Tse,et al. Optimal embedding parameters: a modelling paradigm , 2004 .
[50] John M. Beggs,et al. Extending Transfer Entropy Improves Identification of Effective Connectivity in a Spiking Cortical Network Model , 2011, PloS one.
[51] Okyu Kwon,et al. Information flow between stock indices , 2008, 0802.1747.
[52] Thomas M. Cover,et al. Elements of Information Theory , 2005 .
[53] Vasily A. Vakorin,et al. Confounding effects of indirect connections on causality estimation , 2009, Journal of Neuroscience Methods.
[54] F. Atay,et al. Information storage, loop motifs, and clustered structure in complex networks. , 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.
[55] J. Martinerie,et al. Statistical assessment of nonlinear causality: application to epileptic EEG signals , 2003, Journal of Neuroscience Methods.
[56] Luca Faes,et al. Non-uniform multivariate embedding to assess the information transfer in cardiovascular and cardiorespiratory variability series , 2012, Comput. Biol. Medicine.
[57] K. Müller,et al. Robustly estimating the flow direction of information in complex physical systems. , 2007, Physical review letters.
[58] K. Tsakalis,et al. Information Flow and Application to Epileptogenic Focus Localization From Intracranial EEG , 2009, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[59] J. Pearl. Causality: Models, Reasoning and Inference , 2000 .
[60] Gustavo Deco,et al. Optimal Information Transfer in the Cortex through Synchronization , 2010, PLoS Comput. Biol..
[61] T. Schreiber,et al. Information transfer in continuous processes , 2002 .
[62] J. Massey. CAUSALITY, FEEDBACK AND DIRECTED INFORMATION , 1990 .
[63] 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.
[64] 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.
[65] Olivier J. J. Michel,et al. The relation between Granger causality and directed information theory: a review , 2012, Entropy.
[66] J. Ford,et al. Schizophrenia, myelination, and delayed corollary discharges: a hypothesis. , 2012, Schizophrenia bulletin.
[67] David Marr,et al. VISION A Computational Investigation into the Human Representation and Processing of Visual Information , 2009 .
[68] Joseph T. Lizier,et al. Moving Frames of Reference, Relativity and Invariance in Transfer Entropy and Information Dynamics , 2013, Entropy.
[69] A. Ledberg,et al. When two become one: the limits of causality analysis of brain dynamics. , 2012, PloS one.
[70] Christof Koch,et al. Quantifying synergistic mutual information , 2012, ArXiv.
[71] Randall D. Beer,et al. Generalized Measures of Information Transfer , 2011, ArXiv.
[72] Olivier J. J. Michel,et al. On directed information theory and Granger causality graphs , 2010, Journal of Computational Neuroscience.
[73] W. Singer,et al. Oscillatory responses in cat visual cortex exhibit inter-columnar synchronization which reflects global stimulus properties , 1989, Nature.
[74] A. Seth,et al. Granger causality and transfer entropy are equivalent for Gaussian variables. , 2009, Physical review letters.
[75] Joseph T. Lizier,et al. Towards a synergy-based approach to measuring information modification , 2013, 2013 IEEE Symposium on Artificial Life (ALife).
[76] Viola Priesemann,et al. Measuring Information-Transfer Delays , 2013, PloS one.
[77] Robert Oostenveld,et al. FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data , 2010, Comput. Intell. Neurosci..
[78] Randall D. Beer,et al. Nonnegative Decomposition of Multivariate Information , 2010, ArXiv.
[79] W. Singer,et al. Testing non-linearity and directedness of interactions between neural groups in the macaque inferotemporal cortex , 1999, Journal of Neuroscience Methods.
[80] Francisco Javier Díaz Pernas,et al. Efficient Transfer Entropy Analysis of Non-Stationary Neural Time Series , 2014, PloS one.