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file = {/Users/edeno/Dropbox (Personal)/Papers/Journal of Neuroscience Methods-/2015/Journal of Neuroscience Methods-2015-Aoi et al-Rate-adjusted spike–LFP coherence comparisons from spike-train statistics.pdf}
file = {/Users/edeno/Dropbox (Personal)/Papers/Digital Signal Processing, 2007 15th International Conference on-/2007/Digital Signal Processing, 2007 15th International Conference on-2007-Baccalá et al-Generalized partial directed coherence.pdf}
file = {/Users/edeno/Dropbox (Personal)/Papers/Proceedings of the National Academy of Sciences-/2011/Proceedings of the National Academy of Sciences-2011-Cimenser et al-Tracking brain states under general anesthesia by using global coherence.pdf}
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@article{CliffUnifyingPairwiseInteractions2022,
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title = {Unifying {{Pairwise Interactions}} in {{Complex Dynamics}}},
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author = {Cliff, Oliver M and Lizier, Joseph T and Tsuchiya, Naotsugu and Fulcher, Ben D},
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doi = {10.48550/ARXIV.2201.11941},
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url = {https://arxiv.org/abs/2201.11941},
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author = {Cliff, Oliver M. and Lizier, Joseph T. and Tsuchiya, Naotsugu and Fulcher, Ben D.},
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title = {Unifying Pairwise Interactions in Complex Dynamics},
file = {/Users/edeno/Dropbox (Personal)/Papers/NeuroImage-/2008/NeuroImage-2008-Dhamala et al-Analyzing information flow in brain networks with nonparametric Granger.pdf}
file = {/Users/edeno/Dropbox (Personal)/Papers/Journal of the American statistical association-/1982/Journal of the American Statistical Association-1982-Geweke-Measurement of Linear Dependence and Feedback Between Multiple Time Series.pdf}
file = {/Users/edeno/Dropbox (Personal)/Papers/Electroencephalography and Clinical Neurophysiology-/1983/Electroencephalography and clinical neurophysiology-1983-Gotman-Measurement of small time differences between EEG channels_2.pdf}
file = {/Users/edeno/Dropbox (Personal)/Papers/Frontiers in Neuroscience-/2013/Frontiers in Neuroscience-2013-Gramfort-MEG and EEG data analysis with MNE-Python.pdf}
file = {/Users/edeno/Dropbox (Personal)/Papers/Biological Cybernetics-/1991/Biological cybernetics-1991-Kaminski_Blinowska-A new method of the description of the information flow in the brain structures.pdf}
file = {/Users/edeno/Dropbox (Personal)/Papers/Journal of Neuroscience Methods-/2003/Journal of Neuroscience Methods-2003-Korzeniewska et al-Determination of information flow direction among brain structures by a.pdf}
file = {/Users/edeno/Dropbox (Personal)/Papers/Physical Review Letters-/2008/Physical Review Letters-2008-Nolte et al-Robustly Estimating the Flow Direction of Information in Complex Physical.pdf}
file = {/Users/edeno/Dropbox (Personal)/Papers/NeuroImage-/2011/NeuroImage-2011-Vinck et al-An improved index of phase-synchronization for electrophysiological data in the.pdf}
title = {Python for the Practicing Neuroscientist: An Online Educational Resource},
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shorttitle = {Python for the Practicing Neuroscientist},
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author = {Schlafly, Emily and Cheung, Anthea and Michalka, Samantha W and Lipton, Paul A and Kochlacs, Caroline Moore and Bohland, Jason and Eden, Uri T and Kramer, Mark},
abstract = {As neuronal data accumulates worldwide, accessible - yet rigorous - resources to develop hands-on experience with modern data analysis techniques are required. We present here an online educational resource for neural data analysis (https://markkramer.github.io/Case-Studies-Python). To reach the biologists, psychologists, and clinicians collecting neuronal data, we assume only a basic mathematics background, common to those trained in biological sciences. Through an interdisciplinary case-study approach, we use real-world data to motivate the study of modern quantitative analysis methods in Python. A modular format provides multiple coherent learning paths through the material, and thereby allows personalized learning for individuals with varying quantitative backgrounds and research interests, and flexible curation of material for redeployment in other curricula. Developed using Jupyter notebooks, the material supports fully interactive environments in most web browsers, and hosted on GitHub, the material is freely available for reuse, modification, and further development by the community.},
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langid = {english},
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file = {/Users/edeno/Zotero/storage/48VNSJCT/Schlafly et al. - 2020 - Python for the practicing neuroscientist an onlin.pdf}
author = {Emily Schlafly and Anthea Cheung and Samantha W Michalka and Paul A Lipton and Caroline Moore Kochlacs and Jason Bohland and Uri T Eden and Mark Kramer},
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title = {Python for the practicing~ neuroscientist: an online educational resource}
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