A Statistical Framework to Infer Delay and Direction of Information Flow from Measurements of Complex Systems

In neuroscience, data are typically generated from neural network activity. The resulting time series represent measurements from spatially distributed subsystems with complex interactions, weakly coupled to a high-dimensional global system. We present a statistical framework to estimate the directi...

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Veröffentlicht in:	Neural computation 2015-08, Vol.27 (8), p.1555-1608
Hauptverfasser:	Schumacher, Johannes, Wunderle, Thomas, Fries, Pascal, Jäkel, Frank, Pipa, Gordon
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Animals Brain Waves - physiology Cats Complex systems Delay Estimates Inference Information flow Information Theory Joining Measurement Models, Neurological Neural networks Neurons - physiology Neurosciences Nonlinear Dynamics Normal distribution Oscillometry Reconstruction Synchronism Time Factors Time series Visual Cortex - cytology
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