English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Journal Article

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

MPS-Authors
/persons/resource/persons62551

Schumacher,  J.
Department Biogeochemical Processes, Prof. S. E. Trumbore, Max Planck Institute for Biogeochemistry, Max Planck Society;
Department Biogeochemical Processes, Prof. E.-D. Schulze, Max Planck Institute for Biogeochemistry, Max Planck Society;

/persons/resource/persons141641

Wunderle,  T.
Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Max Planck Society;

/persons/resource/persons141609

Fries,  P.
Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Max Planck Society;

Locator
There are no locators available
Fulltext (public)
There are no public fulltexts available
Supplementary Material (public)
There is no public supplementary material available
Citation

Schumacher, J., Wunderle, T., Fries, P., Jakel, F., & Pipa, G. (2015). A Statistical Framework to Infer Delay and Direction of Information Flow from Measurements of Complex Systems. Neural computation. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/26079751.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0027-D176-2
Abstract
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 direction of information flow and its delay in measurements from systems of this type. Informed by differential topology, gaussian process regression is employed to reconstruct measurements of putative driving systems from measurements of the driven systems. These reconstructions serve to estimate the delay of the interaction by means of an analytical criterion developed for this purpose. The model accounts for a range of possible sources of uncertainty, including temporally evolving intrinsic noise, while assuming complex nonlinear dependencies. Furthermore, we show that if information flow is delayed, this approach also allows for inference in strong coupling scenarios of systems exhibiting synchronization phenomena. The validity of the method is demonstrated with a variety of delay-coupled chaotic oscillators. In addition, we show that these results seamlessly transfer to local field potentials in cat visual cortex.