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

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.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0027-D176-2 Version Permalink: http://hdl.handle.net/11858/00-001M-0000-0027-D177-F
Genre: Journal Article

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 Creators:
Schumacher, J.1, 2, Author              
Wunderle, T.3, Author              
Fries, P.3, Author              
Jakel, F.4, Author
Pipa, G.4, Author
Affiliations:
1Department Biogeochemical Processes, Prof. S. E. Trumbore, Max Planck Institute for Biogeochemistry, Max Planck Society, ou_1497752              
2Department Biogeochemical Processes, Prof. E.-D. Schulze, Max Planck Institute for Biogeochemistry, Max Planck Society, ou_1497751              
3Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Max Planck Society, ou_2074314              
4external, ou_persistent22              

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 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.

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 Dates: 2015-06-142015
 Publication Status: Published in print
 Pages: -
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 Table of Contents: -
 Rev. Method: -
 Identifiers: URI: http://www.ncbi.nlm.nih.gov/pubmed/26079751
Other: 762
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Title: Neural computation
Source Genre: Journal
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Publ. Info: Cambridge, Mass. : MIT Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: - Identifier: ISSN: 0899-7667
CoNE: /journals/resource/954925561591