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

Schumacher, J. and Wunderle, T. and Fries, P. and Jäkel, F. and Pipa, G. :
A Statistical Framework to Infer Delay and Direction of Information Flow from Measurements of Complex Systems.
[Online-Edition: https://doi.org/10.1162/NECO_a_00756]
In: Neural Computation, 27 pp. 1555-1608.
[Article] , (2015)

Official URL: https://doi.org/10.1162/NECO_a_00756

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.

Item Type: Article
Erschienen: 2015
Creators: Schumacher, J. and Wunderle, T. and Fries, P. and Jäkel, F. and Pipa, G.
Title: A Statistical Framework to Infer Delay and Direction of Information Flow from Measurements of Complex Systems
Language: English
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.

Journal or Publication Title: Neural Computation
Volume: 27
Divisions: 03 Department Human Sciences
03 Department Human Sciences > Institute for Psychology
03 Department Human Sciences > Institute for Psychology > Models of Higher Cognition
Date Deposited: 09 Jul 2018 09:25
DOI: 10.1162/NECOa₀₀₇₅₆
Official URL: https://doi.org/10.1162/NECO_a_00756
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