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

Autor(en): Schumacher, Johannes
Wunderle, Thomas
Fries, Pascal
Jaekel, Frank
Pipa, Gordon 
Stichwörter: Computer Science; Computer Science, Artificial Intelligence; EMBEDDINGS; FORCED SYSTEMS; KERNEL; Neurosciences; Neurosciences & Neurology; RECONSTRUCTION; SYNCHRONIZATION
Erscheinungsdatum: 2015
Herausgeber: MIT PRESS
Journal: NEURAL COMPUTATION
Volumen: 27
Ausgabe: 8
Startseite: 1555
Seitenende: 1608
Zusammenfassung: 
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.
ISSN: 08997667
DOI: 10.1162/NECO_a_00756

Show full item record

Page view(s)

1
Last Week
0
Last month
1
checked on Feb 21, 2024

Google ScholarTM

Check

Altmetric