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Reconstruction of coupling architecture of neural field networks from vector time series
- We propose a method of reconstruction of the network coupling matrix for a basic voltage-model of the neural field dynamics. Assuming that the multivariate time series of observations from all nodes are available, we describe a technique to find coupling constants which is unbiased in the limit of long observations. Furthermore, the method is generalized for reconstruction of networks with time-delayed coupling, including the reconstruction of unknown time delays. The approach is compared with other recently proposed techniques.
Author details: | Ilya V. Sysoev, Vladimir I. Ponomarenko, Arkadij PikovskijORCiDGND |
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DOI: | https://doi.org/10.1016/j.cnsns.2017.10.006 |
ISSN: | 1007-5704 |
ISSN: | 1878-7274 |
Title of parent work (English): | Communications in nonlinear science & numerical simulation |
Publisher: | Elsevier |
Place of publishing: | Amsterdam |
Publication type: | Article |
Language: | English |
Date of first publication: | 2017/10/13 |
Publication year: | 2017 |
Release date: | 2022/01/03 |
Tag: | Network reconstruction; Neurooscillators; Time delay; Time series |
Volume: | 57 |
Number of pages: | 10 |
First page: | 342 |
Last Page: | 351 |
Funding institution: | Russian Foundation for Basic ResearchRussian Foundation for Basic Research (RFBR) [16-02-00091]; Russian FederationRussian Federation [CPi-1510.2015.4]; Russian Science FoundationRussian Science Foundation (RSF) [17-12-01534] |
Organizational units: | Mathematisch-Naturwissenschaftliche Fakultät / Institut für Physik und Astronomie |
DDC classification: | 5 Naturwissenschaften und Mathematik / 53 Physik / 530 Physik |
Peer review: | Referiert |