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 |
|---|---|
| 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 |
