Reconstruction of a neural network from a time series of firing rates
- Randomly coupled neural fields demonstrate irregular variation of firing rates, if the coupling is strong enough, as has been shown by Sompolinsky et al. [Phys. Rev. Lett. 61, 259 (1988)]. We present a method for reconstruction of the coupling matrix from a time series of irregular firing rates. The approach is based on the particular property of the nonlinearity in the coupling, as the latter is determined by a sigmoidal gain function. We demonstrate that for a large enough data set and a small measurement noise, the method gives an accurate estimation of the coupling matrix and of other parameters of the system, including the gain function.
| Verfasserangaben: | Arkadij PikovskijORCiDGND |
|---|---|
| DOI: | https://doi.org/10.1103/PhysRevE.93.062313 |
| ISSN: | 2470-0045 |
| ISSN: | 2470-0053 |
| Pubmed ID: | https://pubmed.ncbi.nlm.nih.gov/27415286 |
| Titel des übergeordneten Werks (Englisch): | Physical review : E, Statistical, nonlinear and soft matter physics |
| Verlag: | American Physical Society |
| Verlagsort: | College Park |
| Publikationstyp: | Wissenschaftlicher Artikel |
| Sprache: | Englisch |
| Jahr der Erstveröffentlichung: | 2016 |
| Erscheinungsjahr: | 2016 |
| Datum der Freischaltung: | 22.03.2020 |
| Band: | 93 |
| Seitenanzahl: | 4 |
| Fördernde Institution: | ITN COSMOS (European Unions Horizon research and innovation programme under the Marie Sklodowska-Curie Grant) [642563]; Russian Science Foundation [14-12-00811] |
| Organisationseinheiten: | Mathematisch-Naturwissenschaftliche Fakultät / Institut für Physik und Astronomie |
| Peer Review: | Referiert |
