TY  - JOUR
A1  - Pikovskij, Arkadij
T1  - Reconstruction of a neural network from a time series of firing rates
T2  - Physical review : E, Statistical, nonlinear and soft matter physics
N2  - 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.
Y1  - 2016
UR  - https://publishup.uni-potsdam.de/frontdoor/index/index/docId/45225
SN  - 2470-0045
SN  - 2470-0053
VL  - 93
PB  - American Physical Society
CY  - College Park
ER  -