@article{SysoevPonomarenkoPikovskij2017,
  author    = {Sysoev, Ilya V. and Ponomarenko, Vladimir I. and Pikovskij, Arkadij},
  title     = {Reconstruction of coupling architecture of neural field networks from vector time series},
  series = {Communications in nonlinear science \& numerical simulation},
  volume    = {57},
  journal   = {Communications in nonlinear science \& numerical simulation},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {1007-5704},
  doi       = {10.1016/j.cnsns.2017.10.006},
  pages     = {342 -- 351},
  year      = {2017},
  abstract  = {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.},
  language  = {en}
}