Reconstruction of a scalar voltage-based neural field network from observed time series

Abstract

We present a general method for the reconstruction of a network of nonlinearly coupled neural fields from observations. A prominent example of such a system is a dynamical random neural network model studied by Sompolinsky et al. (Phys. Rev. Lett., 61 (1988) 259). We develop a technique for inferring the properties of the system from the observations of the chaotic voltages. Only the structure of the model is assumed to be known, while the nonlinear gain functions of the interactions, the matrix of the coupling constants, and the time constants of the local dynamics are reconstructed from the time series.