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Randomly coupled neural fields demonstrate chaotic variation of firing rates,if the coupling is strong enough, as has been shown by Sompolinsky et. al[Phys. Rev. Lett., v. 61, 259 (1988)]. We present a method for reconstructionof the coupling matrix from the observations of the chaotic firing rates. Theapproach is based on the particular property of the nonlinearity in thecoupling, as the latter is determined by a sigmoidal gain function. Wedemonstrate that for a large enough data set, the method gives an accurateestimation of the coupling matrix and of other parameters of the system,including the gain function.

Reconstruction of chaotic neural network from observed firing rates