Reconstruction of chaotic neural network from observed firing rates

TL;DR

This paper introduces a method to reconstruct the coupling matrix of chaotic neural networks from observed firing rates, leveraging the nonlinear properties of the sigmoidal gain function to accurately estimate system parameters.

Contribution

It presents a novel reconstruction technique for neural coupling matrices using observed firing rates and nonlinear system properties, improving parameter estimation accuracy.

Findings

Accurate reconstruction of coupling matrices with sufficient data.

Effective estimation of system parameters including gain function.

Method applicable to large chaotic neural datasets.

Abstract

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 reconstruction of the coupling matrix from the observations of the chaotic 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, the method gives an accurate estimation of the coupling matrix and of other parameters of the system, including the gain function.