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

TL;DR

This paper introduces a method to reconstruct the parameters of a neural field network from observed chaotic voltage time series, enabling insights into the system's nonlinear interactions.

Contribution

A novel technique for inferring coupling parameters, nonlinear gain functions, and time constants of neural networks solely from voltage observations.

Findings

Successfully reconstructs network parameters from chaotic voltage data

Applicable to neural systems with known structure but unknown dynamics

Enhances understanding of neural interactions through data-driven inference

Abstract

We present a general method for reconstruction of a network of nonlinearly coupled neural fields from the observations. A prominent example of such a system is a dynamical random neural network model studied by Sompolinsky et. al [Phys. Rev. Lett., v. 61, 259 (1988)]. 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.